From ed9d53cb44ba91e83a9ce2579ade41b50314dab7 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sat, 23 May 2026 13:31:10 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: llmware/dragon-falcon-7b-v0 Source: Original Platform --- .gitattributes | 35 + README.md | 105 + config.json | 33 + configuration_falcon.py | 152 + dragon-falcon-7b-answer_sheet_1113_1.jsonl | 200 + dragon-falcon-7b-answer_sheet_1113_2.jsonl | 200 + dragon-falcon-7b-core_test_1113_1.jsonl | 200 + dragon-falcon-7b-core_test_1113_2.jsonl | 200 + generation_config.json | 6 + generation_test_hf_script.py | 89 + generation_test_llmware_script.py | 70 + modeling_falcon.py | 1262 + pytorch_model.bin | 3 + special_tokens_map.json | 16 + tokenizer.json | 129970 ++++++++++++++++++ tokenizer_config.json | 12 + 16 files changed, 132553 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 configuration_falcon.py create mode 100644 dragon-falcon-7b-answer_sheet_1113_1.jsonl create mode 100644 dragon-falcon-7b-answer_sheet_1113_2.jsonl create mode 100644 dragon-falcon-7b-core_test_1113_1.jsonl create mode 100644 dragon-falcon-7b-core_test_1113_2.jsonl create mode 100644 generation_config.json create mode 100644 generation_test_hf_script.py create mode 100644 generation_test_llmware_script.py create mode 100644 modeling_falcon.py create mode 100644 pytorch_model.bin create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..4229ff3 --- /dev/null +++ b/README.md @@ -0,0 +1,105 @@ +--- +license: apache-2.0 +--- + +# Model Card for Model ID + + + +dragon-falcon-7b-v0 part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Falcon-7B base model. + +DRAGON models have been fine-tuned with the specific objective of fact-based question-answering over complex business and legal documents with an emphasis on reducing hallucinations and providing short, clear answers for workflow automation. + +### Benchmark Tests + +Evaluated against the benchmark test: [RAG-Instruct-Benchmark-Tester](https://www.huggingface.co/datasets/llmware/rag_instruct_benchmark_tester) +Average of 2 Test Runs with 1 point for correct answer, 0.5 point for partial correct or blank / NF, 0.0 points for incorrect, and -1 points for hallucinations. + +--**Accuracy Score**: **94** correct out of 100 +--Not Found Classification: 75.0% +--Boolean: 81.25% +--Math/Logic: 66.75% +--Complex Questions (1-5): 3 (Medium) +--Summarization Quality (1-5): 3 (Coherent, extractive) +--Hallucinations: No hallucinations observed in test runs. + +For test run results (and good indicator of target use cases), please see the files ("core_rag_test" and "answer_sheet" in this repo). + +### Model Description + + + +- **Developed by:** llmware +- **Model type:** Falcon +- **Language(s) (NLP):** English +- **License:** Apache 2.0 +- **Finetuned from model:** Falcon-7B-Base + +### Direct Use + + + +DRAGON is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services, +legal and regulatory industries with complex information sources. + +DRAGON models have been trained for common RAG scenarios, specifically: question-answering, key-value extraction, and basic summarization as the core instruction types +without the need for a lot of complex instruction verbiage - provide a text passage context, ask questions, and get clear fact-based responses. + + +## Bias, Risks, and Limitations + + + +Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms. + + +## How to Get Started with the Model + +The fastest way to get started with dRAGon is through direct import in transformers: + + from transformers import AutoTokenizer, AutoModelForCausalLM + tokenizer = AutoTokenizer.from_pretrained("dragon-falcon-7b-v0") + model = AutoModelForCausalLM.from_pretrained("dragon-falcon-7b-v0") + +Please refer to the generation_test .py files in the Files repository, which includes 200 samples and script to test the model. The **generation_test_llmware_script.py** includes built-in llmware capabilities for fact-checking, as well as easy integration with document parsing and actual retrieval to swap out the test set for RAG workflow consisting of business documents. + +The BLING model was fine-tuned with a simple "\ and \ wrapper", so to get the best results, wrap inference entries as: + + full_prompt = "\\: " + my_prompt + "\n" + "\\:" + +The BLING model was fine-tuned with closed-context samples, which assume generally that the prompt consists of two sub-parts: + +1. Text Passage Context, and +2. Specific question or instruction based on the text passage + +To get the best results, package "my_prompt" as follows: + + my_prompt = {{text_passage}} + "\n" + {{question/instruction}} + + +If you are using a HuggingFace generation script: + + # prepare prompt packaging used in fine-tuning process + new_prompt = ": " + entries["context"] + "\n" + entries["query"] + "\n" + ":" + + inputs = tokenizer(new_prompt, return_tensors="pt") + start_of_output = len(inputs.input_ids[0]) + + # temperature: set at 0.3 for consistency of output + # max_new_tokens: set at 100 - may prematurely stop a few of the summaries + + outputs = model.generate( + inputs.input_ids.to(device), + eos_token_id=tokenizer.eos_token_id, + pad_token_id=tokenizer.eos_token_id, + do_sample=True, + temperature=0.3, + max_new_tokens=100, + ) + + output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True) + + +## Model Card Contact + +Darren Oberst & llmware team diff --git a/config.json b/config.json new file mode 100644 index 0000000..84d8843 --- /dev/null +++ b/config.json @@ -0,0 +1,33 @@ +{ + "alibi": false, + "apply_residual_connection_post_layernorm": false, + "architectures": [ + "FalconForCausalLM" + ], + "attention_dropout": 0.0, + "auto_map": { + "AutoConfig": "configuration_falcon.FalconConfig", + "AutoModel": "modeling_falcon.FalconModel", + "AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification", + "AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification", + "AutoModelForQuestionAnswering": "modeling_falcon.FalconForQuestionAnswering", + "AutoModelForCausalLM": "modeling_falcon.FalconForCausalLM" + }, + "bias": false, + "bos_token_id": 11, + "eos_token_id": 11, + "hidden_dropout": 0.0, + "hidden_size": 4544, + "initializer_range": 0.02, + "layer_norm_epsilon": 1e-05, + "model_type": "falcon", + "multi_query": true, + "new_decoder_architecture": false, + "num_attention_heads": 71, + "num_hidden_layers": 32, + "parallel_attn": true, + "torch_dtype": "bfloat16", + "transformers_version": "4.27.4", + "use_cache": true, + "vocab_size": 65024 +} diff --git a/configuration_falcon.py b/configuration_falcon.py new file mode 100644 index 0000000..def8c2b --- /dev/null +++ b/configuration_falcon.py @@ -0,0 +1,152 @@ +# coding=utf-8 +# Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Falcon configuration""" +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + +FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json", + "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json", +} + + +class FalconConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the + [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 65024): + Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`FalconModel`] + hidden_size (`int`, *optional*, defaults to 4544): + Dimension of the hidden representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer decoder. + num_attention_heads (`int`, *optional*, defaults to 71): + Number of attention heads for each attention layer in the Transformer encoder. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + use_cache (`bool`, *optional*, defaults to `True`): + Whether the model should return the last key/values attentions (not used by all models). Only relevant if + `config.is_decoder=True`. + layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): + The epsilon used by the layer normalization layers. + hidden_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for MLP layers. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for attention layers. + num_kv_heads (`int`, *optional*): + Number of key-value heads to use per attention layer. If unset, defaults to the same value as + `num_attention_heads`. + alibi (`bool`, *optional*, defaults to `False`): + Whether to use ALiBi positional biases during self-attention. + new_decoder_architecture (`bool`, *optional*, defaults to `False`): + Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn` + arguments are ignored, as the new decoder always uses parallel attention. + multi_query (`bool`, *optional*, defaults to `True`): + Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`. + parallel_attn (`bool`, *optional*, defaults to `True`): + Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive + instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`. + bias (`bool`, *optional*, defaults to `False`): + Whether to use bias on Linear layers. + bos_token_id (`int`, *optional*, defaults to 11): + The id of the "beginning-of-sequence" token. + eos_token_id (`int`, *optional*, defaults to 11): + The id of the "end-of-sequence" token. + + Example: + + ```python + >>> from transformers import FalconModel, FalconConfig + + >>> # Initializing a small (2-layer) Falcon configuration + >>> configuration = FalconConfig(num_hidden_layers=2) + + >>> # Initializing a model from the small configuration + >>> model = FalconModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + model_type = "falcon" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=65024, + hidden_size=4544, + num_hidden_layers=32, + num_attention_heads=71, + layer_norm_epsilon=1e-5, + initializer_range=0.02, + use_cache=True, + hidden_dropout=0.0, + attention_dropout=0.0, + num_kv_heads=None, + alibi=False, + new_decoder_architecture=False, + multi_query=True, + parallel_attn=True, + bias=False, + bos_token_id=11, + eos_token_id=11, + **kwargs, + ): + logger.warning_once( + "\nWARNING: You are currently loading Falcon using legacy code contained in the model repository. Falcon has now been fully ported into the Hugging Face transformers library. " + "For the most up-to-date and high-performance version of the Falcon model code, please update to the latest version of transformers and then load the model " + "without the trust_remote_code=True argument.\n" + ) + self.vocab_size = vocab_size + # Backward compatibility with n_embed kwarg + n_embed = kwargs.pop("n_embed", None) + self.hidden_size = hidden_size if n_embed is None else n_embed + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.layer_norm_epsilon = layer_norm_epsilon + self.initializer_range = initializer_range + self.use_cache = use_cache + self.hidden_dropout = hidden_dropout + self.attention_dropout = attention_dropout + + self.bos_token_id = bos_token_id + self.eos_token_id = eos_token_id + self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads + self.alibi = alibi + self.new_decoder_architecture = new_decoder_architecture + self.multi_query = multi_query # Ignored when new_decoder_architecture is True + self.parallel_attn = parallel_attn + self.bias = bias + + super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) + + @property + def head_dim(self): + return self.hidden_size // self.num_attention_heads + + @property + def rotary(self): + return not self.alibi diff --git a/dragon-falcon-7b-answer_sheet_1113_1.jsonl b/dragon-falcon-7b-answer_sheet_1113_1.jsonl new file mode 100644 index 0000000..6a14289 --- /dev/null +++ b/dragon-falcon-7b-answer_sheet_1113_1.jsonl @@ -0,0 +1,200 @@ +{"number": 0, "llm_response": " $22,500.00", "gold_answer": "$22,500.00"} +{"number": 1, "llm_response": " Invoice # 0001", "gold_answer": "#0001"} +{"number": 2, "llm_response": " \u2022Front End Engineering Service - $5,000\r\n\u2022Back End Engineering Service - $7,500\r\n\u2022Quality Assurance Manager - $10,000", "gold_answer": "\u2022Front End Engineering Service;\n\u2022Back End Engineering Service;\n\u2022Quality Assurance Manager"} +{"number": 3, "llm_response": " Bia Hermes", "gold_answer": "Bia Hermes"} +{"number": 4, "llm_response": " #1000", "gold_answer": "#1000"} +{"number": 5, "llm_response": " Within 30 days", "gold_answer": "within 30 days of 01/01/2022"} +{"number": 6, "llm_response": " May 3, 2022", "gold_answer": "May 3, 2022"} +{"number": 7, "llm_response": " $4565.00", "gold_answer": "4150.00"} +{"number": 8, "llm_response": " $4565.00", "gold_answer": "4565.00"} +{"number": 9, "llm_response": " \u2022Boxes of Printer Paper, 30 of which are at $30 each, for a total of $900;\r\n\u202210 Printer Cartridges, at $75 each, for a total of $750;\r\n\u20225 Laser printers, at $500 each, for a total of $2500;\r\n\u202210 Printer Cartridges, at $75 each, for a total of $750;\r\n\u20225 Laser printers, at", "gold_answer": "\u202230 Boxes of Printer Paper;\n\u202210 Printer Cartridges;\n\u20225 Laser Printers."} +{"number": 10, "llm_response": " 234", "gold_answer": "234"} +{"number": 11, "llm_response": " Delta Inc. and ABC Company", "gold_answer": "ABC Company and Delta Inc."} +{"number": 12, "llm_response": " 30", "gold_answer": "30"} +{"number": 13, "llm_response": " Due on receipt on May 3, 2022", "gold_answer": "Due on Receipt"} +{"number": 14, "llm_response": " 8675 River Run Road, Marlington, CT 09392", "gold_answer": "8675 River Run Road, Marlington, CT 09392"} +{"number": 15, "llm_response": " March 3, 2022", "gold_answer": "March 3, 2022"} +{"number": 16, "llm_response": " January 20, 2021.", "gold_answer": "January 20, 2021"} +{"number": 17, "llm_response": " Donald Trump.", "gold_answer": "Donald Trump"} +{"number": 18, "llm_response": " Delaware", "gold_answer": "Delaware"} +{"number": 19, "llm_response": " Donald Trump", "gold_answer": "Barack Obama"} +{"number": 20, "llm_response": " \u2022Vivek Murthy,\r\n\u2022David Kessler, and\r\n\u2022Marcella Nunez-Smith.", "gold_answer": "1. former Surgeon General Vivek Murthy;\n2. former FDA commissioner David A. Kessler;\n3. Yale University's Marcella Nunez-Smith."} +{"number": 21, "llm_response": " Jon Ossoff and Raphael Warnock.", "gold_answer": "Jon Ossoff and Raphael Warnock"} +{"number": 22, "llm_response": " Two years.", "gold_answer": "Two-year term"} +{"number": 23, "llm_response": " 26,000", "gold_answer": "26,000"} +{"number": 24, "llm_response": " November 23, 2020", "gold_answer": "November 23"} +{"number": 25, "llm_response": " $52.9 billion.", "gold_answer": "$52.9 billion"} +{"number": 26, "llm_response": " 10% (up 15% in constant currency)", "gold_answer": "Increased 10%, up 15% in constant currency"} +{"number": 27, "llm_response": " March 31, 2023.", "gold_answer": "March 31, 2023"} +{"number": 28, "llm_response": " \u2022Focused execution by sales teams and partners;\r\n\u2022Microsoft Cloud revenue of $28.5 billion, up 22% (up 25% in constant currency).", "gold_answer": "Focused execution by our sales teams and partners"} +{"number": 29, "llm_response": " $28.5 billion", "gold_answer": "$28.5 billion"} +{"number": 30, "llm_response": " 8%", "gold_answer": "10%"} +{"number": 31, "llm_response": " 65.4 million.", "gold_answer": "65.4 million"} +{"number": 32, "llm_response": " $22.1 billion.", "gold_answer": "$22.1 billion"} +{"number": 33, "llm_response": " 1956", "gold_answer": "1956"} +{"number": 34, "llm_response": " The Church-Turing thesis, also known as the Church-Turing principle, is a theorem in theoretical computer science that states that any computable function can be simulated by a Turing machine.", "gold_answer": "A machine, by shuffling symbols as simple as '0' and '1' can simulate both mathematical deduction and formal reasoning."} +{"number": 35, "llm_response": " 1943", "gold_answer": "1943"} +{"number": 36, "llm_response": " 1956", "gold_answer": "1956"} +{"number": 37, "llm_response": " The 'AI winter' is a period when obtaining funding for AI projects is difficult.", "gold_answer": "A period when obtaining funding for AI projects is difficult."} +{"number": 38, "llm_response": " \"Machines will be capable, within twenty years, of doing any work a man can do.\"", "gold_answer": "He predicted that machines would be capable of doing any work a man can do within twenty years."} +{"number": 39, "llm_response": " 1987", "gold_answer": "1987."} +{"number": 40, "llm_response": " TestCo Software, Inc.", "gold_answer": "TestCo Software, Inc."} +{"number": 41, "llm_response": " Aphrodite Apollo and TestCo Software, Inc.", "gold_answer": "Aphrodite Apollo ('Executive') and TestCo Software, Inc. ('Company')"} +{"number": 42, "llm_response": " April 16, 2012", "gold_answer": "April 16, 2012."} +{"number": 43, "llm_response": " Customer", "gold_answer": "Customer"} +{"number": 44, "llm_response": " 30 days", "gold_answer": "30 days"} +{"number": 45, "llm_response": " 15 days after notice of the breach.", "gold_answer": "If the breach is not cured within 15 days of notice of the breach."} +{"number": 46, "llm_response": " Yes.", "gold_answer": "Yes, by either party with 30 days written notice."} +{"number": 47, "llm_response": " SVP of the Workforce Optimization Division", "gold_answer": "Senior Vice President, Event Planning ('SVP') of the Workforce Optimization Division."} +{"number": 48, "llm_response": " $200,000", "gold_answer": "$200,000"} +{"number": 49, "llm_response": " Section 2.2", "gold_answer": "Section 2.2"} +{"number": 50, "llm_response": " Annual review", "gold_answer": "Annually"} +{"number": 51, "llm_response": " Massachusetts", "gold_answer": "State of Massachusetts, except for Section 18(a)"} +{"number": 52, "llm_response": " Delaware", "gold_answer": "State of Delaware"} +{"number": 53, "llm_response": " Section 18(a)", "gold_answer": "Section 19"} +{"number": 54, "llm_response": " 195.12 points, or 0.76%.", "gold_answer": "195.12 points"} +{"number": 55, "llm_response": " 438,000", "gold_answer": "438,000 jobs"} +{"number": 56, "llm_response": " 273,000", "gold_answer": "273,000 jobs"} +{"number": 57, "llm_response": " 1.35%", "gold_answer": "Up 1.35%"} +{"number": 58, "llm_response": " Vibrant Industries Capital Advisors", "gold_answer": "Vibrant Industries Capital Advisors"} +{"number": 59, "llm_response": " 4.58%", "gold_answer": "4.58%"} +{"number": 60, "llm_response": " $350,000.", "gold_answer": "$350,000"} +{"number": 61, "llm_response": " $33.", "gold_answer": "$33"} +{"number": 62, "llm_response": " 61 years old.", "gold_answer": "61"} +{"number": 63, "llm_response": " One Red River Road, Stamford, Connecticut 08234", "gold_answer": "One Red River Road, Stamford, Connecticut 08234"} +{"number": 64, "llm_response": " Simmons Corp.", "gold_answer": "Simmons Corp"} +{"number": 65, "llm_response": " He is the Vice President of Sales.", "gold_answer": "Vice President of Sales"} +{"number": 66, "llm_response": " 101% from the previous quarter and 88% from the previous year.", "gold_answer": "Up 101%"} +{"number": 67, "llm_response": " $13.51 billion", "gold_answer": "$13.51 billion"} +{"number": 68, "llm_response": " 7.5 million shares", "gold_answer": "7.5 million shares"} +{"number": 69, "llm_response": " Jensen Huang", "gold_answer": "Jensen Huang"} +{"number": 70, "llm_response": " 141%", "gold_answer": "Up 141% from Q1"} +{"number": 71, "llm_response": " $2.48", "gold_answer": "$2.48"} +{"number": 72, "llm_response": " September 28, 2023", "gold_answer": "September 28, 2023"} +{"number": 73, "llm_response": " 429%", "gold_answer": "Up 148%"} +{"number": 74, "llm_response": " No, 72.5% < 70%. The expected gross margin is 72.5%.", "gold_answer": "Yes, between 71.5% and 72.%"} +{"number": 75, "llm_response": " $16.00 billion, plus or minus 2%.", "gold_answer": "$16.00 billion, plus or minus 2%."} +{"number": 76, "llm_response": " Grace Hopper Superchip", "gold_answer": "NVIDIA GH200 Grace Hopper Superchip"} +{"number": 77, "llm_response": " 14.5%", "gold_answer": "14.5%, plus or minor 1%"} +{"number": 78, "llm_response": " $2.95 billion", "gold_answer": "$2.00 billion"} +{"number": 79, "llm_response": " $2.95 billion", "gold_answer": "$2.95 billion"} +{"number": 80, "llm_response": " $2.49 billion", "gold_answer": "$2.49 billion"} +{"number": 81, "llm_response": " $379 million", "gold_answer": "$379 million"} +{"number": 82, "llm_response": " 28% increase in revenue from the previous quarter.", "gold_answer": "Up 28%"} +{"number": 83, "llm_response": " Pixar, Adobe, Apple and Autodesk.", "gold_answer": "Pixar, Adobe, Apple, Autodesk and NVIDIA"} +{"number": 84, "llm_response": " 35", "gold_answer": "35"} +{"number": 85, "llm_response": " $299", "gold_answer": "$299"} +{"number": 86, "llm_response": " MediaTek", "gold_answer": "MediaTek"} +{"number": 87, "llm_response": " Refinitiv", "gold_answer": "Refinitiv"} +{"number": 88, "llm_response": " Jerome Powell", "gold_answer": "Jerome Powell"} +{"number": 89, "llm_response": " Japan", "gold_answer": "Japan"} +{"number": 90, "llm_response": " 16.3%", "gold_answer": "Down 16.3%"} +{"number": 91, "llm_response": " 62.4 billion yen ($416.6 million)", "gold_answer": "62.4 billion yen ($416.6 million)"} +{"number": 92, "llm_response": " 42.5 billion yen.", "gold_answer": "Deficit of 42.5 billion yen."} +{"number": 93, "llm_response": " 4.3% year-on-year", "gold_answer": "Up 4.3%"} +{"number": 94, "llm_response": " 3,577", "gold_answer": "3,577"} +{"number": 95, "llm_response": " 3.2%", "gold_answer": "3.2%"} +{"number": 96, "llm_response": " Bank of America upgrades Target to buy from neutral.", "gold_answer": "Buy"} +{"number": 97, "llm_response": " Buy", "gold_answer": "Buy"} +{"number": 98, "llm_response": " $16 per share.", "gold_answer": "$16"} +{"number": 99, "llm_response": " $133 million.", "gold_answer": "133 million euros."} +{"number": 100, "llm_response": " Not Found.", "gold_answer": "Not Found"} +{"number": 101, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 102, "llm_response": " Not Found.", "gold_answer": "Not Found"} +{"number": 103, "llm_response": " Not Found.", "gold_answer": "Not Found"} +{"number": 104, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 105, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 106, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 107, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 108, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 109, "llm_response": " Not Found.", "gold_answer": "Not Found"} +{"number": 110, "llm_response": " Elon Musk", "gold_answer": "Not Found."} +{"number": 111, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 112, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 113, "llm_response": " 3.2%", "gold_answer": "Not Found"} +{"number": 114, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 115, "llm_response": " 100 laptops.", "gold_answer": "Not Found."} +{"number": 116, "llm_response": " Sonny Perdue", "gold_answer": "Not Found."} +{"number": 117, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 118, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 119, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 120, "llm_response": " Yes, the S&P 500 has increased 22% from 3,577 to 4,376.", "gold_answer": "Yes, by more than 22%."} +{"number": 121, "llm_response": " No, the company is citing \"sticky retail inflation\" for the fourth time in two years.", "gold_answer": "No, Chipotle is increasing prices."} +{"number": 122, "llm_response": " No.", "gold_answer": "No, it is lowering the target from $38 to $36."} +{"number": 123, "llm_response": " No, 4.98 billion euros < 5 billion euros.", "gold_answer": "No."} +{"number": 124, "llm_response": " Yes, the trade surplus was 62.4 billion yen, beating the 42.5 billion yen surplus that economists polled by Reuters were expecting.", "gold_answer": "Yes."} +{"number": 125, "llm_response": " No, Dow Jones futures fell 73 points.", "gold_answer": "No, down 73 points."} +{"number": 126, "llm_response": " No, 4.9% > 4.8%.", "gold_answer": "Yes, it topped 4.9%."} +{"number": 127, "llm_response": " No, the Nasdaq Composite was the underperformer, off 1.6%.", "gold_answer": "No, it was down 1.6%."} +{"number": 128, "llm_response": " No, it decreased by 15%.", "gold_answer": "No, it was down 15% from the previous quarter."} +{"number": 129, "llm_response": " No, $253 million is less than $300 million.", "gold_answer": "Yes, up 15%."} +{"number": 130, "llm_response": " No, $253 million < $250 million.", "gold_answer": "Yes"} +{"number": 131, "llm_response": " No, gaming revenue was up 11% from the previous quarter.", "gold_answer": "No, it was up 11% quarter-to-quarter and 22% year-to-year."} +{"number": 132, "llm_response": " Yes, 101% > 50%.", "gold_answer": "Yes, it increased 101% year-to-year."} +{"number": 133, "llm_response": " Yes, $3.38 billion.", "gold_answer": "Yes, $3.38 billion was returned"} +{"number": 134, "llm_response": " No, revenue was down -2% from the previous quarter.", "gold_answer": "No, it decreased by 28%"} +{"number": 135, "llm_response": " No, 3rd quarter net sales declined 20% year-on-year to 4.98 billion euros.", "gold_answer": "No, net sales declined 20% year-on-year, and the company has described the market environment as 'challenging.'"} +{"number": 136, "llm_response": " Yes, net sales declined 20% year-on-year to 4.98 billion euros.", "gold_answer": "Yes, net sales declined 20% year-on-year."} +{"number": 137, "llm_response": " No, exports to Asia have fallen for the ninth consecutive month.", "gold_answer": "No, exports to Asia decreased for the last nine months."} +{"number": 138, "llm_response": " No, exports in September decreased 16.3% compared to the same period last year.", "gold_answer": "Yes, by 4.3% year to year."} +{"number": 139, "llm_response": " No.", "gold_answer": "No, the base salary is $200,000."} +{"number": 140, "llm_response": " $253 + $100 = $353 million.", "gold_answer": "$353 million"} +{"number": 141, "llm_response": " $16 per share.", "gold_answer": "$16"} +{"number": 142, "llm_response": " $12 - $3 = $9.", "gold_answer": "$9"} +{"number": 143, "llm_response": " January 15, 2019.", "gold_answer": "January 15, 2019"} +{"number": 144, "llm_response": " 37 + 8 = 45.", "gold_answer": "47 years old."} +{"number": 145, "llm_response": " $450,000.", "gold_answer": "$450,000"} +{"number": 146, "llm_response": " January 15.", "gold_answer": "January 15"} +{"number": 147, "llm_response": " 2,000 shares.", "gold_answer": "2,000"} +{"number": 148, "llm_response": " $1200 + 50% = $1800.", "gold_answer": "$1800"} +{"number": 149, "llm_response": " 50% of the base salary amount.", "gold_answer": "$100,000"} +{"number": 150, "llm_response": " 1. Francois, 29 years old.\r\n2. Liam, 41 years old.\r\n3. Myra, 23 years old.\r\n4. Ashish, 27 years old.\r\n5. Liam, 41 years old.", "gold_answer": "1. Liam - 41;\n2. Francois - 29;\n3. Ashish - 27;\n4. Myra - 23."} +{"number": 151, "llm_response": " Elizabeth is 35 years old.", "gold_answer": "John"} +{"number": 152, "llm_response": " Chocolate.", "gold_answer": "Chocolate - $3.25"} +{"number": 153, "llm_response": " Chocolate.", "gold_answer": "Cheese - $6.25"} +{"number": 154, "llm_response": " No.", "gold_answer": "No, chocolate has a lower price than coffee."} +{"number": 155, "llm_response": " S&P 500 futures - 39 points.", "gold_answer": "Nasdaq 100 futures, up 63 points."} +{"number": 156, "llm_response": " S&P 500 futures.", "gold_answer": "S&P 500 futures futures, up 1.2%."} +{"number": 157, "llm_response": " Red Dog LLC - $61.", "gold_answer": "River Industries - $53."} +{"number": 158, "llm_response": " $19 + $2 = $21.", "gold_answer": "$21"} +{"number": 159, "llm_response": " PO 212 - $950,000", "gold_answer": "PO 1993"} +{"number": 160, "llm_response": " (A) $52.9 billion.", "gold_answer": "(D) $28.5 billion"} +{"number": 161, "llm_response": " (A) Microsoft.", "gold_answer": "(B) TestCo Software."} +{"number": 162, "llm_response": " (B) $7500.", "gold_answer": "(D) $10,000."} +{"number": 163, "llm_response": " (B) 7%.", "gold_answer": "(C) 14%."} +{"number": 164, "llm_response": " (A) 20%.", "gold_answer": "(B) 69%."} +{"number": 165, "llm_response": " \u20222019 - $53,823;\r\n\u20222020 - $31,536;\r\n\u20222021 - $31,536;\r\n\u20222022 - $81,462.", "gold_answer": "\u20222019 - 24,578;\n\u20222020 - 31,536;\n\u20222021 - 53,823;\n\u20222022 - 81,462."} +{"number": 166, "llm_response": " $297", "gold_answer": "297."} +{"number": 167, "llm_response": " $13,832", "gold_answer": "6,496."} +{"number": 168, "llm_response": " $31,536", "gold_answer": "18,402."} +{"number": 169, "llm_response": " $13,719", "gold_answer": "13,719"} +{"number": 170, "llm_response": " \u2022Herbert Simon,\r\n\u2022Marvin Minsky,\r\n\u2022James Lighthill,\r\n\u2022Sir James Lighthill,\r\n\u2022Papert,\r\n\u2022Minsky's and Papert's book Perceptrons,\r\n\u2022AI Winter,\r\n\u2022Expert Systems,\r\n\u2022Japan's fifth generation computer project,\r\n\u2022Lisp Machines, and\r\n\u2022Collapse of the Lisp Machines market in 1987.", "gold_answer": "\u2022Herbert Simon;\n\u2022Marvin Minsky;\n\u2022Sir James Lighthill;\n\u2022Papert."} +{"number": 171, "llm_response": " \u2022ServiceNow,\r\n\u2022Accenture,\r\n\u2022VMware,\r\n\u2022Snowflake,\r\n\u2022WPP,\r\n\u2022SoftBank,\r\n\u2022Hugging Face,\r\n\u2022NVIDIA AI Workbench,\r\n\u2022NVIDIA AI Enterprise 4.0, and\r\n\u2022NVIDIA's latest MLPerf training benchmarks with H100 GPUs.", "gold_answer": "\u2022Amazon Web Services;\n\u2022Microsoft Azure;\u2022\nRegional Cloud Service Providers;\n\u2022ServiceNow;\n\u2022Accenture;\n\u2022VMWare;\n\u2022Snowflake;\n\u2022WPP;\n\u2022Softbank;\n\u2022Hugging Face."} +{"number": 172, "llm_response": " \u2022American Airlines,\r\n\u2022Fifth Third,\r\n\u2022Truist Financial,\r\n\u2022KeyCorp,\r\n\u2022Truist Financial,\r\n\u2022CSX,\r\n\u2022United Parcel Service,\r\n\u2022LSEG, formerly known as Refinitiv, and\r\n\u2022Netflix.", "gold_answer": "\u2022Tesla;\n;\u2022Netflix;\n\u2022KeyCorp;\n\u2022Fifth Third;\n\u2022Truist Financial;\n\u2022American Airlines;\n\u2022Union Pacific;\n\u2022CSX."} +{"number": 173, "llm_response": " 1. The U.S. government, \r\n2. The British government, and \r\n3. The Japanese government.", "gold_answer": "1. U.S;\n2. UK;\n3. Japan."} +{"number": 174, "llm_response": " The intraday reversal occurred because of the softer wage number in the jobs report, which may have made investors rethink their earlier bearish stance.", "gold_answer": "Traders were unclear of the reason, but some noted it could be the softer wage numberin the jobs report."} +{"number": 175, "llm_response": " The company is reducing its cost base and increasing operation efficiency to \"address the challenging market environment.\"", "gold_answer": "Cost cutting plan"} +{"number": 176, "llm_response": " Tesla missed expectations on earnings and revenue in the third quarter.", "gold_answer": "The company missed expectations on earnings and revenue,and Elon Musk warned that the Cybertruck will not producepositive cash flow for more than a year."} +{"number": 177, "llm_response": " \u2022January 5, 2021, Democratic Party won control of the United States Senate.\r\n\u2022President-elect Biden had supported and campaigned for both candidates prior to the runoff elections.\r\n\u2022Thousands of Trump supporters violently stormed the Capitol in the hope of overturning Biden's election.", "gold_answer": "The electoral victories in Georgia by Jon Ossoff and Raphael Warnock."} +{"number": 178, "llm_response": " North Industries.", "gold_answer": "North Industries"} +{"number": 179, "llm_response": " Blue Moon Tech.", "gold_answer": "Blue Moon Tech."} +{"number": 180, "llm_response": " \u2022Revenue is expected to be $16.00 billion, plus or minus 2%.\r\n\u2022GAAP and non-GAAP gross margins are expected to be 71.5% and 72.5%, respectively, plus or minus 50 basis points.\r\n\u2022GAAP and non-GAAP operating expenses are expected to be approximately $2.95 billion and $2.00 billion, respectively.\r\n\u2022GAAP and non-GAAP", "gold_answer": "\u2022Revenue: $16.00 billion, plus or minus 2%;\n\u2022Gross Margins: 71.5%-72.%, plus or minor 50 basis points;\n\u2022GAAP operating expenses: $2.95 billion;\n\u2022Non - GAAP operating expense: $2.00 billion."} +{"number": 181, "llm_response": " \u2022Data Center revenue was a record $10.32 billion, up 141% from the previous quarter and up 171% from a year ago;\r\n\u2022Announced that the NVIDIA\u00ae GH200 Grace\u2122 Hopper\u2122 Superchip for complex AI and HPC workloads is shipping this quarter, with a second-generation version with HBM3e memory expected to ship in Q2 of calendar 2024.", "gold_answer": "\u2022Second-quarter revenue: record $10.32 billion;\n\u2022Up 141% from previous quarter;\nUp 171% from previousyear"} +{"number": 182, "llm_response": " \u2022Record revenue of $13.51 billion, up 88% from Q1, up 101% from year ago;\r\n\u2022Record data center revenue of $10.32 billion, up 141% from Q1, up 171% from year ago;\r\n\u2022GAAP earnings per diluted share for the quarter were $2.48, up 854% from a year ago and up 202% from the previous quarter.", "gold_answer": "1. Record revenue of $13.51 billion;\n2. Record Data Center revenue of $10.32 billion;\n3. GAAP earnings per diluted share of $2.48."} +{"number": 183, "llm_response": " \u2022Joe Biden's tenure as the 46th president of the United States began with his inauguration on January 20, 2021.\r\n\u2022Biden, a Democrat from Delaware, previously served as vice president under Barack Obama.\r\n\u2022He won the 2020 presidential election over Republican incumbent president Donald Trump.\r\n\u2022He is the oldest president in American history.", "gold_answer": "\u2022Joe Biden was inaugurated on January 20, 2021;\n\u2022Biden is the 46th President of the United States;\n\u2022Biden previously served as Vice President under Barack Obama;\n\u2022Biden won the 2020 presidential election over incumbent Donald Trump;\n\u2022Biden is the oldest president in American history."} +{"number": 184, "llm_response": " 1. Biden was generally acknowledged as the winner, though General Services Administration head Emily W. Murphy initially refused to begin the transition to the president-elect.\r\n2. On November 23, after Michigan certified its results, Murphy issued the letter of ascertainment, granting the Biden transition team access to federal funds and resources for an orderly transition.\r\n3. Two days after becoming the projected winner of the 2020 election, Biden announced the formation of a task force to", "gold_answer": "1. General Services Administration head Emily W. Murphy initially refused to begin the transition for president-elect Biden until Michigan certified its results on November 23;\n2. Two days after becoming the projected winner, Biden announced the formation of a task forceto advise on the COVID-19 pandemic;\n3. On January 5, 2021, the Democratic Party won control of the U.S. Senate, with the electoralvictories in Georgia by Jon Ossoff and Raphael Warnock;\n4. On January 6, 2021, a mob of Trump supporters stormed the Capitol, forcing Congress to evacuateduring the counting of the Electoral Votes;\n5. For the inauguration, more than 26,000 National Guard members were deployed."} +{"number": 185, "llm_response": " \u2022Record revenue of $13.51 billion, up 88% from Q1, up 101% from year ago;\r\n\u2022Record data center revenue of $10.32 billion, up 141% from Q1, up 171% from year ago;\r\n\u2022GAAP earnings per diluted share for the quarter were $2.48, up 854% from a year ago and up 202% from the previous quarter;\r\n\u2022Non-", "gold_answer": "\u2022Revenue: $52.9 million, up 10% in constant currency;\n\u2022Operating income: $22.4 billion, up 15% in constant currency;\n\u2022Net income: $18.3 billion, up 14% in constant currency;\n\u2022Diluted earnings per share: $2.45 billion, up 14% in constant currency."} +{"number": 186, "llm_response": " 1. The study of mechanical or 'formal' reasoning began with philosophers and mathematicians in antiquity.\r\n2. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as '0' and '1', could simulate both mathematical deduction and formal reasoning, which is known as the Church-Turing thesis.\r\n3. This, along with concurrent discoveries in cyber", "gold_answer": "1. The study of formal reasoning began in antiquity;\n2. These studies led directly to Alan Turing's theory of computation, which led researchers to consider the possibility of building an 'electronic brain';\n3. The first paper that recognized AI was published in 1943, and the field was founded in 1956."} +{"number": 187, "llm_response": " \u2022AI research in the 1960s was led by the \"AI 8\" and their students, who produced programs that the press described as \"astonishing\" and \"amazing.\"\r\n\u2022AI research was heavily funded by the U.S. Department of Defense, and laboratories were established around the world.\r\n\u2022AI research was funded by the commercial success of expert systems, which simulated the knowledge and analytical skills of human experts.\r\n\u2022AI research was", "gold_answer": "\u2022In the 1960s, AI researchers produced 'astonishing' programs that could learn checkers, word problems,prove logical theorems and speak English;\n\u2022AI research at the time was funded heavily by the U.S. Department of Defense;\n\u2022Herbert Simon predicted that machines would be capable in 20 years of doing human work;\n\u2022Marvin Minskey predicted that AI would be substantially solved within a generation;\n\u2022Due to these under-estimations of the difficulty of the problem, reduced government funding, and concerns about the real-world use of neural networks, the 'AI winter' followed;\n\u2022AI research was revived in the 1980s with expert systems, and the market for AI reached over $1 billion in 1985;\nHowever, with the collapse of the Lisp machine market in 1987, AI fell in disrepute again, and another AI winter ensued."} +{"number": 188, "llm_response": " Wednesday - $27, Thursday - $33, Friday - $36.", "gold_answer": "\u2022Wednesday - $27;\n\u2022Thursday - $33;\n\u2022Friday - $36."} +{"number": 189, "llm_response": " Joe Biden's tenure as the 46th president of the United States began with his inauguration on January 20, 2021.", "gold_answer": "Joe Biden is the 46th President of the United States."} +{"number": 190, "llm_response": " Biden was generally acknowledged as the winner.", "gold_answer": "After Biden won the election, he formed a pandemic task force, the democrats won the Senate, and a mob stormed the Capital on January 6."} +{"number": 191, "llm_response": " Not Found.", "gold_answer": "\u2022Office Commercial Products;\n\u2022Office Consumer Products;\n\u2022Microsoft 365 Consumer;\n\u2022LinkedIn;\n\u2022Dynamics Products;\n\u2022Server Products;\n\u2022More Personal Computing;\n\u2022Windows OEM;\n\u2022Devices;\n\u2022Windows Commercial;\n\u2022Xbox;\n\u2022Search and News Advertising."} +{"number": 192, "llm_response": " \u2022Invoice No. 234;\r\n\u2022ABC Company 8675 River Run Road, Marlington, CT 09392;\r\n\u2022Invoice to Delta Inc. 8723 Wilton Boulevard, Los Angeles, CA;\r\n\u2022LASER PRINTER;\r\n\u2022PAYMENT TERMS DUE DATE: May 3, 2022;\r\n\u2022QUANTITY DESCRIPTION: 30 - Box of Printer Paper;\r\n\u2022", "gold_answer": "\u2022Invoice Date: March 3, 2022;\\\u2022Invoice Number: No 234;\n\u2022From: ABC Company;\n\u2022To: Delta Inc.;\n\u2022Due Date: Due on Receipt;\n\u2022Total: $4565.00."} +{"number": 193, "llm_response": " Microsoft's AI models are coming together with the world's most universal user interface - natural language - to create a new era of computing.", "gold_answer": "According to Microsoft's CEO and CFO, the Microsoft Cloud is the platform for choice for AI,and growing 22% year-over-year as a result of focused execution."} +{"number": 194, "llm_response": " \u2022Emily W. Murphy, General Services Administration head;\r\n\u2022Biden Transition Team;\r\n\u2022Co-Chaired by Dr. Vivek Murthy, former Surgeon General, and David A. Kessler, former FDA Commissioner;\r\n\u2022Co-Chaired by Dr. Marcella Nunez-Smith, Yale University;\r\n\u2022Democratic Party wins control of the Senate on January 5, 2021.", "gold_answer": "\u2022Biden;\n\u2022Emily W. Murphy;\n\u2022Vivek Murthy;\n\u2022David A. Kessler;\n\u2022Marcella Nunez-Smith;\n\u2022Jon Ossoff;\n\u2022Raphael Warnock."} +{"number": 195, "llm_response": " Nokia will reduce its cost base and increase operation efficiency to address the challenging market environment.", "gold_answer": "Nokia is cutting 14,000 jobs as part of a cost reductionplan after reporting profits that declined 69% year-on-year."} +{"number": 196, "llm_response": " \u2022Senior Vice President, Event Planning;\r\n\u2022Represents Employer's Workforce Optimization Division;\r\n\u2022Reports to the Board of Directors;\r\n\u2022Devotes substantially all of his business time and attention to the operations of Employer and its subsidiaries;\r\n\u2022Responsible for running Employer's day-to-day operations;\r\n\u2022Performs faithfully, diligently and competently the duties and responsibilities of a SVP;\r\n\u2022Performs other duties and responsibilities as directed", "gold_answer": "\u2022Senior Vice President, Event Planning ('SVP') of Workforce Optimization Division;\n\u2022Report to Board of Directors;\n\u2022Devote substantially all of business time and attention to the operations of the Employer;\n\u2022responsible for running Employer's day-to-day operations;\n\u2022Perform faithfully, diligently and competently the duties and responsibilities of a SVP."} +{"number": 197, "llm_response": " NVIDIA announced financial results for the second quarter of fiscal 2024, reporting record revenue of $13.51 billion, up 88% from Q1, up 101% from the year ago.", "gold_answer": "NVIDIA Announces Second Quarter Fiscal 2024 Results with record revenue of $13.51 billion."} +{"number": 198, "llm_response": " \"Microsoft is the platform of choice to help customers get the most value out of their digital spend and innovate for this next generation of AI.\"", "gold_answer": "Microsoft Cloud is the platform of choice for the next generation of AI."} +{"number": 199, "llm_response": " \u2022Services Vendor Inc. \r\n\u2022100 Elm Street, Pleasantville, NY \r\n\u2022TO Alpha Inc., 5900 1st Street, Los Angeles, CA\r\n\u2022Description Front End Engineering Service $5000.00\r\n\u2022Back End Engineering Service $7500.00\r\n\u2022Quality Assurance Manager $10,000.00\r\n\u2022Total Amount $22,500.00\r\n\u2022Make all checks payable to Services Vendor Inc", "gold_answer": "\u2022Invoice From: Services Vendor Inc.;\n\u2022Invoice To: Alpha Inc.;\n\u2022Total Amount: $22,500.00;\n\u2022Payment Due: within 30 days;\n\u2022Contact: Bia Hermes;\n\u2022Invoice #0001;\u2022Data: 01/01/2022."} diff --git a/dragon-falcon-7b-answer_sheet_1113_2.jsonl b/dragon-falcon-7b-answer_sheet_1113_2.jsonl new file mode 100644 index 0000000..3147d69 --- /dev/null +++ b/dragon-falcon-7b-answer_sheet_1113_2.jsonl @@ -0,0 +1,200 @@ +{"number": 0, "llm_response": " $22,500.00", "gold_answer": "$22,500.00"} +{"number": 1, "llm_response": " Invoice # 0001", "gold_answer": "#0001"} +{"number": 2, "llm_response": " \u2022Front End Engineering Service - $5000.00\r\n\u2022Back End Engineering Service - $7500.00\r\n\u2022Quality Assurance Manager - $10,000.00", "gold_answer": "\u2022Front End Engineering Service;\n\u2022Back End Engineering Service;\n\u2022Quality Assurance Manager"} +{"number": 3, "llm_response": " Bia Hermes", "gold_answer": "Bia Hermes"} +{"number": 4, "llm_response": " #1000", "gold_answer": "#1000"} +{"number": 5, "llm_response": " Within 30 days of the invoice date.", "gold_answer": "within 30 days of 01/01/2022"} +{"number": 6, "llm_response": " May 3, 2022", "gold_answer": "May 3, 2022"} +{"number": 7, "llm_response": " $4565.00", "gold_answer": "4150.00"} +{"number": 8, "llm_response": " $4565.00", "gold_answer": "4565.00"} +{"number": 9, "llm_response": " \u2022Boxes of Printer Paper, 30, $30;\r\n\u2022Boxes of Printer Cartridge, 10, $75;\r\n\u2022Laser Printers, 5, $500;\r\n\u2022Laser Printer Cartridge, 10, $75.", "gold_answer": "\u202230 Boxes of Printer Paper;\n\u202210 Printer Cartridges;\n\u20225 Laser Printers."} +{"number": 10, "llm_response": " 234", "gold_answer": "234"} +{"number": 11, "llm_response": " ABC Company and Delta Inc.", "gold_answer": "ABC Company and Delta Inc."} +{"number": 12, "llm_response": " 30 boxes of printer paper", "gold_answer": "30"} +{"number": 13, "llm_response": " Due on receipt, May 3, 2022.", "gold_answer": "Due on Receipt"} +{"number": 14, "llm_response": " 8675 River Run Road Marlington, CT 09392", "gold_answer": "8675 River Run Road, Marlington, CT 09392"} +{"number": 15, "llm_response": " March 3, 2022", "gold_answer": "March 3, 2022"} +{"number": 16, "llm_response": " January 20, 2021.", "gold_answer": "January 20, 2021"} +{"number": 17, "llm_response": " Donald Trump", "gold_answer": "Donald Trump"} +{"number": 18, "llm_response": " Delaware.", "gold_answer": "Delaware"} +{"number": 19, "llm_response": " Barack Obama", "gold_answer": "Barack Obama"} +{"number": 20, "llm_response": " \u2022Vivek Murthy,\r\n\u2022David A. Kessler, and\r\n\u2022Marcella Nunez-Smith.", "gold_answer": "1. former Surgeon General Vivek Murthy;\n2. former FDA commissioner David A. Kessler;\n3. Yale University's Marcella Nunez-Smith."} +{"number": 21, "llm_response": " 1. Jon Ossoff, in a runoff election for a six-year term, and 2. Raphael Warnock, in a special runoff election for a two-year term.", "gold_answer": "Jon Ossoff and Raphael Warnock"} +{"number": 22, "llm_response": " Two years.", "gold_answer": "Two-year term"} +{"number": 23, "llm_response": " 26,000.", "gold_answer": "26,000"} +{"number": 24, "llm_response": " November 23, 2020", "gold_answer": "November 23"} +{"number": 25, "llm_response": " $52.9 billion.", "gold_answer": "$52.9 billion"} +{"number": 26, "llm_response": " 10% (up 15% in constant currency).", "gold_answer": "Increased 10%, up 15% in constant currency"} +{"number": 27, "llm_response": " March 31, 2023", "gold_answer": "March 31, 2023"} +{"number": 28, "llm_response": " According to the CFO, the increase in cloud revenue is due to focused execution by the sales teams and partners in this dynamic environment.", "gold_answer": "Focused execution by our sales teams and partners"} +{"number": 29, "llm_response": " $28.5 billion, up 22% (up 25% in constant currency) year-over-year.", "gold_answer": "$28.5 billion"} +{"number": 30, "llm_response": " 8%", "gold_answer": "10%"} +{"number": 31, "llm_response": " 65.4 million.", "gold_answer": "65.4 million"} +{"number": 32, "llm_response": " $22.1 billion.", "gold_answer": "$22.1 billion"} +{"number": 33, "llm_response": " 1956.", "gold_answer": "1956"} +{"number": 34, "llm_response": " The Church-Turing thesis is a theorem in theoretical computer science stating that any computable function can be simulated by a deterministic, Turing-complete computer.", "gold_answer": "A machine, by shuffling symbols as simple as '0' and '1' can simulate both mathematical deduction and formal reasoning."} +{"number": 35, "llm_response": " 1943", "gold_answer": "1943"} +{"number": 36, "llm_response": " 1956", "gold_answer": "1956"} +{"number": 37, "llm_response": " AI winter is a period when obtaining funding for AI projects is difficult.", "gold_answer": "A period when obtaining funding for AI projects is difficult."} +{"number": 38, "llm_response": " \"Machines will be capable, within twenty years, of doing any work a man can do.\"", "gold_answer": "He predicted that machines would be capable of doing any work a man can do within twenty years."} +{"number": 39, "llm_response": " 1987", "gold_answer": "1987."} +{"number": 40, "llm_response": " TestCo Software, Inc.", "gold_answer": "TestCo Software, Inc."} +{"number": 41, "llm_response": " Aphrodite Apollo (\"Executive\") and TestCo Software, Inc. (\"Employer\")", "gold_answer": "Aphrodite Apollo ('Executive') and TestCo Software, Inc. ('Company')"} +{"number": 42, "llm_response": " April 16, 2012", "gold_answer": "April 16, 2012."} +{"number": 43, "llm_response": " Customer", "gold_answer": "Customer"} +{"number": 44, "llm_response": " 30 days", "gold_answer": "30 days"} +{"number": 45, "llm_response": " 15 days after notice of breach.", "gold_answer": "If the breach is not cured within 15 days of notice of the breach."} +{"number": 46, "llm_response": " Yes.", "gold_answer": "Yes, by either party with 30 days written notice."} +{"number": 47, "llm_response": " SVP of the Workforce Optimization Division", "gold_answer": "Senior Vice President, Event Planning ('SVP') of the Workforce Optimization Division."} +{"number": 48, "llm_response": " $200,000", "gold_answer": "$200,000"} +{"number": 49, "llm_response": " Section 2.2", "gold_answer": "Section 2.2"} +{"number": 50, "llm_response": " Annual", "gold_answer": "Annually"} +{"number": 51, "llm_response": " Massachusetts", "gold_answer": "State of Massachusetts, except for Section 18(a)"} +{"number": 52, "llm_response": " Massachusetts law", "gold_answer": "State of Delaware"} +{"number": 53, "llm_response": " Section 18(a)", "gold_answer": "Section 19"} +{"number": 54, "llm_response": " 195.12 points, or 0.76%.", "gold_answer": "195.12 points"} +{"number": 55, "llm_response": " 438,000", "gold_answer": "438,000 jobs"} +{"number": 56, "llm_response": " 273,000.", "gold_answer": "273,000 jobs"} +{"number": 57, "llm_response": " 1.35%", "gold_answer": "Up 1.35%"} +{"number": 58, "llm_response": " Vibrant Industries Capital Advisors", "gold_answer": "Vibrant Industries Capital Advisors"} +{"number": 59, "llm_response": " 4.58%", "gold_answer": "4.58%"} +{"number": 60, "llm_response": " $350,000.", "gold_answer": "$350,000"} +{"number": 61, "llm_response": " $33.", "gold_answer": "$33"} +{"number": 62, "llm_response": " 61 years old.", "gold_answer": "61"} +{"number": 63, "llm_response": " One Red River Road, Stamford, Connecticut 08234", "gold_answer": "One Red River Road, Stamford, Connecticut 08234"} +{"number": 64, "llm_response": " Simmons Corp.", "gold_answer": "Simmons Corp"} +{"number": 65, "llm_response": " He is the Vice President of Sales.", "gold_answer": "Vice President of Sales"} +{"number": 66, "llm_response": " 101%", "gold_answer": "Up 101%"} +{"number": 67, "llm_response": " $13.51 billion", "gold_answer": "$13.51 billion"} +{"number": 68, "llm_response": " 7.5 million shares", "gold_answer": "7.5 million shares"} +{"number": 69, "llm_response": " Jensen Huang", "gold_answer": "Jensen Huang"} +{"number": 70, "llm_response": " 141%", "gold_answer": "Up 141% from Q1"} +{"number": 71, "llm_response": " $2.48", "gold_answer": "$2.48"} +{"number": 72, "llm_response": " September 28, 2023", "gold_answer": "September 28, 2023"} +{"number": 73, "llm_response": " 429%", "gold_answer": "Up 148%"} +{"number": 74, "llm_response": " No, 72.5% < 70%.", "gold_answer": "Yes, between 71.5% and 72.%"} +{"number": 75, "llm_response": " $16.00 billion", "gold_answer": "$16.00 billion, plus or minus 2%."} +{"number": 76, "llm_response": " Grace Hopper Superchip", "gold_answer": "NVIDIA GH200 Grace Hopper Superchip"} +{"number": 77, "llm_response": " 14.5%", "gold_answer": "14.5%, plus or minor 1%"} +{"number": 78, "llm_response": " $2.95 billion", "gold_answer": "$2.00 billion"} +{"number": 79, "llm_response": " $2.95 billion.", "gold_answer": "$2.95 billion"} +{"number": 80, "llm_response": " $2.49 billion", "gold_answer": "$2.49 billion"} +{"number": 81, "llm_response": " $379 million", "gold_answer": "$379 million"} +{"number": 82, "llm_response": " 28% increase from the previous quarter.", "gold_answer": "Up 28%"} +{"number": 83, "llm_response": " Pixar, Adobe, Apple and Autodesk.", "gold_answer": "Pixar, Adobe, Apple, Autodesk and NVIDIA"} +{"number": 84, "llm_response": " 35", "gold_answer": "35"} +{"number": 85, "llm_response": " $299", "gold_answer": "$299"} +{"number": 86, "llm_response": " MediaTek", "gold_answer": "MediaTek"} +{"number": 87, "llm_response": " Refinitiv", "gold_answer": "Refinitiv"} +{"number": 88, "llm_response": " Jerome Powell", "gold_answer": "Jerome Powell"} +{"number": 89, "llm_response": " Japan", "gold_answer": "Japan"} +{"number": 90, "llm_response": " 16.3%", "gold_answer": "Down 16.3%"} +{"number": 91, "llm_response": " 62.4 billion yen.", "gold_answer": "62.4 billion yen ($416.6 million)"} +{"number": 92, "llm_response": " 42.5 billion yen.", "gold_answer": "Deficit of 42.5 billion yen."} +{"number": 93, "llm_response": " 4.3% year-over-year", "gold_answer": "Up 4.3%"} +{"number": 94, "llm_response": " 3,577.", "gold_answer": "3,577"} +{"number": 95, "llm_response": " 3.2%", "gold_answer": "3.2%"} +{"number": 96, "llm_response": " Bank of America upgrades Target to buy from neutral.", "gold_answer": "Buy"} +{"number": 97, "llm_response": " Buy", "gold_answer": "Buy"} +{"number": 98, "llm_response": " $16 per share.", "gold_answer": "$16"} +{"number": 99, "llm_response": " $133 million.", "gold_answer": "133 million euros."} +{"number": 100, "llm_response": " Not Found.", "gold_answer": "Not Found"} +{"number": 101, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 102, "llm_response": " Not Found.", "gold_answer": "Not Found"} +{"number": 103, "llm_response": " Not Found.", "gold_answer": "Not Found"} +{"number": 104, "llm_response": " $3.38 billion.", "gold_answer": "Not Found."} +{"number": 105, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 106, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 107, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 108, "llm_response": " $253 million", "gold_answer": "Not Found."} +{"number": 109, "llm_response": " Not Found.", "gold_answer": "Not Found"} +{"number": 110, "llm_response": " Elon Musk", "gold_answer": "Not Found."} +{"number": 111, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 112, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 113, "llm_response": " 3.2%", "gold_answer": "Not Found"} +{"number": 114, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 115, "llm_response": " 100 laptops", "gold_answer": "Not Found."} +{"number": 116, "llm_response": " Sonny Perdue", "gold_answer": "Not Found."} +{"number": 117, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 118, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 119, "llm_response": " Not Found.", "gold_answer": "Not Found."} +{"number": 120, "llm_response": " Yes, the S&P 500 has increased 22%.", "gold_answer": "Yes, by more than 22%."} +{"number": 121, "llm_response": " No, the company is citing sticky retail inflation for the fourth time in two years.", "gold_answer": "No, Chipotle is increasing prices."} +{"number": 122, "llm_response": " No.", "gold_answer": "No, it is lowering the target from $38 to $36."} +{"number": 123, "llm_response": " No, 4.98 billion euros < 5 billion euros.", "gold_answer": "No."} +{"number": 124, "llm_response": " Yes, 62.4 billion yen ($416.6 million) is a surplus, which is higher than the 42.5 billion yen ($0.00) that was the polled expectation.", "gold_answer": "Yes."} +{"number": 125, "llm_response": " No, Dow Jones futures decreased 73 points, or 0.2%.", "gold_answer": "No, down 73 points."} +{"number": 126, "llm_response": " No, 4.9% is above 4.8%.", "gold_answer": "Yes, it topped 4.9%."} +{"number": 127, "llm_response": " No, the Nasdaq Composite underperformer, off 1.6%.", "gold_answer": "No, it was down 1.6%."} +{"number": 128, "llm_response": " No, automotive revenue was $253 million in the current quarter, down 15% from the previous quarter.", "gold_answer": "No, it was down 15% from the previous quarter."} +{"number": 129, "llm_response": " No, $253 million was down 15% from the previous quarter and up 15% from a year ago.", "gold_answer": "Yes, up 15%."} +{"number": 130, "llm_response": " No, $253 million < $250 million.", "gold_answer": "Yes"} +{"number": 131, "llm_response": " No, gaming revenue was up 11% from the previous quarter and up 22% from a year ago.", "gold_answer": "No, it was up 11% quarter-to-quarter and 22% year-to-year."} +{"number": 132, "llm_response": " Yes, 101% > 50%.", "gold_answer": "Yes, it increased 101% year-to-year."} +{"number": 133, "llm_response": " Yes, $3.38 billion.", "gold_answer": "Yes, $3.38 billion was returned"} +{"number": 134, "llm_response": " No, it decreased by -1%.", "gold_answer": "No, it decreased by 28%"} +{"number": 135, "llm_response": " No, Nokia's products are performing poorly in the market.", "gold_answer": "No, net sales declined 20% year-on-year, and the company has described the market environment as 'challenging.'"} +{"number": 136, "llm_response": " Yes, 3rd quarter net sales declined 20% year-on-year to 4.98 billion euros.", "gold_answer": "Yes, net sales declined 20% year-on-year."} +{"number": 137, "llm_response": " No.", "gold_answer": "No, exports to Asia decreased for the last nine months."} +{"number": 138, "llm_response": " No, exports decreased 16.3% year on year.", "gold_answer": "Yes, by 4.3% year to year."} +{"number": 139, "llm_response": " No.", "gold_answer": "No, the base salary is $200,000."} +{"number": 140, "llm_response": " $253 million + $100 million = $353 million.", "gold_answer": "$353 million"} +{"number": 141, "llm_response": " $16 per share.", "gold_answer": "$16"} +{"number": 142, "llm_response": " $12 - $3 = $9.", "gold_answer": "$9"} +{"number": 143, "llm_response": " January 15, 2019.", "gold_answer": "January 15, 2019"} +{"number": 144, "llm_response": " 37 + 6 = 43.", "gold_answer": "47 years old."} +{"number": 145, "llm_response": " $450,000.", "gold_answer": "$450,000"} +{"number": 146, "llm_response": " 3 days after January 12, which is January 15.", "gold_answer": "January 15"} +{"number": 147, "llm_response": " 2,000 shares.", "gold_answer": "2,000"} +{"number": 148, "llm_response": " $1200 + 50% = $1800.", "gold_answer": "$1800"} +{"number": 149, "llm_response": " 50% of the base salary amount.", "gold_answer": "$100,000"} +{"number": 150, "llm_response": " \u2022Liam - 41 years old\r\n\u2022Ashish - 27 years old\r\n\u2022Francois - 29 years old\r\n\u2022Myra - 23 years old\r\n\u2022Ashish - 27 years old", "gold_answer": "1. Liam - 41;\n2. Francois - 29;\n3. Ashish - 27;\n4. Myra - 23."} +{"number": 151, "llm_response": " Elizabeth is 35 years old.", "gold_answer": "John"} +{"number": 152, "llm_response": " Chocolate.", "gold_answer": "Chocolate - $3.25"} +{"number": 153, "llm_response": " Chocolate.", "gold_answer": "Cheese - $6.25"} +{"number": 154, "llm_response": " No.", "gold_answer": "No, chocolate has a lower price than coffee."} +{"number": 155, "llm_response": " S&P 500 futures added 39 points, or 1.2%.", "gold_answer": "Nasdaq 100 futures, up 63 points."} +{"number": 156, "llm_response": " S&P 500.", "gold_answer": "S&P 500 futures futures, up 1.2%."} +{"number": 157, "llm_response": " Red Dog LLC - $61.", "gold_answer": "River Industries - $53."} +{"number": 158, "llm_response": " $19 + $2 = $21.", "gold_answer": "$21"} +{"number": 159, "llm_response": " PO 212 - $950,000.", "gold_answer": "PO 1993"} +{"number": 160, "llm_response": " (A) $52.9 billion.", "gold_answer": "(D) $28.5 billion"} +{"number": 161, "llm_response": " (A) Microsoft.", "gold_answer": "(B) TestCo Software."} +{"number": 162, "llm_response": " (B) $7500.", "gold_answer": "(D) $10,000."} +{"number": 163, "llm_response": " (C) 14%.", "gold_answer": "(C) 14%."} +{"number": 164, "llm_response": " (A) 20%.", "gold_answer": "(B) 69%."} +{"number": 165, "llm_response": " \u20222019 - $53,823\r\n\u20222020 - $31,536\r\n\u20222021 - $31,536\r\n\u20222022 - $81,462", "gold_answer": "\u20222019 - 24,578;\n\u20222020 - 31,536;\n\u20222021 - 53,823;\n\u20222022 - 81,462."} +{"number": 166, "llm_response": " $297", "gold_answer": "297."} +{"number": 167, "llm_response": " $13,832", "gold_answer": "6,496."} +{"number": 168, "llm_response": " $24,578", "gold_answer": "18,402."} +{"number": 169, "llm_response": " $13,719", "gold_answer": "13,719"} +{"number": 170, "llm_response": " \u2022Herbert Simon,\r\n\u2022Marvin Minsky,\r\n\u2022James Lighthill,\r\n\u2022Sir James Lighthill,\r\n\u2022Papert,\r\n\u2022Minsky's book Perceptrons,\r\n\u2022AI Winter,\r\n\u2022AI Research,\r\n\u2022Expert Systems,\r\n\u2022AI Market,\r\n\u2022Lisp Machines,\r\n\u2022Lisp Machine market,\r\n\u2022Collapse of the Lisp Machines market, and\r\n", "gold_answer": "\u2022Herbert Simon;\n\u2022Marvin Minsky;\n\u2022Sir James Lighthill;\n\u2022Papert."} +{"number": 171, "llm_response": " \u2022ServiceNow and Accenture, to develop AI Lighthouse, a first-of-its-kind program to fast-track the development and adoption of enterprise generative AI capabilities.\r\n\u2022VMware, to extend the companies' strategic partnership to ready enterprises running VMware's cloud infrastructure for the era of generative AI.\r\n\u2022Snowflake, to provide businesses with an accelerated path to create customized generative AI applications using their own proprietary data.\r\n\u2022WPP, to develop", "gold_answer": "\u2022Amazon Web Services;\n\u2022Microsoft Azure;\u2022\nRegional Cloud Service Providers;\n\u2022ServiceNow;\n\u2022Accenture;\n\u2022VMWare;\n\u2022Snowflake;\n\u2022WPP;\n\u2022Softbank;\n\u2022Hugging Face."} +{"number": 172, "llm_response": " \u2022American Airlines,\r\n\u2022Fifth Third,\r\n\u2022Truist Financial,\r\n\u2022CSX,\r\n\u2022KeyCorp,\r\n\u2022Truist Financial,\r\n\u2022Fifth Third,\r\n\u2022Truist Financial,\r\n\u2022CSX,\r\n\u2022American Airlines,\r\n\u2022LSEG, formerly known as Refinitiv.", "gold_answer": "\u2022Tesla;\n;\u2022Netflix;\n\u2022KeyCorp;\n\u2022Fifth Third;\n\u2022Truist Financial;\n\u2022American Airlines;\n\u2022Union Pacific;\n\u2022CSX."} +{"number": 173, "llm_response": " 1. The U.S. government, \r\n2. The British government, and \r\n3. The Japanese government.", "gold_answer": "1. U.S;\n2. UK;\n3. Japan."} +{"number": 174, "llm_response": " \u2022Stocks initially fell on the stronger-than-expected jobs report.\r\n\u2022At its session low, the Dow had fallen as much as 198 points.\r\n\u2022The Nasdaq and the S&P 500 slid by 0.8% during their lowest points in the day.\r\n\u2022Traders were unclear of the reason for the intraday reversal.", "gold_answer": "Traders were unclear of the reason, but some noted it could be the softer wage numberin the jobs report."} +{"number": 175, "llm_response": " To address the challenging market environment.", "gold_answer": "Cost cutting plan"} +{"number": 176, "llm_response": " Tesla missed expectations on earnings and revenue in the third quarter.", "gold_answer": "The company missed expectations on earnings and revenue,and Elon Musk warned that the Cybertruck will not producepositive cash flow for more than a year."} +{"number": 177, "llm_response": " \u2022Democratic Party won control of the United States Senate, effective January 5, 2021, as a result of electoral victories in Georgia.\r\n\u2022President-elect Biden had supported and campaigned for both candidates prior to the runoff elections on January 5.\r\n\u2022Trump supporters violently stormed the Capitol in the hope of overturning Biden's election, forcing Congress to evacuate during the counting of the electoral college votes.", "gold_answer": "The electoral victories in Georgia by Jon Ossoff and Raphael Warnock."} +{"number": 178, "llm_response": " Red River Industries", "gold_answer": "North Industries"} +{"number": 179, "llm_response": " Blue Moon Tech", "gold_answer": "Blue Moon Tech."} +{"number": 180, "llm_response": " \u2022Revenue is expected to be $16.00 billion, plus or minus 2%;\r\n\u2022GAAP and non-GAAP gross margins are expected to be 71.5% and 72.5%, respectively, plus or minus 50 basis points;\r\n\u2022GAAP and non-GAAP operating expenses are expected to be approximately $2.95 billion and $2.00 billion, respectively;\r\n\u2022GAAP and non-GAAP", "gold_answer": "\u2022Revenue: $16.00 billion, plus or minus 2%;\n\u2022Gross Margins: 71.5%-72.%, plus or minor 50 basis points;\n\u2022GAAP operating expenses: $2.95 billion;\n\u2022Non - GAAP operating expense: $2.00 billion."} +{"number": 181, "llm_response": " \u2022Data Center revenue was a record $10.32 billion, up 141% from the previous quarter and up 171% from a year ago.\r\n\u2022Announced that the NVIDIA Grace Hopper Superchip for complex AI and HPC workloads is shipping this quarter, with a second-generation version with HBM3e memory expected to ship in Q2 of calendar 2024.", "gold_answer": "\u2022Second-quarter revenue: record $10.32 billion;\n\u2022Up 141% from previous quarter;\nUp 171% from previousyear"} +{"number": 182, "llm_response": " 1. Record revenue of $13.51 billion, up 88% from Q1, up 101% from year ago\r\n2. Record data center revenue of $10.32 billion, up 141% from Q1, up 171% from year ago.\r\n3. GAAP EPS of $2.48, up 854% from a year ago, and up 202% from the previous quarter.", "gold_answer": "1. Record revenue of $13.51 billion;\n2. Record Data Center revenue of $10.32 billion;\n3. GAAP earnings per diluted share of $2.48."} +{"number": 183, "llm_response": " \u2022Joe Biden's tenure as the 46th president of the United States began with his inauguration on January 20, 2021.\r\n\u2022Biden, a Democrat from Delaware, previously served as vice president under Barack Obama.\r\n\u2022He defeated Republican incumbent president Donald Trump in the 2020 presidential election.\r\n\u2022Upon his inauguration, he became the oldest president in American history.", "gold_answer": "\u2022Joe Biden was inaugurated on January 20, 2021;\n\u2022Biden is the 46th President of the United States;\n\u2022Biden previously served as Vice President under Barack Obama;\n\u2022Biden won the 2020 presidential election over incumbent Donald Trump;\n\u2022Biden is the oldest president in American history."} +{"number": 184, "llm_response": " 1. Biden was generally acknowledged as the winner of the 2020 election, though there was a delay in the transition to the president-elect.\r\n2. Biden was the projected winner of the 2020 election on November 23, 2020, and announced the formation of a task force to advise him on the COVID-19 pandemic during the transition.\r\n3. On January 5, 2021, the Democratic Party won control of the United States", "gold_answer": "1. General Services Administration head Emily W. Murphy initially refused to begin the transition for president-elect Biden until Michigan certified its results on November 23;\n2. Two days after becoming the projected winner, Biden announced the formation of a task forceto advise on the COVID-19 pandemic;\n3. On January 5, 2021, the Democratic Party won control of the U.S. Senate, with the electoralvictories in Georgia by Jon Ossoff and Raphael Warnock;\n4. On January 6, 2021, a mob of Trump supporters stormed the Capitol, forcing Congress to evacuateduring the counting of the Electoral Votes;\n5. For the inauguration, more than 26,000 National Guard members were deployed."} +{"number": 185, "llm_response": " \u2022Record revenue of $13.51 billion, up 88% from Q1, up 101% from year ago;\r\n\u2022Record data center revenue of $10.32 billion, up 141% from Q1, up 171% from year ago;\r\n\u2022GAAP earnings per diluted share for the quarter were $2.48, up 854% from a year ago and up 202% from the previous quarter;\r\n\u2022Non-", "gold_answer": "\u2022Revenue: $52.9 million, up 10% in constant currency;\n\u2022Operating income: $22.4 billion, up 15% in constant currency;\n\u2022Net income: $18.3 billion, up 14% in constant currency;\n\u2022Diluted earnings per share: $2.45 billion, up 14% in constant currency."} +{"number": 186, "llm_response": " 1. The study of mechanical or formal reasoning began with philosophers and mathematicians in antiquity.\r\n2. The study of logic led directly to Alan Turing's theory of computation.\r\n3. The Church-Turing thesis, which states that a machine can simulate both mathematical deduction and formal reasoning.", "gold_answer": "1. The study of formal reasoning began in antiquity;\n2. These studies led directly to Alan Turing's theory of computation, which led researchers to consider the possibility of building an 'electronic brain';\n3. The first paper that recognized AI was published in 1943, and the field was founded in 1956."} +{"number": 187, "llm_response": " \u2022In the 1960s, the U.S. government and the British government cut off exploratory research in response to the criticism of Sir James Lighthill and ongoing pressure from the U.S. Congress to fund more productive projects.\r\n\u2022Minsky and Papert's book Perceptrons was understood as proving that artificial neural networks approach would never be useful for solving real-world tasks, thus discrediting the approach.\r\n\u2022In the early 1980s,", "gold_answer": "\u2022In the 1960s, AI researchers produced 'astonishing' programs that could learn checkers, word problems,prove logical theorems and speak English;\n\u2022AI research at the time was funded heavily by the U.S. Department of Defense;\n\u2022Herbert Simon predicted that machines would be capable in 20 years of doing human work;\n\u2022Marvin Minskey predicted that AI would be substantially solved within a generation;\n\u2022Due to these under-estimations of the difficulty of the problem, reduced government funding, and concerns about the real-world use of neural networks, the 'AI winter' followed;\n\u2022AI research was revived in the 1980s with expert systems, and the market for AI reached over $1 billion in 1985;\nHowever, with the collapse of the Lisp machine market in 1987, AI fell in disrepute again, and another AI winter ensued."} +{"number": 188, "llm_response": " Wednesday - $27, Thursday - $33, Friday - $36.", "gold_answer": "\u2022Wednesday - $27;\n\u2022Thursday - $33;\n\u2022Friday - $36."} +{"number": 189, "llm_response": " Joe Biden's tenure as the 46th president of the United States began with his inauguration on January 20, 2021.", "gold_answer": "Joe Biden is the 46th President of the United States."} +{"number": 190, "llm_response": " Biden was generally acknowledged as the winner, but General Services Administration head Emily W. Murphy refused to begin the transition to the president-elect.", "gold_answer": "After Biden won the election, he formed a pandemic task force, the democrats won the Senate, and a mob stormed the Capital on January 6."} +{"number": 191, "llm_response": " \u2022Microsoft Teams;\r\n\u2022Microsoft Azure;\r\n\u2022Microsoft Dynamics;\r\n\u2022Microsoft 365;\r\n\u2022Microsoft Windows;\r\n\u2022Microsoft Office;\r\n\u2022Microsoft Edge;\r\n\u2022Microsoft Teams;\r\n\u2022Microsoft Azure;\r\n\u2022Microsoft Dynamics;\r\n\u2022Microsoft 365;\r\n\u2022Microsoft Windows;\r\n\u2022Microsoft Office;\r\n\u2022Microsoft Edge;\r\n\u2022Microsoft Teams;\r\n\u2022Microsoft Azure;\r\n\u2022Microsoft", "gold_answer": "\u2022Office Commercial Products;\n\u2022Office Consumer Products;\n\u2022Microsoft 365 Consumer;\n\u2022LinkedIn;\n\u2022Dynamics Products;\n\u2022Server Products;\n\u2022More Personal Computing;\n\u2022Windows OEM;\n\u2022Devices;\n\u2022Windows Commercial;\n\u2022Xbox;\n\u2022Search and News Advertising."} +{"number": 192, "llm_response": " \u2022Invoice date: March 3, 2022;\r\n\u2022Invoice number: 234;\r\n\u2022Invoice to: Delta Inc.;\r\n\u2022Invoice from: ABC Company;\r\n\u2022Invoice to: 8723 Wilton Boulevard, Los Angeles, CA;\r\n\u2022Invoice payment terms due date: May 3, 2022;\r\n\u2022Invoice quantity: 30 boxes of printer paper;\r\n\u2022Invoice unit", "gold_answer": "\u2022Invoice Date: March 3, 2022;\\\u2022Invoice Number: No 234;\n\u2022From: ABC Company;\n\u2022To: Delta Inc.;\n\u2022Due Date: Due on Receipt;\n\u2022Total: $4565.00."} +{"number": 193, "llm_response": " Microsoft's AI and natural language capabilities are coming together with the world's most universal user interface - natural language - to create a new era of computing.", "gold_answer": "According to Microsoft's CEO and CFO, the Microsoft Cloud is the platform for choice for AI,and growing 22% year-over-year as a result of focused execution."} +{"number": 194, "llm_response": " \u2022Emily W. Murphy, General Services Administration head;\r\n\u2022Joe Biden, President-elect;\r\n\u2022Vivek Murthy, former Surgeon General;\r\n\u2022David A. Kessler, former FDA commissioner;\r\n\u2022Marcella Nunez-Smith, Yale University's Professor;\r\n\u2022Jon Ossoff, Georgia Senator;\r\n\u2022Raphael Warnock, Georgia Senator.", "gold_answer": "\u2022Biden;\n\u2022Emily W. Murphy;\n\u2022Vivek Murthy;\n\u2022David A. Kessler;\n\u2022Marcella Nunez-Smith;\n\u2022Jon Ossoff;\n\u2022Raphael Warnock."} +{"number": 195, "llm_response": " Nokia announced that it will reduce its cost base and increase operation efficiency to address the challenging market environment.", "gold_answer": "Nokia is cutting 14,000 jobs as part of a cost reductionplan after reporting profits that declined 69% year-on-year."} +{"number": 196, "llm_response": " \u2022Senior Vice President, Event Planning;\r\n\u2022Report to the Board of Directors;\r\n\u2022Devote substantially all of his business time and attention and his best efforts and ability to the operations of Employer and its subsidiaries;\r\n\u2022Run day-to-day operations;\r\n\u2022Perform faithfully, diligently and competently the duties and responsibilities of a SVP;\r\n\u2022Perform other duties and responsibilities as directed by the Board and consistent with such position.", "gold_answer": "\u2022Senior Vice President, Event Planning ('SVP') of Workforce Optimization Division;\n\u2022Report to Board of Directors;\n\u2022Devote substantially all of business time and attention to the operations of the Employer;\n\u2022responsible for running Employer's day-to-day operations;\n\u2022Perform faithfully, diligently and competently the duties and responsibilities of a SVP."} +{"number": 197, "llm_response": " NVIDIA announces financial results for second quarter of fiscal 2024, with record revenue of $13.51 billion, up 88% from Q1, up 101% from year ago, and record data center revenue of $10.32 billion, up 141% from Q1, up 171% from year ago.", "gold_answer": "NVIDIA Announces Second Quarter Fiscal 2024 Results with record revenue of $13.51 billion."} +{"number": 198, "llm_response": " \"Microsoft is the platform of choice for customers to get the most value out of their digital spend and innovate for the next generation of AI.\"", "gold_answer": "Microsoft Cloud is the platform of choice for the next generation of AI."} +{"number": 199, "llm_response": " \u2022Services Vendor Inc.\r\n\u2022100 Elm Street, Pleasantville, NY\r\n\u2022TO Alpha Inc., 5900 1st Street, Los Angeles, CA\r\n\u2022Description: Front End Engineering Service $5000.00\r\n\u2022Back End Engineering Service $7500.00\r\n\u2022Quality Assurance Manager $10,000.00\r\n\u2022Total Amount $22,500.00", "gold_answer": "\u2022Invoice From: Services Vendor Inc.;\n\u2022Invoice To: Alpha Inc.;\n\u2022Total Amount: $22,500.00;\n\u2022Payment Due: within 30 days;\n\u2022Contact: Bia Hermes;\n\u2022Invoice #0001;\u2022Data: 01/01/2022."} diff --git a/dragon-falcon-7b-core_test_1113_1.jsonl b/dragon-falcon-7b-core_test_1113_1.jsonl new file mode 100644 index 0000000..f714301 --- /dev/null +++ b/dragon-falcon-7b-core_test_1113_1.jsonl @@ -0,0 +1,200 @@ +{"number": 0, "llm_response": " $22,500.00", "gold_answer": "$22,500.00", "prompt": "What is the total amount of the invoice?", "usage": {"input": 164, "metric": "tokens", "output": 8, "processing_time": 1.6303207874298096, "total": 172}, "category": "core"} +{"number": 1, "llm_response": " Invoice # 0001", "gold_answer": "#0001", "prompt": "What is the invoice number?", "usage": {"input": 161, "metric": "tokens", "output": 7, "processing_time": 0.24559283256530762, "total": 168}, "category": "core"} +{"number": 2, "llm_response": " \u2022Front End Engineering Service - $5,000\r\n\u2022Back End Engineering Service - $7,500\r\n\u2022Quality Assurance Manager - $10,000", "gold_answer": "\u2022Front End Engineering Service;\n\u2022Back End Engineering Service;\n\u2022Quality Assurance Manager", "prompt": "What is a list of the items being purchased?", "usage": {"input": 165, "metric": "tokens", "output": 41, "processing_time": 1.549907922744751, "total": 206}, "category": "core"} +{"number": 3, "llm_response": " Bia Hermes", "gold_answer": "Bia Hermes", "prompt": "What is the name of the contact for question?", "usage": {"input": 165, "metric": "tokens", "output": 4, "processing_time": 0.14754891395568848, "total": 169}, "category": "core"} +{"number": 4, "llm_response": " #1000", "gold_answer": "#1000", "prompt": "What is the PO number?", "usage": {"input": 161, "metric": "tokens", "output": 5, "processing_time": 0.18185758590698242, "total": 166}, "category": "core"} +{"number": 5, "llm_response": " Within 30 days", "gold_answer": "within 30 days of 01/01/2022", "prompt": "When is payment due?", "usage": {"input": 160, "metric": "tokens", "output": 5, "processing_time": 0.18038368225097656, "total": 165}, "category": "core"} +{"number": 6, "llm_response": " May 3, 2022", "gold_answer": "May 3, 2022", "prompt": "When is payment due?", "usage": {"input": 167, "metric": "tokens", "output": 8, "processing_time": 0.2862088680267334, "total": 175}, "category": "core"} +{"number": 7, "llm_response": " $4565.00", "gold_answer": "4150.00", "prompt": "What is the subtotal amount?", "usage": {"input": 169, "metric": "tokens", "output": 7, "processing_time": 0.25138378143310547, "total": 176}, "category": "core"} +{"number": 8, "llm_response": " $4565.00", "gold_answer": "4565.00", "prompt": "What is the total amount?", "usage": {"input": 168, "metric": "tokens", "output": 7, "processing_time": 0.24828386306762695, "total": 175}, "category": "core"} +{"number": 9, "llm_response": " \u2022Boxes of Printer Paper, 30 of which are at $30 each, for a total of $900;\r\n\u202210 Printer Cartridges, at $75 each, for a total of $750;\r\n\u20225 Laser printers, at $500 each, for a total of $2500;\r\n\u202210 Printer Cartridges, at $75 each, for a total of $750;\r\n\u20225 Laser printers, at", "gold_answer": "\u202230 Boxes of Printer Paper;\n\u202210 Printer Cartridges;\n\u20225 Laser Printers.", "prompt": "What is a list of the purchased items?", "usage": {"input": 171, "metric": "tokens", "output": 101, "processing_time": 3.7404420375823975, "total": 272}, "category": "core"} +{"number": 10, "llm_response": " 234", "gold_answer": "234", "prompt": "What is the invoice number?", "usage": {"input": 168, "metric": "tokens", "output": 3, "processing_time": 0.11272692680358887, "total": 171}, "category": "core"} +{"number": 11, "llm_response": " Delta Inc. and ABC Company", "gold_answer": "ABC Company and Delta Inc.", "prompt": "What is the name of the two companies?", "usage": {"input": 171, "metric": "tokens", "output": 7, "processing_time": 0.25048279762268066, "total": 178}, "category": "core"} +{"number": 12, "llm_response": " 30", "gold_answer": "30", "prompt": "How many boxes of paper are being purchased?", "usage": {"input": 171, "metric": "tokens", "output": 3, "processing_time": 0.11308789253234863, "total": 174}, "category": "core"} +{"number": 13, "llm_response": " Due on receipt on May 3, 2022", "gold_answer": "Due on Receipt", "prompt": "What are the payment terms?", "usage": {"input": 168, "metric": "tokens", "output": 12, "processing_time": 0.41799116134643555, "total": 180}, "category": "core"} +{"number": 14, "llm_response": " 8675 River Run Road, Marlington, CT 09392", "gold_answer": "8675 River Run Road, Marlington, CT 09392", "prompt": "What is the address of ABC Company?", "usage": {"input": 170, "metric": "tokens", "output": 15, "processing_time": 0.5309417247772217, "total": 185}, "category": "core"} +{"number": 15, "llm_response": " March 3, 2022", "gold_answer": "March 3, 2022", "prompt": "What is the date of the invoice?", "usage": {"input": 170, "metric": "tokens", "output": 8, "processing_time": 0.28344011306762695, "total": 178}, "category": "core"} +{"number": 16, "llm_response": " January 20, 2021.", "gold_answer": "January 20, 2021", "prompt": "When was Biden inaugurated?", "usage": {"input": 89, "metric": "tokens", "output": 9, "processing_time": 0.31268835067749023, "total": 98}, "category": "core"} +{"number": 17, "llm_response": " Donald Trump.", "gold_answer": "Donald Trump", "prompt": "Who was Biden's opponent in the 2020 presidential election?", "usage": {"input": 98, "metric": "tokens", "output": 4, "processing_time": 0.14264655113220215, "total": 102}, "category": "core"} +{"number": 18, "llm_response": " Delaware", "gold_answer": "Delaware", "prompt": "What state is Biden from?", "usage": {"input": 90, "metric": "tokens", "output": 2, "processing_time": 0.07265996932983398, "total": 92}, "category": "core"} +{"number": 19, "llm_response": " Donald Trump", "gold_answer": "Barack Obama", "prompt": "Who was President when Biden was Vice President?", "usage": {"input": 93, "metric": "tokens", "output": 3, "processing_time": 0.10693621635437012, "total": 96}, "category": "core"} +{"number": 20, "llm_response": " \u2022Vivek Murthy,\r\n\u2022David Kessler, and\r\n\u2022Marcella Nunez-Smith.", "gold_answer": "1. former Surgeon General Vivek Murthy;\n2. former FDA commissioner David A. Kessler;\n3. Yale University's Marcella Nunez-Smith.", "prompt": "What is a list of the three people that served as co-chairs of the COVID task force?", "usage": {"input": 325, "metric": "tokens", "output": 29, "processing_time": 1.1548612117767334, "total": 354}, "category": "core"} +{"number": 21, "llm_response": " Jon Ossoff and Raphael Warnock.", "gold_answer": "Jon Ossoff and Raphael Warnock", "prompt": "Who are the two people that won elections in Georgia?", "usage": {"input": 316, "metric": "tokens", "output": 10, "processing_time": 0.38385844230651855, "total": 326}, "category": "core"} +{"number": 22, "llm_response": " Two years.", "gold_answer": "Two-year term", "prompt": "What is the length of Raphael Warnock's term?", "usage": {"input": 317, "metric": "tokens", "output": 4, "processing_time": 0.17698431015014648, "total": 321}, "category": "core"} +{"number": 23, "llm_response": " 26,000", "gold_answer": "26,000", "prompt": "How many national guard members were deployed on January 6?", "usage": {"input": 317, "metric": "tokens", "output": 5, "processing_time": 0.20994806289672852, "total": 322}, "category": "core"} +{"number": 24, "llm_response": " November 23, 2020", "gold_answer": "November 23", "prompt": "When was the letter of ascertainment issued?", "usage": {"input": 314, "metric": "tokens", "output": 8, "processing_time": 0.3115231990814209, "total": 322}, "category": "core"} +{"number": 25, "llm_response": " $52.9 billion.", "gold_answer": "$52.9 billion", "prompt": "What was Microsoft's revenue in the 3rd quarter?", "usage": {"input": 185, "metric": "tokens", "output": 8, "processing_time": 0.28634166717529297, "total": 193}, "category": "core"} +{"number": 26, "llm_response": " 10% (up 15% in constant currency)", "gold_answer": "Increased 10%, up 15% in constant currency", "prompt": "What was the improvement in operating income year-to-year?", "usage": {"input": 185, "metric": "tokens", "output": 14, "processing_time": 0.48785853385925293, "total": 199}, 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"gold_answer": "Not Found.", "prompt": "When was the A100 chip released?", "usage": {"input": 283, "metric": "tokens", "output": 4, "processing_time": 0.1766982078552246, "total": 287}, "category": "not_found_classification"} +{"number": 107, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "What was gaming revenue for the quarter?", "usage": {"input": 104, "metric": "tokens", "output": 4, "processing_time": 0.1409163475036621, "total": 108}, "category": "not_found_classification"} +{"number": 108, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "What was third quarter revenue?", "usage": {"input": 102, "metric": "tokens", "output": 4, "processing_time": 0.14280438423156738, "total": 106}, "category": "not_found_classification"} +{"number": 109, "llm_response": " Not Found.", "gold_answer": "Not Found", "prompt": "What is gaming revenue for the full year FY24?", "usage": {"input": 347, "metric": "tokens", "output": 4, "processing_time": 0.17862701416015625, "total": 351}, "category": "not_found_classification"} +{"number": 110, "llm_response": " Elon Musk", "gold_answer": "Not Found.", "prompt": "Who is the CEO of Netflix?", "usage": {"input": 416, "metric": "tokens", "output": 3, "processing_time": 0.1539919376373291, "total": 419}, "category": "not_found_classification"} +{"number": 111, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "What was China's trade balance in September?", "usage": {"input": 152, "metric": "tokens", "output": 4, "processing_time": 0.14581704139709473, "total": 156}, "category": "not_found_classification"} +{"number": 112, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "What was Japan's trade balance in March?", "usage": {"input": 152, "metric": "tokens", "output": 4, "processing_time": 0.1464078426361084, "total": 156}, "category": "not_found_classification"} +{"number": 113, "llm_response": " 3.2%", "gold_answer": "Not Found", "prompt": "What was the cost of living adjustment for 2023?", "usage": {"input": 675, "metric": "tokens", "output": 6, "processing_time": 0.3094909191131592, "total": 681}, "category": "not_found_classification"} +{"number": 114, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "When is TJX reporting earnings?", "usage": {"input": 670, "metric": "tokens", "output": 4, "processing_time": 0.2394709587097168, "total": 674}, "category": "not_found_classification"} +{"number": 115, "llm_response": " 100 laptops.", "gold_answer": "Not Found.", "prompt": "How many were laptops were purchased?", "usage": {"input": 162, "metric": "tokens", "output": 5, "processing_time": 0.18417835235595703, "total": 167}, "category": "not_found_classification"} +{"number": 116, "llm_response": " Sonny Perdue", "gold_answer": "Not Found.", "prompt": "Who is the secretary of agriculture?", "usage": {"input": 91, "metric": "tokens", "output": 4, "processing_time": 0.14058876037597656, "total": 95}, "category": "not_found_classification"} +{"number": 117, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "What is the executive's salary?", "usage": {"input": 342, "metric": "tokens", "output": 4, "processing_time": 0.1786038875579834, "total": 346}, "category": "not_found_classification"} +{"number": 118, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "What is the 4th quarter revenue of Broadcom?", "usage": {"input": 476, "metric": "tokens", "output": 4, "processing_time": 0.19226288795471191, "total": 480}, "category": "not_found_classification"} +{"number": 119, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "How old is Maribeth?", "usage": {"input": 39, "metric": "tokens", "output": 4, "processing_time": 0.141160249710083, "total": 43}, "category": "not_found_classification"} +{"number": 120, "llm_response": " Yes, the S&P 500 has increased 22% from 3,577 to 4,376.", "gold_answer": "Yes, by more than 22%.", "prompt": "Has the S&P increased over the last year?", "usage": {"input": 674, "metric": "tokens", "output": 25, "processing_time": 1.0243401527404785, "total": 699}, "category": "boolean_mc"} +{"number": 121, "llm_response": " No, the company is citing \"sticky retail inflation\" for the fourth time in two years.", "gold_answer": "No, Chipotle is increasing prices.", "prompt": "Is Chipotle lowering prices?", "usage": {"input": 669, "metric": "tokens", "output": 22, "processing_time": 0.8631875514984131, "total": 691}, "category": "boolean_mc"} +{"number": 122, "llm_response": " No.", "gold_answer": "No, it is lowering the target from $38 to $36.", "prompt": "Is Barclay's raising its price target on KHC?", "usage": {"input": 676, "metric": "tokens", "output": 3, "processing_time": 0.2056591510772705, "total": 679}, "category": "boolean_mc"} +{"number": 123, "llm_response": " No, 4.98 billion euros < 5 billion euros.", "gold_answer": "No.", "prompt": "Were third quarter sales more than 5 billion euros?", "usage": {"input": 119, "metric": "tokens", "output": 16, "processing_time": 0.5564653873443604, "total": 135}, "category": "boolean_mc"} +{"number": 124, "llm_response": " Yes, the trade surplus was 62.4 billion yen, beating the 42.5 billion yen surplus that economists polled by Reuters were expecting.", "gold_answer": "Yes.", "prompt": "Did the trade surplus beat expectations?", "usage": {"input": 149, "metric": "tokens", "output": 32, "processing_time": 1.1018640995025635, "total": 181}, "category": "boolean_mc"} +{"number": 125, "llm_response": " No, Dow Jones futures fell 73 points.", "gold_answer": "No, down 73 points.", "prompt": "Did Dow Jones futures increase?", "usage": {"input": 415, "metric": "tokens", "output": 11, "processing_time": 0.4260258674621582, "total": 426}, "category": "boolean_mc"} +{"number": 126, "llm_response": " No, 4.9% > 4.8%.", "gold_answer": "Yes, it topped 4.9%.", "prompt": "Is the yield on the 10-year above 4.8%?", "usage": {"input": 425, "metric": "tokens", "output": 15, "processing_time": 0.5636718273162842, "total": 440}, "category": "boolean_mc"} +{"number": 127, "llm_response": " No, the Nasdaq Composite was the underperformer, off 1.6%.", "gold_answer": "No, it was down 1.6%.", "prompt": "Did the Nasdaq Composite increase?", "usage": {"input": 415, "metric": "tokens", "output": 18, "processing_time": 0.6744356155395508, "total": 433}, "category": "boolean_mc"} +{"number": 128, "llm_response": " No, it decreased by 15%.", "gold_answer": "No, it was down 15% from the previous quarter.", "prompt": "Did automotive revenue increase in the current quarter compared to last quarter?", "usage": {"input": 109, "metric": "tokens", "output": 9, "processing_time": 0.3154258728027344, "total": 118}, "category": "boolean_mc"} +{"number": 129, "llm_response": " No, $253 million is less than $300 million.", "gold_answer": "Yes, up 15%.", "prompt": "Did automotive revenue increase year-over-year?", "usage": {"input": 106, "metric": "tokens", "output": 15, "processing_time": 0.5284769535064697, "total": 121}, "category": "boolean_mc"} +{"number": 130, "llm_response": " No, $253 million < $250 million.", "gold_answer": "Yes", "prompt": "Was automotive revenue more than $250 million?", "usage": {"input": 106, "metric": "tokens", "output": 14, "processing_time": 0.49077463150024414, "total": 120}, "category": "boolean_mc"} +{"number": 131, "llm_response": " No, gaming revenue was up 11% from the previous quarter.", "gold_answer": "No, it was up 11% quarter-to-quarter and 22% year-to-year.", "prompt": "Did gaming revenue decrease?", "usage": {"input": 341, "metric": "tokens", "output": 15, "processing_time": 0.5634667873382568, "total": 356}, "category": "boolean_mc"} +{"number": 132, "llm_response": " Yes, 101% > 50%.", "gold_answer": "Yes, it increased 101% year-to-year.", "prompt": "Did revenue increase more than 50% year-to-year?", "usage": {"input": 478, "metric": "tokens", "output": 11, "processing_time": 0.43375658988952637, "total": 489}, "category": "boolean_mc"} +{"number": 133, "llm_response": " Yes, $3.38 billion.", "gold_answer": "Yes, $3.38 billion was returned", "prompt": "Was more than $3 billion returned to shareholders in the quarter?", "usage": {"input": 478, "metric": "tokens", "output": 10, "processing_time": 0.39887523651123047, "total": 488}, "category": "boolean_mc"} +{"number": 134, "llm_response": " No, revenue was down -2% from the previous quarter.", "gold_answer": "No, it decreased by 28%", "prompt": "Did Windows OEM revenue increase?", "usage": {"input": 281, "metric": "tokens", "output": 15, "processing_time": 0.5516996383666992, "total": 296}, "category": "boolean_mc"} +{"number": 135, "llm_response": " No, 3rd quarter net sales declined 20% year-on-year to 4.98 billion euros.", "gold_answer": "No, net sales declined 20% year-on-year, and the company has described the market environment as 'challenging.'", "prompt": "Are Nokia's products performing well in the market?", "usage": {"input": 119, "metric": "tokens", "output": 26, "processing_time": 0.8909690380096436, "total": 145}, "category": "boolean_mc"} +{"number": 136, "llm_response": " Yes, net sales declined 20% year-on-year to 4.98 billion euros.", "gold_answer": "Yes, net sales declined 20% year-on-year.", "prompt": "Is Nokia having trouble with sales?", "usage": {"input": 115, "metric": "tokens", "output": 22, "processing_time": 0.7686367034912109, "total": 137}, "category": "boolean_mc"} +{"number": 137, "llm_response": " No, exports to Asia have fallen for the ninth consecutive month.", "gold_answer": "No, exports to Asia decreased for the last nine months.", "prompt": "Are Japan's exports to Asia increasing?", "usage": {"input": 151, "metric": "tokens", "output": 14, "processing_time": 0.4967031478881836, "total": 165}, "category": "boolean_mc"} +{"number": 138, "llm_response": " No, exports in September decreased 16.3% compared to the same period last year.", "gold_answer": "Yes, by 4.3% year to year.", "prompt": "Are Japan's total exports increasing?", "usage": {"input": 150, "metric": "tokens", "output": 20, "processing_time": 0.6979436874389648, "total": 170}, "category": "boolean_mc"} +{"number": 139, "llm_response": " No.", "gold_answer": "No, the base salary is $200,000.", "prompt": "Is the executive's base salary $250,000?", "usage": {"input": 174, "metric": "tokens", "output": 3, "processing_time": 0.11354684829711914, "total": 177}, "category": "boolean_mc"} +{"number": 140, "llm_response": " $253 + $100 = $353 million.", "gold_answer": "$353 million", "prompt": "If automotive revenue increases by $100 million in third quarter, what will be the amount?", "usage": {"input": 115, "metric": "tokens", "output": 16, "processing_time": 0.5577638149261475, "total": 131}, "category": "math_basic"} +{"number": 141, "llm_response": " $16 per share.", "gold_answer": "$16", "prompt": "If the stock increases by $2, what is the new price?", "usage": {"input": 33, "metric": "tokens", "output": 7, "processing_time": 0.24306964874267578, "total": 40}, "category": "math_basic"} +{"number": 142, "llm_response": " $12 - $3 = $9.", "gold_answer": "$9", "prompt": "If the stock decreases by $3, what is the new price?", "usage": {"input": 40, "metric": "tokens", "output": 15, "processing_time": 0.5226762294769287, "total": 55}, "category": "math_basic"} +{"number": 143, "llm_response": " January 15, 2019.", "gold_answer": "January 15, 2019", "prompt": "What is the date when the loan will expire?", "usage": {"input": 40, "metric": "tokens", "output": 9, "processing_time": 0.3105580806732178, "total": 49}, "category": "math_basic"} +{"number": 144, "llm_response": " 37 + 8 = 45.", "gold_answer": "47 years old.", "prompt": "If the year is now 2022, how old is Bryan now?", "usage": {"input": 34, "metric": "tokens", "output": 12, "processing_time": 0.4123225212097168, "total": 46}, "category": "math_basic"} +{"number": 145, "llm_response": " $450,000.", "gold_answer": "$450,000", "prompt": "What is the amount of the executive's new salary?", "usage": {"input": 47, "metric": "tokens", "output": 7, "processing_time": 0.2441403865814209, "total": 54}, "category": "math_basic"} +{"number": 146, "llm_response": " January 15.", "gold_answer": "January 15", "prompt": "When is the last day to pay the invoice?", "usage": {"input": 41, "metric": "tokens", "output": 5, "processing_time": 0.17705249786376953, "total": 46}, "category": "math_basic"} +{"number": 147, "llm_response": " 2,000 shares.", "gold_answer": "2,000", "prompt": "How many shares traded on Wednesday?", "usage": {"input": 47, "metric": "tokens", "output": 7, "processing_time": 0.24461126327514648, "total": 54}, "category": "math_basic"} +{"number": 148, "llm_response": " $1200 + 50% = $1800.", "gold_answer": "$1800", "prompt": "If the rent increased by 50%, what is the new rental price?", "usage": {"input": 41, "metric": "tokens", "output": 17, "processing_time": 0.5855317115783691, "total": 58}, "category": "math_basic"} +{"number": 149, "llm_response": " 50% of the base salary amount.", "gold_answer": "$100,000", "prompt": "What is the amount of the target bonus?", "usage": {"input": 49, "metric": "tokens", "output": 10, "processing_time": 0.3453867435455322, "total": 59}, "category": "math_basic"} +{"number": 150, "llm_response": " 1. Francois, 29 years old.\r\n2. Liam, 41 years old.\r\n3. Myra, 23 years old.\r\n4. Ashish, 27 years old.\r\n5. Liam, 41 years old.", "gold_answer": "1. Liam - 41;\n2. Francois - 29;\n3. Ashish - 27;\n4. Myra - 23.", "prompt": "What is a list of the people ranked in order of age, from oldest to youngest?", "usage": {"input": 58, "metric": "tokens", "output": 63, "processing_time": 2.1673882007598877, "total": 121}, "category": "math_basic"} +{"number": 151, "llm_response": " Elizabeth is 35 years old.", "gold_answer": "John", "prompt": "Who is older - Elizabeth or John?", "usage": {"input": 32, "metric": "tokens", "output": 8, "processing_time": 0.27971816062927246, "total": 40}, "category": "math_basic"} +{"number": 152, "llm_response": " Chocolate.", "gold_answer": "Chocolate - $3.25", "prompt": "Which item has the lowest price?", "usage": {"input": 55, "metric": "tokens", "output": 3, "processing_time": 0.1068885326385498, "total": 58}, "category": "math_basic"} +{"number": 153, "llm_response": " Chocolate.", "gold_answer": "Cheese - $6.25", "prompt": "Which item has the highest price?", "usage": {"input": 55, "metric": "tokens", "output": 3, "processing_time": 0.10696220397949219, "total": 58}, "category": "math_basic"} +{"number": 154, "llm_response": " No.", "gold_answer": "No, chocolate has a lower price than coffee.", "prompt": "Does chocolate have a higher price than coffee?", "usage": {"input": 54, "metric": "tokens", "output": 3, "processing_time": 0.10918569564819336, "total": 57}, "category": "math_basic"} +{"number": 155, "llm_response": " S&P 500 futures - 39 points.", "gold_answer": "Nasdaq 100 futures, up 63 points.", "prompt": "Which stock index increased by the most points yesterday?", "usage": {"input": 87, "metric": "tokens", "output": 13, "processing_time": 0.4567725658416748, "total": 100}, "category": "math_basic"} +{"number": 156, "llm_response": " S&P 500 futures.", "gold_answer": "S&P 500 futures futures, up 1.2%.", "prompt": "Which stock index increased by the highest percentage yesterday?", "usage": {"input": 87, "metric": "tokens", "output": 8, "processing_time": 0.28204894065856934, "total": 95}, "category": "math_basic"} +{"number": 157, "llm_response": " Red Dog LLC - $61.", "gold_answer": "River Industries - $53.", "prompt": "Which stock has the highest price?", "usage": {"input": 94, "metric": "tokens", "output": 10, "processing_time": 0.34989190101623535, "total": 104}, "category": "math_basic"} +{"number": 158, "llm_response": " $19 + $2 = $21.", "gold_answer": "$21", "prompt": "If Smithson stock increases by $2, what is the new price?", "usage": {"input": 90, "metric": "tokens", "output": 15, "processing_time": 0.5215029716491699, "total": 105}, "category": "math_basic"} +{"number": 159, "llm_response": " PO 212 - $950,000", "gold_answer": "PO 1993", "prompt": "Which PO number has the smallest value?", "usage": {"input": 106, "metric": "tokens", "output": 11, "processing_time": 0.3892834186553955, "total": 117}, "category": "math_basic"} +{"number": 160, "llm_response": " (A) $52.9 billion.", "gold_answer": "(D) $28.5 billion", "prompt": "What was the amount of Cloud revenue in the quarter - (A) $52.9 billion, (B) $18.3 billion, (C) $17.5 billion, or (D) $28.5 billion?", "usage": {"input": 568, "metric": "tokens", "output": 12, "processing_time": 0.49440860748291016, "total": 580}, "category": "complex_qa"} +{"number": 161, "llm_response": " (A) Microsoft.", "gold_answer": "(B) TestCo Software.", "prompt": "What is the name of the company - (A) Microsoft, (B) TestCo Software, (C) Aphrodite Apollo, or (D) Executive Employment Agreement?", "usage": {"input": 200, "metric": "tokens", "output": 7, "processing_time": 0.2566261291503906, "total": 207}, "category": "complex_qa"} +{"number": 162, "llm_response": " (B) $7500.", "gold_answer": "(D) $10,000.", "prompt": "What is the price for the services of the Quality Assurance Manager - (A) $1,000, (B) $7500, (C) $22,500, or (D) $10,000?", "usage": {"input": 209, "metric": "tokens", "output": 10, "processing_time": 0.3550293445587158, "total": 219}, "category": "complex_qa"} +{"number": 163, "llm_response": " (B) 7%.", "gold_answer": "(C) 14%.", "prompt": "What was the percentage increase in diluted earnings per share in constant currency - (A) 10%, (B) 7%, (C) 14%, or (D) 15%?", "usage": {"input": 217, "metric": "tokens", "output": 8, "processing_time": 0.2974812984466553, "total": 225}, "category": "complex_qa"} +{"number": 164, "llm_response": " (A) 20%.", "gold_answer": "(B) 69%.", "prompt": "What is the rate of decline in profit - (A) 20%, (B) 69%, (C) 68%, (D) 32%, or (E) 133 million?", "usage": {"input": 155, "metric": "tokens", "output": 8, "processing_time": 0.2846097946166992, "total": 163}, "category": "complex_qa"} +{"number": 165, "llm_response": " \u20222019 - $53,823;\r\n\u20222020 - $31,536;\r\n\u20222021 - $31,536;\r\n\u20222022 - $81,462.", "gold_answer": "\u20222019 - 24,578;\n\u20222020 - 31,536;\n\u20222021 - 53,823;\n\u20222022 - 81,462.", "prompt": "What is a list of net sales in 2019, 2020, 2021, and 2022?", "usage": {"input": 632, "metric": "tokens", "output": 52, "processing_time": 1.876232624053955, "total": 684}, "category": "complex_qa"} +{"number": 166, "llm_response": " $297", "gold_answer": "297.", "prompt": "What is non-operating income interest in 2022?", "usage": {"input": 620, "metric": "tokens", "output": 4, "processing_time": 0.22362804412841797, "total": 624}, "category": "complex_qa"} +{"number": 167, "llm_response": " $13,832", "gold_answer": "6,496.", "prompt": "What is Operating Income in 2021?", "usage": {"input": 616, "metric": "tokens", "output": 6, "processing_time": 0.29172587394714355, "total": 622}, "category": "complex_qa"} +{"number": 168, "llm_response": " $31,536", "gold_answer": "18,402.", "prompt": "What is the cost of goods sold (excluding depreciation) in 2019?", "usage": {"input": 624, "metric": "tokens", "output": 6, "processing_time": 0.2913057804107666, "total": 630}, "category": "complex_qa"} +{"number": 169, "llm_response": " $13,719", "gold_answer": "13,719", "prompt": "What is the amount of pretax income in 2022?", "usage": {"input": 620, "metric": "tokens", "output": 6, "processing_time": 0.29204583168029785, "total": 626}, "category": "complex_qa"} +{"number": 170, "llm_response": " \u2022Herbert Simon,\r\n\u2022Marvin Minsky,\r\n\u2022James Lighthill,\r\n\u2022Sir James Lighthill,\r\n\u2022Papert,\r\n\u2022Minsky's and Papert's book Perceptrons,\r\n\u2022AI Winter,\r\n\u2022Expert Systems,\r\n\u2022Japan's fifth generation computer project,\r\n\u2022Lisp Machines, and\r\n\u2022Collapse of the Lisp Machines market in 1987.", "gold_answer": "\u2022Herbert Simon;\n\u2022Marvin Minsky;\n\u2022Sir James Lighthill;\n\u2022Papert.", "prompt": "What is a list of all of the people mentioned?", "usage": {"input": 390, "metric": "tokens", "output": 100, "processing_time": 3.493354082107544, "total": 490}, "category": "complex_qa"} +{"number": 171, "llm_response": " \u2022ServiceNow,\r\n\u2022Accenture,\r\n\u2022VMware,\r\n\u2022Snowflake,\r\n\u2022WPP,\r\n\u2022SoftBank,\r\n\u2022Hugging Face,\r\n\u2022NVIDIA AI Workbench,\r\n\u2022NVIDIA AI Enterprise 4.0, and\r\n\u2022NVIDIA's latest MLPerf training benchmarks with H100 GPUs.", "gold_answer": "\u2022Amazon Web Services;\n\u2022Microsoft Azure;\u2022\nRegional Cloud Service Providers;\n\u2022ServiceNow;\n\u2022Accenture;\n\u2022VMWare;\n\u2022Snowflake;\n\u2022WPP;\n\u2022Softbank;\n\u2022Hugging Face.", "prompt": "What is a list of Nvidia's partners mentioned in the text?", "usage": {"input": 529, "metric": "tokens", "output": 83, "processing_time": 2.937744617462158, "total": 612}, "category": "complex_qa"} +{"number": 172, "llm_response": " \u2022American Airlines,\r\n\u2022Fifth Third,\r\n\u2022Truist Financial,\r\n\u2022KeyCorp,\r\n\u2022Truist Financial,\r\n\u2022CSX,\r\n\u2022United Parcel Service,\r\n\u2022LSEG, formerly known as Refinitiv, and\r\n\u2022Netflix.", "gold_answer": "\u2022Tesla;\n;\u2022Netflix;\n\u2022KeyCorp;\n\u2022Fifth Third;\n\u2022Truist Financial;\n\u2022American Airlines;\n\u2022Union Pacific;\n\u2022CSX.", "prompt": "What is a list of the companies that are mentioned in the text?", "usage": {"input": 423, "metric": "tokens", "output": 69, "processing_time": 2.418790578842163, "total": 492}, "category": "complex_qa"} +{"number": 173, "llm_response": " 1. The U.S. government, \r\n2. The British government, and \r\n3. The Japanese government.", "gold_answer": "1. U.S;\n2. UK;\n3. Japan.", "prompt": "Which three governments are mentioned in the text?", "usage": {"input": 388, "metric": "tokens", "output": 33, "processing_time": 1.1925022602081299, "total": 421}, "category": "complex_qa"} +{"number": 174, "llm_response": " The intraday reversal occurred because of the softer wage number in the jobs report, which may have made investors rethink their earlier bearish stance.", "gold_answer": "Traders were unclear of the reason, but some noted it could be the softer wage numberin the jobs report.", "prompt": "Why did the intraday reversal occur?", "usage": {"input": 461, "metric": "tokens", "output": 29, "processing_time": 1.0519733428955078, "total": 490}, "category": "complex_qa"} +{"number": 175, "llm_response": " The company is reducing its cost base and increasing operation efficiency to \"address the challenging market environment.\"", "gold_answer": "Cost cutting plan", "prompt": "Why is Nokia cutting jobs?", "usage": {"input": 114, "metric": "tokens", "output": 21, "processing_time": 0.7282662391662598, "total": 135}, "category": "complex_qa"} +{"number": 176, "llm_response": " Tesla missed expectations on earnings and revenue in the third quarter.", "gold_answer": "The company missed expectations on earnings and revenue,and Elon Musk warned that the Cybertruck will not producepositive cash flow for more than a year.", "prompt": "Why did Tesla stock decline?", "usage": {"input": 415, "metric": "tokens", "output": 13, "processing_time": 0.5030436515808105, "total": 428}, "category": "complex_qa"} +{"number": 177, "llm_response": " \u2022January 5, 2021, Democratic Party won control of the United States Senate.\r\n\u2022President-elect Biden had supported and campaigned for both candidates prior to the runoff elections.\r\n\u2022Thousands of Trump supporters violently stormed the Capitol in the hope of overturning Biden's election.", "gold_answer": "The electoral victories in Georgia by Jon Ossoff and Raphael Warnock.", "prompt": "What events enabled the Democrats to win the Senate?", "usage": {"input": 315, "metric": "tokens", "output": 64, "processing_time": 2.2612857818603516, "total": 379}, "category": "complex_qa"} +{"number": 178, "llm_response": " North Industries.", "gold_answer": "North Industries", "prompt": "Which company is located in Nevada?", "usage": {"input": 101, "metric": "tokens", "output": 4, "processing_time": 0.14189910888671875, "total": 105}, "category": "complex_qa"} +{"number": 179, "llm_response": " Blue Moon Tech.", "gold_answer": "Blue Moon Tech.", "prompt": "Which company is located on California Street?", "usage": {"input": 102, "metric": "tokens", "output": 5, "processing_time": 0.17707037925720215, "total": 107}, "category": "complex_qa"} +{"number": 180, "llm_response": " \u2022Revenue is expected to be $16.00 billion, plus or minus 2%.\r\n\u2022GAAP and non-GAAP gross margins are expected to be 71.5% and 72.5%, respectively, plus or minus 50 basis points.\r\n\u2022GAAP and non-GAAP operating expenses are expected to be approximately $2.95 billion and $2.00 billion, respectively.\r\n\u2022GAAP and non-GAAP", "gold_answer": "\u2022Revenue: $16.00 billion, plus or minus 2%;\n\u2022Gross Margins: 71.5%-72.%, plus or minor 50 basis points;\n\u2022GAAP operating expenses: $2.95 billion;\n\u2022Non - GAAP operating expense: $2.00 billion.", "prompt": "What are the key financial metrics for the outlook for the third quarter?", "usage": {"input": 289, "metric": "tokens", "output": 101, "processing_time": 3.523019313812256, "total": 390}, "category": "summary"} +{"number": 181, "llm_response": " \u2022Data Center revenue was a record $10.32 billion, up 141% from the previous quarter and up 171% from a year ago;\r\n\u2022Announced that the NVIDIA\u00ae GH200 Grace\u2122 Hopper\u2122 Superchip for complex AI and HPC workloads is shipping this quarter, with a second-generation version with HBM3e memory expected to ship in Q2 of calendar 2024.", "gold_answer": "\u2022Second-quarter revenue: record $10.32 billion;\n\u2022Up 141% from previous quarter;\nUp 171% from previousyear", "prompt": "What is a list of key points about data center performance?", "usage": {"input": 287, "metric": "tokens", "output": 88, "processing_time": 3.079256057739258, "total": 375}, "category": "summary"} +{"number": 182, "llm_response": " \u2022Record revenue of $13.51 billion, up 88% from Q1, up 101% from year ago;\r\n\u2022Record data center revenue of $10.32 billion, up 141% from Q1, up 171% from year ago;\r\n\u2022GAAP earnings per diluted share for the quarter were $2.48, up 854% from a year ago and up 202% from the previous quarter.", "gold_answer": "1. Record revenue of $13.51 billion;\n2. Record Data Center revenue of $10.32 billion;\n3. GAAP earnings per diluted share of $2.48.", "prompt": "What is a list of the top 3 financial highlights for the quarter?", "usage": {"input": 479, "metric": "tokens", "output": 97, "processing_time": 3.38352108001709, "total": 576}, "category": "summary"} +{"number": 183, "llm_response": " \u2022Joe Biden's tenure as the 46th president of the United States began with his inauguration on January 20, 2021.\r\n\u2022Biden, a Democrat from Delaware, previously served as vice president under Barack Obama.\r\n\u2022He won the 2020 presidential election over Republican incumbent president Donald Trump.\r\n\u2022He is the oldest president in American history.", "gold_answer": "\u2022Joe Biden was inaugurated on January 20, 2021;\n\u2022Biden is the 46th President of the United States;\n\u2022Biden previously served as Vice President under Barack Obama;\n\u2022Biden won the 2020 presidential election over incumbent Donald Trump;\n\u2022Biden is the oldest president in American history.", "prompt": "What is a list of the top summary points?", "usage": {"input": 94, "metric": "tokens", "output": 80, "processing_time": 2.7603702545166016, "total": 174}, "category": "summary"} +{"number": 184, "llm_response": " 1. Biden was generally acknowledged as the winner, though General Services Administration head Emily W. Murphy initially refused to begin the transition to the president-elect.\r\n2. On November 23, after Michigan certified its results, Murphy issued the letter of ascertainment, granting the Biden transition team access to federal funds and resources for an orderly transition.\r\n3. Two days after becoming the projected winner of the 2020 election, Biden announced the formation of a task force to", "gold_answer": "1. General Services Administration head Emily W. Murphy initially refused to begin the transition for president-elect Biden until Michigan certified its results on November 23;\n2. Two days after becoming the projected winner, Biden announced the formation of a task forceto advise on the COVID-19 pandemic;\n3. On January 5, 2021, the Democratic Party won control of the U.S. Senate, with the electoralvictories in Georgia by Jon Ossoff and Raphael Warnock;\n4. On January 6, 2021, a mob of Trump supporters stormed the Capitol, forcing Congress to evacuateduring the counting of the Electoral Votes;\n5. For the inauguration, more than 26,000 National Guard members were deployed.", "prompt": "What is a list of the top 5 summary points?", "usage": {"input": 317, "metric": "tokens", "output": 101, "processing_time": 3.5230588912963867, "total": 418}, "category": "summary"} +{"number": 185, "llm_response": " \u2022Record revenue of $13.51 billion, up 88% from Q1, up 101% from year ago;\r\n\u2022Record data center revenue of $10.32 billion, up 141% from Q1, up 171% from year ago;\r\n\u2022GAAP earnings per diluted share for the quarter were $2.48, up 854% from a year ago and up 202% from the previous quarter;\r\n\u2022Non-", "gold_answer": "\u2022Revenue: $52.9 million, up 10% in constant currency;\n\u2022Operating income: $22.4 billion, up 15% in constant currency;\n\u2022Net income: $18.3 billion, up 14% in constant currency;\n\u2022Diluted earnings per share: $2.45 billion, up 14% in constant currency.", "prompt": "What is the list of the top financial highlights for the quarter?", "usage": {"input": 477, "metric": "tokens", "output": 101, "processing_time": 3.530743360519409, "total": 578}, "category": "summary"} +{"number": 186, "llm_response": " 1. The study of mechanical or 'formal' reasoning began with philosophers and mathematicians in antiquity.\r\n2. The study of logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as '0' and '1', could simulate both mathematical deduction and formal reasoning, which is known as the Church-Turing thesis.\r\n3. This, along with concurrent discoveries in cyber", "gold_answer": "1. The study of formal reasoning began in antiquity;\n2. These studies led directly to Alan Turing's theory of computation, which led researchers to consider the possibility of building an 'electronic brain';\n3. The first paper that recognized AI was published in 1943, and the field was founded in 1956.", "prompt": "What is a list of the top 3 points?", "usage": {"input": 181, "metric": "tokens", "output": 101, "processing_time": 3.4860851764678955, "total": 282}, "category": "summary"} +{"number": 187, "llm_response": " \u2022AI research in the 1960s was led by the \"AI 8\" and their students, who produced programs that the press described as \"astonishing\" and \"amazing.\"\r\n\u2022AI research was heavily funded by the U.S. Department of Defense, and laboratories were established around the world.\r\n\u2022AI research was funded by the commercial success of expert systems, which simulated the knowledge and analytical skills of human experts.\r\n\u2022AI research was", "gold_answer": "\u2022In the 1960s, AI researchers produced 'astonishing' programs that could learn checkers, word problems,prove logical theorems and speak English;\n\u2022AI research at the time was funded heavily by the U.S. Department of Defense;\n\u2022Herbert Simon predicted that machines would be capable in 20 years of doing human work;\n\u2022Marvin Minskey predicted that AI would be substantially solved within a generation;\n\u2022Due to these under-estimations of the difficulty of the problem, reduced government funding, and concerns about the real-world use of neural networks, the 'AI winter' followed;\n\u2022AI research was revived in the 1980s with expert systems, and the market for AI reached over $1 billion in 1985;\nHowever, with the collapse of the Lisp machine market in 1987, AI fell in disrepute again, and another AI winter ensued.", "prompt": "What is a list of the top summary points?", "usage": {"input": 389, "metric": "tokens", "output": 101, "processing_time": 3.5569868087768555, "total": 490}, "category": "summary"} +{"number": 188, "llm_response": " Wednesday - $27, Thursday - $33, Friday - $36.", "gold_answer": "\u2022Wednesday - $27;\n\u2022Thursday - $33;\n\u2022Friday - $36.", "prompt": "What is a list of the stock prices on Wednesday, Thursday and Friday?", "usage": {"input": 65, "metric": "tokens", "output": 22, "processing_time": 0.7597062587738037, "total": 87}, "category": "summary"} +{"number": 189, "llm_response": " Joe Biden's tenure as the 46th president of the United States began with his inauguration on January 20, 2021.", "gold_answer": "Joe Biden is the 46th President of the United States.", "prompt": "What is a headline summary in 10 words or less?", "usage": {"input": 96, "metric": "tokens", "output": 29, "processing_time": 1.0027666091918945, "total": 125}, "category": "summary"} +{"number": 190, "llm_response": " Biden was generally acknowledged as the winner.", "gold_answer": "After Biden won the election, he formed a pandemic task force, the democrats won the Senate, and a mob stormed the Capital on January 6.", "prompt": "What is a one-line brief description in 25 words or less?", "usage": {"input": 320, "metric": "tokens", "output": 9, "processing_time": 0.35122036933898926, "total": 329}, "category": "summary"} +{"number": 191, "llm_response": " Not Found.", "gold_answer": "\u2022Office Commercial Products;\n\u2022Office Consumer Products;\n\u2022Microsoft 365 Consumer;\n\u2022LinkedIn;\n\u2022Dynamics Products;\n\u2022Server Products;\n\u2022More Personal Computing;\n\u2022Windows OEM;\n\u2022Devices;\n\u2022Windows Commercial;\n\u2022Xbox;\n\u2022Search and News Advertising.", "prompt": "What is a list of top Microsoft product offerings?", "usage": {"input": 285, "metric": "tokens", "output": 4, "processing_time": 0.177656888961792, "total": 289}, "category": "summary"} +{"number": 192, "llm_response": " \u2022Invoice No. 234;\r\n\u2022ABC Company 8675 River Run Road, Marlington, CT 09392;\r\n\u2022Invoice to Delta Inc. 8723 Wilton Boulevard, Los Angeles, CA;\r\n\u2022LASER PRINTER;\r\n\u2022PAYMENT TERMS DUE DATE: May 3, 2022;\r\n\u2022QUANTITY DESCRIPTION: 30 - Box of Printer Paper;\r\n\u2022", "gold_answer": "\u2022Invoice Date: March 3, 2022;\\\u2022Invoice Number: No 234;\n\u2022From: ABC Company;\n\u2022To: Delta Inc.;\n\u2022Due Date: Due on Receipt;\n\u2022Total: $4565.00.", "prompt": "What is a list of the key points?", "usage": {"input": 171, "metric": "tokens", "output": 101, "processing_time": 3.486886739730835, "total": 272}, "category": "summary"} +{"number": 193, "llm_response": " Microsoft's AI models are coming together with the world's most universal user interface - natural language - to create a new era of computing.", "gold_answer": "According to Microsoft's CEO and CFO, the Microsoft Cloud is the platform for choice for AI,and growing 22% year-over-year as a result of focused execution.", "prompt": "What is a summary in 30 words or less?", "usage": {"input": 166, "metric": "tokens", "output": 32, "processing_time": 1.106576919555664, "total": 198}, "category": "summary"} +{"number": 194, "llm_response": " \u2022Emily W. Murphy, General Services Administration head;\r\n\u2022Biden Transition Team;\r\n\u2022Co-Chaired by Dr. Vivek Murthy, former Surgeon General, and David A. Kessler, former FDA Commissioner;\r\n\u2022Co-Chaired by Dr. Marcella Nunez-Smith, Yale University;\r\n\u2022Democratic Party wins control of the Senate on January 5, 2021.", "gold_answer": "\u2022Biden;\n\u2022Emily W. Murphy;\n\u2022Vivek Murthy;\n\u2022David A. Kessler;\n\u2022Marcella Nunez-Smith;\n\u2022Jon Ossoff;\n\u2022Raphael Warnock.", "prompt": "What is a list of all of the people mentioned?", "usage": {"input": 316, "metric": "tokens", "output": 92, "processing_time": 3.199162244796753, "total": 408}, "category": "summary"} +{"number": 195, "llm_response": " Nokia will reduce its cost base and increase operation efficiency to address the challenging market environment.", "gold_answer": "Nokia is cutting 14,000 jobs as part of a cost reductionplan after reporting profits that declined 69% year-on-year.", "prompt": "What is a one-line summary?", "usage": {"input": 116, "metric": "tokens", "output": 18, "processing_time": 0.6207818984985352, "total": 134}, "category": "summary"} +{"number": 196, "llm_response": " \u2022Senior Vice President, Event Planning;\r\n\u2022Represents Employer's Workforce Optimization Division;\r\n\u2022Reports to the Board of Directors;\r\n\u2022Devotes substantially all of his business time and attention to the operations of Employer and its subsidiaries;\r\n\u2022Responsible for running Employer's day-to-day operations;\r\n\u2022Performs faithfully, diligently and competently the duties and responsibilities of a SVP;\r\n\u2022Performs other duties and responsibilities as directed", "gold_answer": "\u2022Senior Vice President, Event Planning ('SVP') of Workforce Optimization Division;\n\u2022Report to Board of Directors;\n\u2022Devote substantially all of business time and attention to the operations of the Employer;\n\u2022responsible for running Employer's day-to-day operations;\n\u2022Perform faithfully, diligently and competently the duties and responsibilities of a SVP.", "prompt": "What is a list of the executive's responsibilities?", "usage": {"input": 345, "metric": "tokens", "output": 101, "processing_time": 3.482405424118042, "total": 446}, "category": "summary"} +{"number": 197, "llm_response": " NVIDIA announced financial results for the second quarter of fiscal 2024, reporting record revenue of $13.51 billion, up 88% from Q1, up 101% from the year ago.", "gold_answer": "NVIDIA Announces Second Quarter Fiscal 2024 Results with record revenue of $13.51 billion.", "prompt": "What is a headline description?", "usage": {"input": 470, "metric": "tokens", "output": 43, "processing_time": 1.5358905792236328, "total": 513}, "category": "summary"} +{"number": 198, "llm_response": " \"Microsoft is the platform of choice to help customers get the most value out of their digital spend and innovate for this next generation of AI.\"", "gold_answer": "Microsoft Cloud is the platform of choice for the next generation of AI.", "prompt": "What is a summary of the CEO's statement in 15 words or less?", "usage": {"input": 172, "metric": "tokens", "output": 30, "processing_time": 1.0394139289855957, "total": 202}, "category": "summary"} +{"number": 199, "llm_response": " \u2022Services Vendor Inc. \r\n\u2022100 Elm Street, Pleasantville, NY \r\n\u2022TO Alpha Inc., 5900 1st Street, Los Angeles, CA\r\n\u2022Description Front End Engineering Service $5000.00\r\n\u2022Back End Engineering Service $7500.00\r\n\u2022Quality Assurance Manager $10,000.00\r\n\u2022Total Amount $22,500.00\r\n\u2022Make all checks payable to Services Vendor Inc", "gold_answer": "\u2022Invoice From: Services Vendor Inc.;\n\u2022Invoice To: Alpha Inc.;\n\u2022Total Amount: $22,500.00;\n\u2022Payment Due: within 30 days;\n\u2022Contact: Bia Hermes;\n\u2022Invoice #0001;\u2022Data: 01/01/2022.", "prompt": "What are the key terms of the invoice?", "usage": {"input": 164, "metric": "tokens", "output": 101, "processing_time": 3.4716250896453857, "total": 265}, "category": "summary"} diff --git a/dragon-falcon-7b-core_test_1113_2.jsonl b/dragon-falcon-7b-core_test_1113_2.jsonl new file mode 100644 index 0000000..396512e --- /dev/null +++ b/dragon-falcon-7b-core_test_1113_2.jsonl @@ -0,0 +1,200 @@ +{"number": 0, "llm_response": " $22,500.00", "gold_answer": "$22,500.00", "prompt": "What is the total amount of the invoice?", "usage": {"input": 164, "metric": "tokens", "output": 8, "processing_time": 0.28378915786743164, "total": 172}, "category": "core"} +{"number": 1, "llm_response": " Invoice # 0001", "gold_answer": "#0001", "prompt": "What is the invoice number?", "usage": {"input": 161, "metric": "tokens", "output": 7, "processing_time": 0.2521779537200928, "total": 168}, "category": "core"} +{"number": 2, "llm_response": " \u2022Front End Engineering Service - $5000.00\r\n\u2022Back End Engineering Service - $7500.00\r\n\u2022Quality Assurance Manager - $10,000.00", "gold_answer": "\u2022Front End Engineering Service;\n\u2022Back End Engineering Service;\n\u2022Quality Assurance Manager", "prompt": "What is a list of the items being purchased?", "usage": {"input": 165, "metric": "tokens", "output": 45, "processing_time": 1.5513944625854492, "total": 210}, "category": "core"} +{"number": 3, "llm_response": " Bia Hermes", "gold_answer": "Bia Hermes", "prompt": "What is the name of the contact for question?", "usage": {"input": 165, "metric": "tokens", "output": 4, "processing_time": 0.1480846405029297, "total": 169}, "category": "core"} +{"number": 4, "llm_response": " #1000", "gold_answer": "#1000", "prompt": "What is the PO number?", "usage": {"input": 161, "metric": "tokens", "output": 5, "processing_time": 0.18198466300964355, "total": 166}, "category": "core"} +{"number": 5, "llm_response": " Within 30 days of the invoice date.", "gold_answer": "within 30 days of 01/01/2022", "prompt": "When is payment due?", "usage": {"input": 160, "metric": "tokens", "output": 10, "processing_time": 0.3518033027648926, "total": 170}, "category": "core"} +{"number": 6, "llm_response": " May 3, 2022", "gold_answer": "May 3, 2022", "prompt": "When is payment due?", "usage": {"input": 167, "metric": "tokens", "output": 8, "processing_time": 0.28484678268432617, "total": 175}, "category": "core"} +{"number": 7, "llm_response": " $4565.00", "gold_answer": "4150.00", "prompt": "What is the subtotal amount?", "usage": {"input": 169, "metric": "tokens", "output": 7, "processing_time": 0.24966883659362793, "total": 176}, "category": "core"} +{"number": 8, "llm_response": " $4565.00", "gold_answer": "4565.00", "prompt": "What is the total amount?", "usage": {"input": 168, "metric": "tokens", "output": 7, "processing_time": 0.24618101119995117, "total": 175}, "category": "core"} +{"number": 9, "llm_response": " \u2022Boxes of Printer Paper, 30, $30;\r\n\u2022Boxes of Printer Cartridge, 10, $75;\r\n\u2022Laser Printers, 5, $500;\r\n\u2022Laser Printer Cartridge, 10, $75.", "gold_answer": "\u202230 Boxes of Printer Paper;\n\u202210 Printer Cartridges;\n\u20225 Laser Printers.", "prompt": "What is a list of the purchased items?", "usage": {"input": 171, "metric": "tokens", "output": 62, "processing_time": 2.1377103328704834, "total": 233}, "category": "core"} +{"number": 10, "llm_response": " 234", "gold_answer": "234", "prompt": "What is the 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"category": "not_found_classification"} +{"number": 106, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "When was the A100 chip released?", "usage": {"input": 283, "metric": "tokens", "output": 4, "processing_time": 0.17591381072998047, "total": 287}, "category": "not_found_classification"} +{"number": 107, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "What was gaming revenue for the quarter?", "usage": {"input": 104, "metric": "tokens", "output": 4, "processing_time": 0.1419525146484375, "total": 108}, "category": "not_found_classification"} +{"number": 108, "llm_response": " $253 million", "gold_answer": "Not Found.", "prompt": "What was third quarter revenue?", "usage": {"input": 102, "metric": "tokens", "output": 5, "processing_time": 0.17686867713928223, "total": 107}, "category": "not_found_classification"} +{"number": 109, "llm_response": " Not Found.", "gold_answer": "Not Found", "prompt": "What is gaming revenue for the full year FY24?", "usage": {"input": 347, "metric": "tokens", "output": 4, "processing_time": 0.17765569686889648, "total": 351}, "category": "not_found_classification"} +{"number": 110, "llm_response": " Elon Musk", "gold_answer": "Not Found.", "prompt": "Who is the CEO of Netflix?", "usage": {"input": 416, "metric": "tokens", "output": 3, "processing_time": 0.1551823616027832, "total": 419}, "category": "not_found_classification"} +{"number": 111, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "What was China's trade balance in September?", "usage": {"input": 152, "metric": "tokens", "output": 4, "processing_time": 0.14712023735046387, "total": 156}, "category": "not_found_classification"} +{"number": 112, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "What was Japan's trade balance in March?", "usage": {"input": 152, "metric": "tokens", "output": 4, "processing_time": 0.14899420738220215, "total": 156}, "category": "not_found_classification"} +{"number": 113, "llm_response": " 3.2%", "gold_answer": "Not Found", "prompt": "What was the cost of living adjustment for 2023?", "usage": {"input": 675, "metric": "tokens", "output": 6, "processing_time": 0.3078269958496094, "total": 681}, "category": "not_found_classification"} +{"number": 114, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "When is TJX reporting earnings?", "usage": {"input": 670, "metric": "tokens", "output": 4, "processing_time": 0.23816943168640137, "total": 674}, "category": "not_found_classification"} +{"number": 115, "llm_response": " 100 laptops", "gold_answer": "Not Found.", "prompt": "How many were laptops were purchased?", "usage": {"input": 162, "metric": "tokens", "output": 4, "processing_time": 0.14439105987548828, "total": 166}, "category": "not_found_classification"} +{"number": 116, "llm_response": " Sonny Perdue", "gold_answer": "Not Found.", "prompt": "Who is the secretary of agriculture?", "usage": {"input": 91, "metric": "tokens", "output": 4, "processing_time": 0.1392068862915039, "total": 95}, "category": "not_found_classification"} +{"number": 117, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "What is the executive's salary?", "usage": {"input": 342, "metric": "tokens", "output": 4, "processing_time": 0.17825603485107422, "total": 346}, "category": "not_found_classification"} +{"number": 118, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "What is the 4th quarter revenue of Broadcom?", "usage": {"input": 476, "metric": "tokens", "output": 4, "processing_time": 0.19287967681884766, "total": 480}, "category": "not_found_classification"} +{"number": 119, "llm_response": " Not Found.", "gold_answer": "Not Found.", "prompt": "How old is Maribeth?", "usage": {"input": 39, "metric": "tokens", "output": 4, "processing_time": 0.14162731170654297, "total": 43}, "category": "not_found_classification"} +{"number": 120, "llm_response": " Yes, the S&P 500 has increased 22%.", "gold_answer": "Yes, by more than 22%.", "prompt": "Has the S&P increased over the last year?", "usage": {"input": 674, "metric": "tokens", "output": 14, "processing_time": 0.581068754196167, "total": 688}, "category": "boolean_mc"} +{"number": 121, "llm_response": " No, the company is citing sticky retail inflation for the fourth time in two years.", "gold_answer": "No, Chipotle is increasing prices.", "prompt": "Is Chipotle lowering prices?", "usage": {"input": 669, "metric": "tokens", "output": 18, "processing_time": 0.7189645767211914, "total": 687}, "category": "boolean_mc"} +{"number": 122, "llm_response": " No.", "gold_answer": "No, it is lowering the target from $38 to $36.", "prompt": "Is Barclay's raising its price target on KHC?", "usage": {"input": 676, "metric": "tokens", "output": 3, "processing_time": 0.20446252822875977, "total": 679}, "category": "boolean_mc"} +{"number": 123, "llm_response": " No, 4.98 billion euros < 5 billion euros.", "gold_answer": "No.", "prompt": "Were third quarter sales more than 5 billion euros?", "usage": {"input": 119, "metric": "tokens", "output": 16, "processing_time": 0.5588915348052979, "total": 135}, "category": "boolean_mc"} +{"number": 124, "llm_response": " Yes, 62.4 billion yen ($416.6 million) is a surplus, which is higher than the 42.5 billion yen ($0.00) that was the polled expectation.", "gold_answer": "Yes.", "prompt": "Did the trade surplus beat expectations?", "usage": {"input": 149, "metric": "tokens", "output": 44, "processing_time": 1.5181853771209717, "total": 193}, "category": "boolean_mc"} +{"number": 125, "llm_response": " No, Dow Jones futures decreased 73 points, or 0.2%.", "gold_answer": "No, down 73 points.", "prompt": "Did Dow Jones futures increase?", "usage": {"input": 415, "metric": "tokens", "output": 17, "processing_time": 0.6321268081665039, "total": 432}, "category": "boolean_mc"} +{"number": 126, "llm_response": " No, 4.9% is above 4.8%.", "gold_answer": "Yes, it topped 4.9%.", "prompt": "Is the yield on the 10-year above 4.8%?", "usage": {"input": 425, "metric": "tokens", "output": 15, "processing_time": 0.5680766105651855, "total": 440}, "category": "boolean_mc"} +{"number": 127, "llm_response": " No, the Nasdaq Composite underperformer, off 1.6%.", "gold_answer": "No, it was down 1.6%.", "prompt": "Did the Nasdaq Composite increase?", "usage": {"input": 415, "metric": "tokens", "output": 16, "processing_time": 0.599236011505127, "total": 431}, "category": "boolean_mc"} +{"number": 128, "llm_response": " No, automotive revenue was $253 million in the current quarter, down 15% from the previous quarter.", "gold_answer": "No, it was down 15% from the previous quarter.", "prompt": "Did automotive revenue increase in the current quarter compared to last quarter?", "usage": {"input": 109, "metric": "tokens", "output": 24, "processing_time": 0.8315634727478027, "total": 133}, "category": "boolean_mc"} +{"number": 129, "llm_response": " No, $253 million was down 15% from the previous quarter and up 15% from a year ago.", "gold_answer": "Yes, up 15%.", "prompt": "Did automotive revenue increase year-over-year?", "usage": {"input": 106, "metric": "tokens", "output": 26, "processing_time": 0.9038763046264648, "total": 132}, "category": "boolean_mc"} +{"number": 130, "llm_response": " No, $253 million < $250 million.", "gold_answer": "Yes", "prompt": "Was automotive revenue more than $250 million?", "usage": {"input": 106, "metric": "tokens", "output": 14, "processing_time": 0.4881265163421631, "total": 120}, "category": "boolean_mc"} +{"number": 131, "llm_response": " No, gaming revenue was up 11% from the previous quarter and up 22% from a year ago.", "gold_answer": "No, it was up 11% quarter-to-quarter and 22% year-to-year.", "prompt": "Did gaming revenue decrease?", "usage": {"input": 341, "metric": "tokens", "output": 24, "processing_time": 0.8606479167938232, "total": 365}, "category": "boolean_mc"} +{"number": 132, "llm_response": " Yes, 101% > 50%.", "gold_answer": "Yes, it increased 101% year-to-year.", "prompt": "Did revenue increase more than 50% year-to-year?", "usage": {"input": 478, "metric": "tokens", "output": 11, "processing_time": 0.4343266487121582, "total": 489}, "category": "boolean_mc"} +{"number": 133, "llm_response": " Yes, $3.38 billion.", "gold_answer": "Yes, $3.38 billion was returned", "prompt": "Was more than $3 billion returned to shareholders in the quarter?", "usage": {"input": 478, "metric": "tokens", "output": 10, "processing_time": 0.39653730392456055, "total": 488}, "category": "boolean_mc"} +{"number": 134, "llm_response": " No, it decreased by -1%.", "gold_answer": "No, it decreased by 28%", "prompt": "Did Windows OEM revenue increase?", "usage": {"input": 281, "metric": "tokens", "output": 10, "processing_time": 0.3787999153137207, "total": 291}, "category": "boolean_mc"} +{"number": 135, "llm_response": " No, Nokia's products are performing poorly in the market.", "gold_answer": "No, net sales declined 20% year-on-year, and the company has described the market environment as 'challenging.'", "prompt": "Are Nokia's products performing well in the market?", "usage": {"input": 119, "metric": "tokens", "output": 14, "processing_time": 0.49024152755737305, "total": 133}, "category": "boolean_mc"} +{"number": 136, "llm_response": " Yes, 3rd quarter net sales declined 20% year-on-year to 4.98 billion euros.", "gold_answer": "Yes, net sales declined 20% year-on-year.", "prompt": "Is Nokia having trouble with sales?", "usage": {"input": 115, "metric": "tokens", "output": 26, "processing_time": 0.9078512191772461, "total": 141}, "category": "boolean_mc"} +{"number": 137, "llm_response": " No.", "gold_answer": "No, exports to Asia decreased for the last nine months.", "prompt": "Are Japan's exports to Asia increasing?", "usage": {"input": 151, "metric": "tokens", "output": 3, "processing_time": 0.1121358871459961, "total": 154}, "category": "boolean_mc"} +{"number": 138, "llm_response": " No, exports decreased 16.3% year on year.", "gold_answer": "Yes, by 4.3% year to year.", "prompt": "Are Japan's total exports increasing?", "usage": {"input": 150, "metric": "tokens", "output": 14, "processing_time": 0.4900975227355957, "total": 164}, "category": "boolean_mc"} +{"number": 139, "llm_response": " No.", "gold_answer": "No, the base salary is $200,000.", "prompt": "Is the executive's base salary $250,000?", "usage": {"input": 174, "metric": "tokens", "output": 3, "processing_time": 0.11349797248840332, "total": 177}, "category": "boolean_mc"} +{"number": 140, "llm_response": " $253 million + $100 million = $353 million.", "gold_answer": "$353 million", "prompt": "If automotive revenue increases by $100 million in third quarter, what will be the amount?", "usage": {"input": 115, "metric": "tokens", "output": 18, "processing_time": 0.6265013217926025, "total": 133}, "category": "math_basic"} +{"number": 141, "llm_response": " $16 per share.", "gold_answer": "$16", "prompt": "If the stock increases by $2, what is the new price?", "usage": {"input": 33, "metric": "tokens", "output": 7, "processing_time": 0.24665427207946777, "total": 40}, "category": "math_basic"} +{"number": 142, "llm_response": " $12 - $3 = $9.", "gold_answer": "$9", "prompt": "If the stock decreases by $3, what is the new price?", "usage": {"input": 40, "metric": "tokens", "output": 15, "processing_time": 0.5209736824035645, "total": 55}, "category": "math_basic"} +{"number": 143, "llm_response": " January 15, 2019.", "gold_answer": "January 15, 2019", "prompt": "What is the date when the loan will expire?", "usage": {"input": 40, "metric": "tokens", "output": 9, "processing_time": 0.3117506504058838, "total": 49}, "category": "math_basic"} +{"number": 144, "llm_response": " 37 + 6 = 43.", "gold_answer": "47 years old.", "prompt": "If the year is now 2022, how old is Bryan now?", "usage": {"input": 34, "metric": "tokens", "output": 12, "processing_time": 0.41188645362854004, "total": 46}, "category": "math_basic"} +{"number": 145, "llm_response": " $450,000.", "gold_answer": "$450,000", "prompt": "What is the amount of the executive's new salary?", "usage": {"input": 47, "metric": "tokens", "output": 7, "processing_time": 0.24466562271118164, "total": 54}, "category": "math_basic"} +{"number": 146, "llm_response": " 3 days after January 12, which is January 15.", "gold_answer": "January 15", "prompt": "When is the last day to pay the invoice?", "usage": {"input": 41, "metric": "tokens", "output": 15, "processing_time": 0.5246376991271973, "total": 56}, "category": "math_basic"} +{"number": 147, "llm_response": " 2,000 shares.", "gold_answer": "2,000", "prompt": "How many shares traded on Wednesday?", "usage": {"input": 47, "metric": "tokens", "output": 7, "processing_time": 0.24625015258789062, "total": 54}, "category": "math_basic"} +{"number": 148, "llm_response": " $1200 + 50% = $1800.", "gold_answer": "$1800", "prompt": "If the rent increased by 50%, what is the new rental price?", "usage": {"input": 41, "metric": "tokens", "output": 17, "processing_time": 0.591808557510376, "total": 58}, "category": "math_basic"} +{"number": 149, "llm_response": " 50% of the base salary amount.", "gold_answer": "$100,000", "prompt": "What is the amount of the target bonus?", "usage": {"input": 49, "metric": "tokens", "output": 10, "processing_time": 0.35002732276916504, "total": 59}, "category": "math_basic"} +{"number": 150, "llm_response": " \u2022Liam - 41 years old\r\n\u2022Ashish - 27 years old\r\n\u2022Francois - 29 years old\r\n\u2022Myra - 23 years old\r\n\u2022Ashish - 27 years old", "gold_answer": "1. Liam - 41;\n2. Francois - 29;\n3. Ashish - 27;\n4. Myra - 23.", "prompt": "What is a list of the people ranked in order of age, from oldest to youngest?", "usage": {"input": 58, "metric": "tokens", "output": 55, "processing_time": 1.8839166164398193, "total": 113}, "category": "math_basic"} +{"number": 151, "llm_response": " Elizabeth is 35 years old.", "gold_answer": "John", "prompt": "Who is older - Elizabeth or John?", "usage": {"input": 32, "metric": "tokens", "output": 8, "processing_time": 0.27673792839050293, "total": 40}, "category": "math_basic"} +{"number": 152, "llm_response": " Chocolate.", "gold_answer": "Chocolate - $3.25", "prompt": "Which item has the lowest price?", "usage": {"input": 55, "metric": "tokens", "output": 3, "processing_time": 0.10579919815063477, "total": 58}, "category": "math_basic"} +{"number": 153, "llm_response": " Chocolate.", "gold_answer": "Cheese - $6.25", "prompt": "Which item has the highest price?", "usage": {"input": 55, "metric": "tokens", "output": 3, "processing_time": 0.10578513145446777, "total": 58}, "category": "math_basic"} +{"number": 154, "llm_response": " No.", "gold_answer": "No, chocolate has a lower price than coffee.", "prompt": "Does chocolate have a higher price than coffee?", "usage": {"input": 54, "metric": "tokens", "output": 3, "processing_time": 0.10898256301879883, "total": 57}, "category": "math_basic"} +{"number": 155, "llm_response": " S&P 500 futures added 39 points, or 1.2%.", "gold_answer": "Nasdaq 100 futures, up 63 points.", "prompt": "Which stock index increased by the most points yesterday?", "usage": {"input": 87, "metric": "tokens", "output": 18, "processing_time": 0.6205909252166748, "total": 105}, "category": "math_basic"} +{"number": 156, "llm_response": " S&P 500.", "gold_answer": "S&P 500 futures futures, up 1.2%.", "prompt": "Which stock index increased by the highest percentage yesterday?", "usage": {"input": 87, "metric": "tokens", "output": 7, "processing_time": 0.24467062950134277, "total": 94}, "category": "math_basic"} +{"number": 157, "llm_response": " Red Dog LLC - $61.", "gold_answer": "River Industries - $53.", "prompt": "Which stock has the highest price?", "usage": {"input": 94, "metric": "tokens", "output": 10, "processing_time": 0.3546571731567383, "total": 104}, "category": "math_basic"} +{"number": 158, "llm_response": " $19 + $2 = $21.", "gold_answer": "$21", "prompt": "If Smithson stock increases by $2, what is the new price?", "usage": {"input": 90, "metric": "tokens", "output": 15, "processing_time": 0.5172219276428223, "total": 105}, "category": "math_basic"} +{"number": 159, "llm_response": " PO 212 - $950,000.", "gold_answer": "PO 1993", "prompt": "Which PO number has the smallest value?", "usage": {"input": 106, "metric": "tokens", "output": 12, "processing_time": 0.4183676242828369, "total": 118}, "category": "math_basic"} +{"number": 160, "llm_response": " (A) $52.9 billion.", "gold_answer": "(D) $28.5 billion", "prompt": "What was the amount of Cloud revenue in the quarter - (A) $52.9 billion, (B) $18.3 billion, (C) $17.5 billion, or (D) $28.5 billion?", "usage": {"input": 568, "metric": "tokens", "output": 12, "processing_time": 0.4944193363189697, "total": 580}, "category": "complex_qa"} +{"number": 161, "llm_response": " (A) Microsoft.", "gold_answer": "(B) TestCo Software.", "prompt": "What is the name of the company - (A) Microsoft, (B) TestCo Software, (C) Aphrodite Apollo, or (D) Executive Employment Agreement?", "usage": {"input": 200, "metric": "tokens", "output": 7, "processing_time": 0.25288939476013184, "total": 207}, "category": "complex_qa"} +{"number": 162, "llm_response": " (B) $7500.", "gold_answer": "(D) $10,000.", "prompt": "What is the price for the services of the Quality Assurance Manager - (A) $1,000, (B) $7500, (C) $22,500, or (D) $10,000?", "usage": {"input": 209, "metric": "tokens", "output": 10, "processing_time": 0.35381293296813965, "total": 219}, "category": "complex_qa"} +{"number": 163, "llm_response": " (C) 14%.", "gold_answer": "(C) 14%.", "prompt": "What was the percentage increase in diluted earnings per share in constant currency - (A) 10%, (B) 7%, (C) 14%, or (D) 15%?", "usage": {"input": 217, "metric": "tokens", "output": 8, "processing_time": 0.2901015281677246, "total": 225}, "category": "complex_qa"} +{"number": 164, "llm_response": " (A) 20%.", "gold_answer": "(B) 69%.", "prompt": "What is the rate of decline in profit - (A) 20%, (B) 69%, (C) 68%, (D) 32%, or (E) 133 million?", "usage": {"input": 155, "metric": "tokens", "output": 8, "processing_time": 0.2891519069671631, "total": 163}, "category": "complex_qa"} +{"number": 165, "llm_response": " \u20222019 - $53,823\r\n\u20222020 - $31,536\r\n\u20222021 - $31,536\r\n\u20222022 - $81,462", "gold_answer": "\u20222019 - 24,578;\n\u20222020 - 31,536;\n\u20222021 - 53,823;\n\u20222022 - 81,462.", "prompt": "What is a list of net sales in 2019, 2020, 2021, and 2022?", "usage": {"input": 632, "metric": "tokens", "output": 48, "processing_time": 1.7377607822418213, "total": 680}, "category": "complex_qa"} +{"number": 166, "llm_response": " $297", "gold_answer": "297.", "prompt": "What is non-operating income interest in 2022?", "usage": {"input": 620, "metric": "tokens", "output": 4, "processing_time": 0.2221541404724121, "total": 624}, "category": "complex_qa"} +{"number": 167, "llm_response": " $13,832", "gold_answer": "6,496.", "prompt": "What is Operating Income in 2021?", "usage": {"input": 616, "metric": "tokens", "output": 6, "processing_time": 0.2906038761138916, "total": 622}, "category": "complex_qa"} +{"number": 168, "llm_response": " $24,578", "gold_answer": "18,402.", "prompt": "What is the cost of goods sold (excluding depreciation) in 2019?", "usage": {"input": 624, "metric": "tokens", "output": 6, "processing_time": 0.28990793228149414, "total": 630}, "category": "complex_qa"} +{"number": 169, "llm_response": " $13,719", "gold_answer": "13,719", "prompt": "What is the amount of pretax income in 2022?", "usage": {"input": 620, "metric": "tokens", "output": 6, "processing_time": 0.2910003662109375, "total": 626}, "category": "complex_qa"} +{"number": 170, "llm_response": " \u2022Herbert Simon,\r\n\u2022Marvin Minsky,\r\n\u2022James Lighthill,\r\n\u2022Sir James Lighthill,\r\n\u2022Papert,\r\n\u2022Minsky's book Perceptrons,\r\n\u2022AI Winter,\r\n\u2022AI Research,\r\n\u2022Expert Systems,\r\n\u2022AI Market,\r\n\u2022Lisp Machines,\r\n\u2022Lisp Machine market,\r\n\u2022Collapse of the Lisp Machines market, and\r\n", "gold_answer": "\u2022Herbert Simon;\n\u2022Marvin Minsky;\n\u2022Sir James Lighthill;\n\u2022Papert.", "prompt": "What is a list of all of the people mentioned?", "usage": {"input": 390, "metric": "tokens", "output": 101, "processing_time": 3.5229432582855225, "total": 491}, "category": "complex_qa"} +{"number": 171, "llm_response": " \u2022ServiceNow and Accenture, to develop AI Lighthouse, a first-of-its-kind program to fast-track the development and adoption of enterprise generative AI capabilities.\r\n\u2022VMware, to extend the companies' strategic partnership to ready enterprises running VMware's cloud infrastructure for the era of generative AI.\r\n\u2022Snowflake, to provide businesses with an accelerated path to create customized generative AI applications using their own proprietary data.\r\n\u2022WPP, to develop", "gold_answer": "\u2022Amazon Web Services;\n\u2022Microsoft Azure;\u2022\nRegional Cloud Service Providers;\n\u2022ServiceNow;\n\u2022Accenture;\n\u2022VMWare;\n\u2022Snowflake;\n\u2022WPP;\n\u2022Softbank;\n\u2022Hugging Face.", "prompt": "What is a list of Nvidia's partners mentioned in the text?", "usage": {"input": 529, "metric": "tokens", "output": 101, "processing_time": 3.5827550888061523, "total": 630}, "category": "complex_qa"} +{"number": 172, "llm_response": " \u2022American Airlines,\r\n\u2022Fifth Third,\r\n\u2022Truist Financial,\r\n\u2022CSX,\r\n\u2022KeyCorp,\r\n\u2022Truist Financial,\r\n\u2022Fifth Third,\r\n\u2022Truist Financial,\r\n\u2022CSX,\r\n\u2022American Airlines,\r\n\u2022LSEG, formerly known as Refinitiv.", "gold_answer": "\u2022Tesla;\n;\u2022Netflix;\n\u2022KeyCorp;\n\u2022Fifth Third;\n\u2022Truist Financial;\n\u2022American Airlines;\n\u2022Union Pacific;\n\u2022CSX.", "prompt": "What is a list of the companies that are mentioned in the text?", "usage": {"input": 423, "metric": "tokens", "output": 82, "processing_time": 2.89247989654541, "total": 505}, "category": "complex_qa"} +{"number": 173, "llm_response": " 1. The U.S. government, \r\n2. The British government, and \r\n3. The Japanese government.", "gold_answer": "1. U.S;\n2. UK;\n3. Japan.", "prompt": "Which three governments are mentioned in the text?", "usage": {"input": 388, "metric": "tokens", "output": 33, "processing_time": 1.183218240737915, "total": 421}, "category": "complex_qa"} +{"number": 174, "llm_response": " \u2022Stocks initially fell on the stronger-than-expected jobs report.\r\n\u2022At its session low, the Dow had fallen as much as 198 points.\r\n\u2022The Nasdaq and the S&P 500 slid by 0.8% during their lowest points in the day.\r\n\u2022Traders were unclear of the reason for the intraday reversal.", "gold_answer": "Traders were unclear of the reason, but some noted it could be the softer wage numberin the jobs report.", "prompt": "Why did the intraday reversal occur?", "usage": {"input": 461, "metric": "tokens", "output": 80, "processing_time": 2.8127105236053467, "total": 541}, "category": "complex_qa"} +{"number": 175, "llm_response": " To address the challenging market environment.", "gold_answer": "Cost cutting plan", "prompt": "Why is Nokia cutting jobs?", "usage": {"input": 114, "metric": "tokens", "output": 8, "processing_time": 0.2772829532623291, "total": 122}, "category": "complex_qa"} +{"number": 176, "llm_response": " Tesla missed expectations on earnings and revenue in the third quarter.", "gold_answer": "The company missed expectations on earnings and revenue,and Elon Musk warned that the Cybertruck will not producepositive cash flow for more than a year.", "prompt": "Why did Tesla stock decline?", "usage": {"input": 415, "metric": "tokens", "output": 13, "processing_time": 0.5040414333343506, "total": 428}, "category": "complex_qa"} +{"number": 177, "llm_response": " \u2022Democratic Party won control of the United States Senate, effective January 5, 2021, as a result of electoral victories in Georgia.\r\n\u2022President-elect Biden had supported and campaigned for both candidates prior to the runoff elections on January 5.\r\n\u2022Trump supporters violently stormed the Capitol in the hope of overturning Biden's election, forcing Congress to evacuate during the counting of the electoral college votes.", "gold_answer": "The electoral victories in Georgia by Jon Ossoff and Raphael Warnock.", "prompt": "What events enabled the Democrats to win the Senate?", "usage": {"input": 315, "metric": "tokens", "output": 91, "processing_time": 3.151782512664795, "total": 406}, "category": "complex_qa"} +{"number": 178, "llm_response": " Red River Industries", "gold_answer": "North Industries", "prompt": "Which company is located in Nevada?", "usage": {"input": 101, "metric": "tokens", "output": 4, "processing_time": 0.1425187587738037, "total": 105}, "category": "complex_qa"} +{"number": 179, "llm_response": " Blue Moon Tech", "gold_answer": "Blue Moon Tech.", "prompt": "Which company is located on California Street?", "usage": {"input": 102, "metric": "tokens", "output": 4, "processing_time": 0.14140081405639648, "total": 106}, "category": "complex_qa"} +{"number": 180, "llm_response": " \u2022Revenue is expected to be $16.00 billion, plus or minus 2%;\r\n\u2022GAAP and non-GAAP gross margins are expected to be 71.5% and 72.5%, respectively, plus or minus 50 basis points;\r\n\u2022GAAP and non-GAAP operating expenses are expected to be approximately $2.95 billion and $2.00 billion, respectively;\r\n\u2022GAAP and non-GAAP", "gold_answer": "\u2022Revenue: $16.00 billion, plus or minus 2%;\n\u2022Gross Margins: 71.5%-72.%, plus or minor 50 basis points;\n\u2022GAAP operating expenses: $2.95 billion;\n\u2022Non - GAAP operating expense: $2.00 billion.", "prompt": "What are the key financial metrics for the outlook for the third quarter?", "usage": {"input": 289, "metric": "tokens", "output": 101, "processing_time": 3.5331945419311523, "total": 390}, "category": "summary"} +{"number": 181, "llm_response": " \u2022Data Center revenue was a record $10.32 billion, up 141% from the previous quarter and up 171% from a year ago.\r\n\u2022Announced that the NVIDIA Grace Hopper Superchip for complex AI and HPC workloads is shipping this quarter, with a second-generation version with HBM3e memory expected to ship in Q2 of calendar 2024.", "gold_answer": "\u2022Second-quarter revenue: record $10.32 billion;\n\u2022Up 141% from previous quarter;\nUp 171% from previousyear", "prompt": "What is a list of key points about data center performance?", "usage": {"input": 287, "metric": "tokens", "output": 83, "processing_time": 2.8864870071411133, "total": 370}, "category": "summary"} +{"number": 182, "llm_response": " 1. Record revenue of $13.51 billion, up 88% from Q1, up 101% from year ago\r\n2. Record data center revenue of $10.32 billion, up 141% from Q1, up 171% from year ago.\r\n3. GAAP EPS of $2.48, up 854% from a year ago, and up 202% from the previous quarter.", "gold_answer": "1. Record revenue of $13.51 billion;\n2. Record Data Center revenue of $10.32 billion;\n3. GAAP earnings per diluted share of $2.48.", "prompt": "What is a list of the top 3 financial highlights for the quarter?", "usage": {"input": 479, "metric": "tokens", "output": 97, "processing_time": 3.416253089904785, "total": 576}, "category": "summary"} +{"number": 183, "llm_response": " \u2022Joe Biden's tenure as the 46th president of the United States began with his inauguration on January 20, 2021.\r\n\u2022Biden, a Democrat from Delaware, previously served as vice president under Barack Obama.\r\n\u2022He defeated Republican incumbent president Donald Trump in the 2020 presidential election.\r\n\u2022Upon his inauguration, he became the oldest president in American history.", "gold_answer": "\u2022Joe Biden was inaugurated on January 20, 2021;\n\u2022Biden is the 46th President of the United States;\n\u2022Biden previously served as Vice President under Barack Obama;\n\u2022Biden won the 2020 presidential election over incumbent Donald Trump;\n\u2022Biden is the oldest president in American history.", "prompt": "What is a list of the top summary points?", "usage": {"input": 94, "metric": "tokens", "output": 84, "processing_time": 2.891251802444458, "total": 178}, "category": "summary"} +{"number": 184, "llm_response": " 1. Biden was generally acknowledged as the winner of the 2020 election, though there was a delay in the transition to the president-elect.\r\n2. Biden was the projected winner of the 2020 election on November 23, 2020, and announced the formation of a task force to advise him on the COVID-19 pandemic during the transition.\r\n3. On January 5, 2021, the Democratic Party won control of the United States", "gold_answer": "1. General Services Administration head Emily W. Murphy initially refused to begin the transition for president-elect Biden until Michigan certified its results on November 23;\n2. Two days after becoming the projected winner, Biden announced the formation of a task forceto advise on the COVID-19 pandemic;\n3. On January 5, 2021, the Democratic Party won control of the U.S. Senate, with the electoralvictories in Georgia by Jon Ossoff and Raphael Warnock;\n4. On January 6, 2021, a mob of Trump supporters stormed the Capitol, forcing Congress to evacuateduring the counting of the Electoral Votes;\n5. For the inauguration, more than 26,000 National Guard members were deployed.", "prompt": "What is a list of the top 5 summary points?", "usage": {"input": 317, "metric": "tokens", "output": 101, "processing_time": 3.513174057006836, "total": 418}, "category": "summary"} +{"number": 185, "llm_response": " \u2022Record revenue of $13.51 billion, up 88% from Q1, up 101% from year ago;\r\n\u2022Record data center revenue of $10.32 billion, up 141% from Q1, up 171% from year ago;\r\n\u2022GAAP earnings per diluted share for the quarter were $2.48, up 854% from a year ago and up 202% from the previous quarter;\r\n\u2022Non-", "gold_answer": "\u2022Revenue: $52.9 million, up 10% in constant currency;\n\u2022Operating income: $22.4 billion, up 15% in constant currency;\n\u2022Net income: $18.3 billion, up 14% in constant currency;\n\u2022Diluted earnings per share: $2.45 billion, up 14% in constant currency.", "prompt": "What is the list of the top financial highlights for the quarter?", "usage": {"input": 477, "metric": "tokens", "output": 101, "processing_time": 3.5379128456115723, "total": 578}, "category": "summary"} +{"number": 186, "llm_response": " 1. The study of mechanical or formal reasoning began with philosophers and mathematicians in antiquity.\r\n2. The study of logic led directly to Alan Turing's theory of computation.\r\n3. The Church-Turing thesis, which states that a machine can simulate both mathematical deduction and formal reasoning.", "gold_answer": "1. The study of formal reasoning began in antiquity;\n2. These studies led directly to Alan Turing's theory of computation, which led researchers to consider the possibility of building an 'electronic brain';\n3. The first paper that recognized AI was published in 1943, and the field was founded in 1956.", "prompt": "What is a list of the top 3 points?", "usage": {"input": 181, "metric": "tokens", "output": 69, "processing_time": 2.39233136177063, "total": 250}, "category": "summary"} +{"number": 187, "llm_response": " \u2022In the 1960s, the U.S. government and the British government cut off exploratory research in response to the criticism of Sir James Lighthill and ongoing pressure from the U.S. Congress to fund more productive projects.\r\n\u2022Minsky and Papert's book Perceptrons was understood as proving that artificial neural networks approach would never be useful for solving real-world tasks, thus discrediting the approach.\r\n\u2022In the early 1980s,", "gold_answer": "\u2022In the 1960s, AI researchers produced 'astonishing' programs that could learn checkers, word problems,prove logical theorems and speak English;\n\u2022AI research at the time was funded heavily by the U.S. Department of Defense;\n\u2022Herbert Simon predicted that machines would be capable in 20 years of doing human work;\n\u2022Marvin Minskey predicted that AI would be substantially solved within a generation;\n\u2022Due to these under-estimations of the difficulty of the problem, reduced government funding, and concerns about the real-world use of neural networks, the 'AI winter' followed;\n\u2022AI research was revived in the 1980s with expert systems, and the market for AI reached over $1 billion in 1985;\nHowever, with the collapse of the Lisp machine market in 1987, AI fell in disrepute again, and another AI winter ensued.", "prompt": "What is a list of the top summary points?", "usage": {"input": 389, "metric": "tokens", "output": 101, "processing_time": 3.531312942504883, "total": 490}, "category": "summary"} +{"number": 188, "llm_response": " Wednesday - $27, Thursday - $33, Friday - $36.", "gold_answer": "\u2022Wednesday - $27;\n\u2022Thursday - $33;\n\u2022Friday - $36.", "prompt": "What is a list of the stock prices on Wednesday, Thursday and Friday?", "usage": {"input": 65, "metric": "tokens", "output": 22, "processing_time": 0.7591512203216553, "total": 87}, "category": "summary"} +{"number": 189, "llm_response": " Joe Biden's tenure as the 46th president of the United States began with his inauguration on January 20, 2021.", "gold_answer": "Joe Biden is the 46th President of the United States.", "prompt": "What is a headline summary in 10 words or less?", "usage": {"input": 96, "metric": "tokens", "output": 29, "processing_time": 1.0076935291290283, "total": 125}, "category": "summary"} +{"number": 190, "llm_response": " Biden was generally acknowledged as the winner, but General Services Administration head Emily W. Murphy refused to begin the transition to the president-elect.", "gold_answer": "After Biden won the election, he formed a pandemic task force, the democrats won the Senate, and a mob stormed the Capital on January 6.", "prompt": "What is a one-line brief description in 25 words or less?", "usage": {"input": 320, "metric": "tokens", "output": 29, "processing_time": 1.0358037948608398, "total": 349}, "category": "summary"} +{"number": 191, "llm_response": " \u2022Microsoft Teams;\r\n\u2022Microsoft Azure;\r\n\u2022Microsoft Dynamics;\r\n\u2022Microsoft 365;\r\n\u2022Microsoft Windows;\r\n\u2022Microsoft Office;\r\n\u2022Microsoft Edge;\r\n\u2022Microsoft Teams;\r\n\u2022Microsoft Azure;\r\n\u2022Microsoft Dynamics;\r\n\u2022Microsoft 365;\r\n\u2022Microsoft Windows;\r\n\u2022Microsoft Office;\r\n\u2022Microsoft Edge;\r\n\u2022Microsoft Teams;\r\n\u2022Microsoft Azure;\r\n\u2022Microsoft", "gold_answer": "\u2022Office Commercial Products;\n\u2022Office Consumer Products;\n\u2022Microsoft 365 Consumer;\n\u2022LinkedIn;\n\u2022Dynamics Products;\n\u2022Server Products;\n\u2022More Personal Computing;\n\u2022Windows OEM;\n\u2022Devices;\n\u2022Windows Commercial;\n\u2022Xbox;\n\u2022Search and News Advertising.", "prompt": "What is a list of top Microsoft product offerings?", "usage": {"input": 285, "metric": "tokens", "output": 101, "processing_time": 3.5102076530456543, "total": 386}, "category": "summary"} +{"number": 192, "llm_response": " \u2022Invoice date: March 3, 2022;\r\n\u2022Invoice number: 234;\r\n\u2022Invoice to: Delta Inc.;\r\n\u2022Invoice from: ABC Company;\r\n\u2022Invoice to: 8723 Wilton Boulevard, Los Angeles, CA;\r\n\u2022Invoice payment terms due date: May 3, 2022;\r\n\u2022Invoice quantity: 30 boxes of printer paper;\r\n\u2022Invoice unit", "gold_answer": "\u2022Invoice Date: March 3, 2022;\\\u2022Invoice Number: No 234;\n\u2022From: ABC Company;\n\u2022To: Delta Inc.;\n\u2022Due Date: Due on Receipt;\n\u2022Total: $4565.00.", "prompt": "What is a list of the key points?", "usage": {"input": 171, "metric": "tokens", "output": 101, "processing_time": 3.4869673252105713, "total": 272}, "category": "summary"} +{"number": 193, "llm_response": " Microsoft's AI and natural language capabilities are coming together with the world's most universal user interface - natural language - to create a new era of computing.", "gold_answer": "According to Microsoft's CEO and CFO, the Microsoft Cloud is the platform for choice for AI,and growing 22% year-over-year as a result of focused execution.", "prompt": "What is a summary in 30 words or less?", "usage": {"input": 166, "metric": "tokens", "output": 35, "processing_time": 1.2131953239440918, "total": 201}, "category": "summary"} +{"number": 194, "llm_response": " \u2022Emily W. Murphy, General Services Administration head;\r\n\u2022Joe Biden, President-elect;\r\n\u2022Vivek Murthy, former Surgeon General;\r\n\u2022David A. Kessler, former FDA commissioner;\r\n\u2022Marcella Nunez-Smith, Yale University's Professor;\r\n\u2022Jon Ossoff, Georgia Senator;\r\n\u2022Raphael Warnock, Georgia Senator.", "gold_answer": "\u2022Biden;\n\u2022Emily W. Murphy;\n\u2022Vivek Murthy;\n\u2022David A. Kessler;\n\u2022Marcella Nunez-Smith;\n\u2022Jon Ossoff;\n\u2022Raphael Warnock.", "prompt": "What is a list of all of the people mentioned?", "usage": {"input": 316, "metric": "tokens", "output": 89, "processing_time": 3.1019973754882812, "total": 405}, "category": "summary"} +{"number": 195, "llm_response": " Nokia announced that it will reduce its cost base and increase operation efficiency to address the challenging market environment.", "gold_answer": "Nokia is cutting 14,000 jobs as part of a cost reductionplan after reporting profits that declined 69% year-on-year.", "prompt": "What is a one-line summary?", "usage": {"input": 116, "metric": "tokens", "output": 21, "processing_time": 0.7212250232696533, "total": 137}, "category": "summary"} +{"number": 196, "llm_response": " \u2022Senior Vice President, Event Planning;\r\n\u2022Report to the Board of Directors;\r\n\u2022Devote substantially all of his business time and attention and his best efforts and ability to the operations of Employer and its subsidiaries;\r\n\u2022Run day-to-day operations;\r\n\u2022Perform faithfully, diligently and competently the duties and responsibilities of a SVP;\r\n\u2022Perform other duties and responsibilities as directed by the Board and consistent with such position.", "gold_answer": "\u2022Senior Vice President, Event Planning ('SVP') of Workforce Optimization Division;\n\u2022Report to Board of Directors;\n\u2022Devote substantially all of business time and attention to the operations of the Employer;\n\u2022responsible for running Employer's day-to-day operations;\n\u2022Perform faithfully, diligently and competently the duties and responsibilities of a SVP.", "prompt": "What is a list of the executive's responsibilities?", "usage": {"input": 345, "metric": "tokens", "output": 97, "processing_time": 3.376293659210205, "total": 442}, "category": "summary"} +{"number": 197, "llm_response": " NVIDIA announces financial results for second quarter of fiscal 2024, with record revenue of $13.51 billion, up 88% from Q1, up 101% from year ago, and record data center revenue of $10.32 billion, up 141% from Q1, up 171% from year ago.", "gold_answer": "NVIDIA Announces Second Quarter Fiscal 2024 Results with record revenue of $13.51 billion.", "prompt": "What is a headline description?", "usage": {"input": 470, "metric": "tokens", "output": 70, "processing_time": 2.4617671966552734, "total": 540}, "category": "summary"} +{"number": 198, "llm_response": " \"Microsoft is the platform of choice for customers to get the most value out of their digital spend and innovate for the next generation of AI.\"", "gold_answer": "Microsoft Cloud is the platform of choice for the next generation of AI.", "prompt": "What is a summary of the CEO's statement in 15 words or less?", "usage": {"input": 172, "metric": "tokens", "output": 30, "processing_time": 1.0371322631835938, "total": 202}, "category": "summary"} +{"number": 199, "llm_response": " \u2022Services Vendor Inc.\r\n\u2022100 Elm Street, Pleasantville, NY\r\n\u2022TO Alpha Inc., 5900 1st Street, Los Angeles, CA\r\n\u2022Description: Front End Engineering Service $5000.00\r\n\u2022Back End Engineering Service $7500.00\r\n\u2022Quality Assurance Manager $10,000.00\r\n\u2022Total Amount $22,500.00", "gold_answer": "\u2022Invoice From: Services Vendor Inc.;\n\u2022Invoice To: Alpha Inc.;\n\u2022Total Amount: $22,500.00;\n\u2022Payment Due: within 30 days;\n\u2022Contact: Bia Hermes;\n\u2022Invoice #0001;\u2022Data: 01/01/2022.", "prompt": "What are the key terms of the invoice?", "usage": {"input": 164, "metric": "tokens", "output": 90, "processing_time": 3.1135928630828857, "total": 254}, "category": "summary"} diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..02b145e --- /dev/null +++ b/generation_config.json @@ -0,0 +1,6 @@ +{ + "_from_model_config": true, + "bos_token_id": 11, + "eos_token_id": 11, + "transformers_version": "4.33.0.dev0" +} \ No newline at end of file diff --git a/generation_test_hf_script.py b/generation_test_hf_script.py new file mode 100644 index 0000000..0fb393c --- /dev/null +++ b/generation_test_hf_script.py @@ -0,0 +1,89 @@ + + +from transformers import AutoModelForCausalLM, AutoTokenizer +import torch + + +def load_rag_benchmark_tester_ds(): + + # pull 200 question rag benchmark test dataset from LLMWare HuggingFace repo + from datasets import load_dataset + + ds_name = "llmware/rag_instruct_benchmark_tester" + + dataset = load_dataset(ds_name) + + print("update: loading RAG Benchmark test dataset - ", dataset) + + test_set = [] + for i, samples in enumerate(dataset["train"]): + test_set.append(samples) + + # to view test set samples + # print("rag benchmark dataset test samples: ", i, samples) + + return test_set + + +def run_test(model_name, test_ds): + + device = "cuda" if torch.cuda.is_available() else "cpu" + + print("\nRAG Performance Test - 200 questions") + print("update: model - ", model_name) + print("update: device - ", device) + + model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True) + model.to(device) + + tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) + + for i, entries in enumerate(test_ds): + + # prepare prompt packaging used in fine-tuning process + new_prompt = ": " + entries["context"] + "\n" + entries["query"] + "\n" + ":" + + inputs = tokenizer(new_prompt, return_tensors="pt") + start_of_output = len(inputs.input_ids[0]) + + # temperature: set at 0.3 for consistency of output + # max_new_tokens: set at 100 - may prematurely stop a few of the summaries + + outputs = model.generate( + inputs.input_ids.to(device), + eos_token_id=tokenizer.eos_token_id, + pad_token_id=tokenizer.eos_token_id, + do_sample=True, + temperature=0.3, + max_new_tokens=100, + ) + + output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True) + + # quick/optional post-processing clean-up of potential fine-tuning artifacts + + eot = output_only.find("<|endoftext|>") + if eot > -1: + output_only = output_only[:eot] + + bot = output_only.find(":") + if bot > -1: + output_only = output_only[bot+len(":"):] + + # end - post-processing + + print("\n") + print(i, "llm_response - ", output_only) + print(i, "gold_answer - ", entries["answer"]) + + return 0 + + +if __name__ == "__main__": + + test_ds = load_rag_benchmark_tester_ds() + + model_name = "llmware/dragon-falcon-7b-v0" + output = run_test(model_name,test_ds) + + diff --git a/generation_test_llmware_script.py b/generation_test_llmware_script.py new file mode 100644 index 0000000..1d3f081 --- /dev/null +++ b/generation_test_llmware_script.py @@ -0,0 +1,70 @@ + +from llmware.prompts import Prompt + + +def load_rag_benchmark_tester_ds(): + + # pull 200 question rag benchmark test dataset from LLMWare HuggingFace repo + from datasets import load_dataset + + ds_name = "llmware/rag_instruct_benchmark_tester" + + dataset = load_dataset(ds_name) + + print("update: loading RAG Benchmark test dataset - ", dataset) + + test_set = [] + for i, samples in enumerate(dataset["train"]): + test_set.append(samples) + + # to view test set samples + # print("rag benchmark dataset test samples: ", i, samples) + + return test_set + + +def run_test(model_name, prompt_list): + + print("\nupdate: Starting RAG Benchmark Inference Test - ", model_name) + + # pull DRAGON / BLING model directly from catalog, e.g., no from_hf=True + prompter = Prompt().load_model(model_name) + + for i, entries in enumerate(prompt_list): + + prompt = entries["query"] + context = entries["context"] + + response = prompter.prompt_main(prompt,context=context,prompt_name="default_with_context", temperature=0.3) + + print("\nupdate: model inference output - ", i, response["llm_response"]) + print("update: gold_answer - ", i, entries["answer"]) + + fc = prompter.evidence_check_numbers(response) + sc = prompter.evidence_comparison_stats(response) + sr = prompter.evidence_check_sources(response) + + print("\nFact-Checking Tools") + + for entries in fc: + for f, facts in enumerate(entries["fact_check"]): + print("update: fact check - ", f, facts) + + for entries in sc: + print("update: comparison stats - ", entries["comparison_stats"]) + + for entries in sr: + for s, sources in enumerate(entries["source_review"]): + print("update: sources - ", s, sources) + + return 0 + + +if __name__ == "__main__": + + core_test_set = load_rag_benchmark_tester_ds() + + # one of the 7 gpu dragon models + gpu_model_name = "llmware/dragon-falcon-7b-v0" + + output = run_test(gpu_model_name, core_test_set) diff --git a/modeling_falcon.py b/modeling_falcon.py new file mode 100644 index 0000000..834822c --- /dev/null +++ b/modeling_falcon.py @@ -0,0 +1,1262 @@ +# coding=utf-8 +# Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Falcon model.""" + +import math +from typing import Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss +from torch.nn import functional as F + +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + QuestionAnsweringModelOutput, + SequenceClassifierOutputWithPast, + TokenClassifierOutput, +) +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging +from .configuration_falcon import FalconConfig + + +logger = logging.get_logger(__name__) + +FALCON_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "tiiuae/falcon-40b", + "tiiuae/falcon-40b-instruct", + "tiiuae/falcon-7b", + "tiiuae/falcon-7b-instruct", + "tiiuae/falcon-rw-7b", + "tiiuae/falcon-rw-1b", +] +_CHECKPOINT_FOR_DOC = "Rocketknight1/falcon-rw-1b" +_CONFIG_FOR_DOC = "FalconConfig" + + +# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations. +# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model. +class FalconLinear(nn.Linear): + def forward(self, input: torch.Tensor) -> torch.Tensor: + hidden_states = input @ self.weight.T + if self.bias is None: + return hidden_states + return hidden_states + self.bias + + +# rotary pos emb helpers (torch.jit.script does not seem to support staticmethod...) +def rotate_half(x): + x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +class FalconRotaryEmbedding(nn.Module): + """Implementation of RotaryEmbedding from GPT-NeoX. + This implementation is designed to operate on queries and keys that are compatible with `[batch_size, + n_heads_per_partition, seq_len, head_dim]` (e.g. MinGPTAttention format). + """ + + def __init__(self, head_dim: int, base=10000): + super().__init__() + inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.head_dim = head_dim + self.seq_len_cached = -1 + self.cos_cached: torch.Tensor | None = None + self.sin_cached: torch.Tensor | None = None + + def cos_sin(self, seq_len: int, past_key_values_length: int, device="cpu", dtype=torch.bfloat16) -> torch.Tensor: + total_length = seq_len + past_key_values_length + if total_length > self.seq_len_cached: + self.seq_len_cached = total_length + t = torch.arange(total_length, device=device, dtype=self.inv_freq.dtype) + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1).to(device) + + if dtype in [torch.float16, torch.bfloat16]: + emb = emb.float() + + self.cos_cached = emb.cos()[None, :, :] + self.sin_cached = emb.sin()[None, :, :] + + self.cos_cached = self.cos_cached.type(dtype) + self.sin_cached = self.sin_cached.type(dtype) + + return ( + self.cos_cached[:, past_key_values_length : seq_len + past_key_values_length], + self.sin_cached[:, past_key_values_length : seq_len + past_key_values_length], + ) + + def forward(self, query, key, past_key_values_length=0): + batch, seq_len, head_dim = query.shape + cos, sin = self.cos_sin(seq_len, past_key_values_length, query.device, query.dtype) + return (query * cos) + (rotate_half(query) * sin), (key * cos) + (rotate_half(key) * sin) + + +def _make_causal_mask( + input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int +) -> torch.BoolTensor: + """ + Make causal mask used for self-attention. This mask does not take the existing attention mask into account - it + just blocks tokens from attending forwards in the sequence. The output shape will be `[batch_size, 1, + target_length, target_length+past_key_values_length]`. + """ + batch_size, target_length = input_ids_shape + + mask = torch.triu(torch.ones((target_length, target_length), dtype=torch.bool, device=device), diagonal=1) + # If past_key_values_length is 0 this is an empty tensor and the concatenation is a no-op. + # This code style is an unfortunate consequence of getting your TF engineer to port models; doing it this + # way avoids a data-dependent conditional, which will help me when I have to port this to XLA later. + past_mask = torch.zeros((target_length, past_key_values_length), dtype=torch.bool, device=device) + mask = torch.cat([past_mask, mask], dim=-1) + expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length) + return expanded_mask + + +def _expand_mask(mask: torch.Tensor, past_key_values_length: int) -> torch.BoolTensor: + """ + Expands attention_mask from `[batch_size, seq_length]` to `[batch_size, 1, seq_length, seq_length + past_length]`. + """ + batch_size, total_length = mask.shape + seq_length = total_length - past_key_values_length if past_key_values_length is not None else total_length + + expanded_mask = ~(mask[:, None, None, :].to(torch.bool)) + return expanded_mask.expand(batch_size, 1, seq_length, total_length) + + +def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor: + batch_size, seq_length = attention_mask.shape + closest_power_of_2 = 2 ** math.floor(math.log2(num_heads)) + base = torch.tensor( + 2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32 + ) + powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32) + slopes = torch.pow(base, powers) + + if closest_power_of_2 != num_heads: + extra_base = torch.tensor( + 2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32 + ) + num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2) + extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32) + slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) + + # Note: alibi will added to the attention bias that will be applied to the query, key product of attention + # => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length) + # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length) + # => the query_length dimension will then be broadcasted correctly + # This is more or less identical to T5's relative position bias: + # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527 + arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :] + alibi = slopes[..., None].bfloat16() * arange_tensor + return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype) + + +# Copied from transformers.models.bloom.modeling_bloom.dropout_add +def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor: + """ + Dropout add function + + Args: + x (`torch.tensor`, *required*): + input tensor + residual (`torch.tensor`, *required*): + residual tensor + prob (`float`, *required*): + dropout probability + training (`bool`, *required*): + training mode + """ + out = F.dropout(x, p=prob, training=training) + out = residual + out + return out + + +class FalconAttention(nn.Module): + def __init__(self, config: FalconConfig): + super().__init__() + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.split_size = self.hidden_size + self.hidden_dropout = config.hidden_dropout + + if self.head_dim * self.num_heads != self.hidden_size: + raise ValueError( + f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:" + f" {self.num_heads})." + ) + + self.maybe_rotary = FalconRotaryEmbedding(config.head_dim) if config.rotary else lambda q, k, t: (q, k) + + # Layer-wise attention scaling + self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim) + self.beta = self.inv_norm_factor + if config.new_decoder_architecture: + qkv_out_dim = (config.num_kv_heads * 2 + config.num_attention_heads) * self.head_dim + elif config.multi_query: + qkv_out_dim = self.hidden_size + 2 * self.head_dim + else: + qkv_out_dim = 3 * self.hidden_size + self.query_key_value = FalconLinear(self.hidden_size, qkv_out_dim, bias=config.bias) + self.new_decoder_architecture = config.new_decoder_architecture + self.multi_query = config.multi_query + self.dense = FalconLinear(self.hidden_size, self.hidden_size, bias=config.bias) + self.attention_dropout = nn.Dropout(config.attention_dropout) + self.num_kv_heads = config.num_kv_heads if (self.new_decoder_architecture or not self.multi_query) else 1 + + def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Split the last dimension into (num_heads, head_dim), results share same memory storage as `fused_qkv` + + Args: + fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim] + + Returns: + query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim] + value: [batch_size, seq_length, num_heads, head_dim] + """ + if self.new_decoder_architecture: + batch, seq_len, _ = fused_qkv.shape + qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv_heads + 2, self.head_dim) + query = qkv[:, :, :, :-2] + key = qkv[:, :, :, [-2]] + value = qkv[:, :, :, [-1]] + key = torch.broadcast_to(key, query.shape) + value = torch.broadcast_to(value, query.shape) + + query, key, value = [x.flatten(2, 3) for x in (query, key, value)] + return query, key, value + elif not self.multi_query: + batch_size, seq_length, three_times_hidden_size = fused_qkv.shape + fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim) + return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :] + else: + batch_size, seq_length, three_times_hidden_size = fused_qkv.shape + fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim) + return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :] + + # Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads + def _merge_heads(self, x: torch.Tensor) -> torch.Tensor: + """ + Merge heads together over the last dimenstion + + Args: + x (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim] + + Returns: + torch.tensor: [batch_size, seq_length, num_heads * head_dim] + """ + # What we want to achieve is: + # batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim + batch_size_and_num_heads, seq_length, _ = x.shape + batch_size = batch_size_and_num_heads // self.num_heads + + # First view to decompose the batch size + # batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim + x = x.view(batch_size, self.num_heads, seq_length, self.head_dim) + + # batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim + x = x.permute(0, 2, 1, 3) + + # batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim + return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim) + + def forward( + self, + hidden_states: torch.Tensor, + alibi: Optional[torch.Tensor], + attention_mask: torch.Tensor, + layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + head_mask: Optional[torch.Tensor] = None, + use_cache: bool = False, + output_attentions: bool = False, + ): + fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size] + num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads + # 3 x [batch_size, seq_length, num_heads, head_dim] + (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv) + + batch_size, query_length, _, _ = query_layer.shape + + query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, query_length, self.head_dim) + key_layer = key_layer.transpose(1, 2).reshape( + batch_size * num_kv_heads, + query_length, + self.head_dim, + ) + value_layer = value_layer.transpose(1, 2).reshape(batch_size * num_kv_heads, query_length, self.head_dim) + + past_kv_length = 0 if layer_past is None else layer_past[0].shape[1] + query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length) + + if layer_past is not None: + past_key, past_value = layer_past + # concatenate along seq_length dimension: + # - key: [batch_size * self.num_heads, kv_length, head_dim] + # - value: [batch_size * self.num_heads, kv_length, head_dim] + key_layer = torch.cat((past_key, key_layer), dim=1) + value_layer = torch.cat((past_value, value_layer), dim=1) + + _, kv_length, _ = key_layer.shape + if use_cache: + present = (key_layer, value_layer) + else: + present = None + + attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float("-1e9")).to(query_layer.dtype) + + query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim) + key_layer_ = key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim) + value_layer_ = value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim) + + if alibi is None: + if output_attentions: + # F.scaled_dot_product_attention doesn't return the attention weights, so we have + # to do it by hand if we want them + attention_scores = query_layer_ @ key_layer_.transpose(-1, -2) + attention_scores /= math.sqrt(self.head_dim) + + attention_scores = F.softmax( + attention_scores + attention_mask_float, dim=-1, dtype=hidden_states.dtype + ) + attn_output = attention_scores @ value_layer_ + else: + attn_output = F.scaled_dot_product_attention( + query_layer_, key_layer_, value_layer_, attention_mask_float, 0.0, is_causal=False + ) + attention_scores = None + + attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim) + attn_output = attn_output.permute(0, 2, 1, 3) + attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim) + + output_tensor = self.dense(attn_output) + + if output_attentions: + return output_tensor, present, attention_scores + else: + return output_tensor, present + + else: + matmul_result = query_layer_ @ key_layer_.transpose(-1, -2) + + # change view to [batch_size, num_heads, q_length, kv_length] + attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length) + + # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length] + input_dtype = attention_scores.dtype + # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38` + if input_dtype == torch.float16 or input_dtype == torch.bfloat16: + attention_scores = attention_scores.to(torch.float32) + # Matt (HF) note: We could possibly use F.scaled_dot_product_attention here too, by + # adding (alibi * self.inv_norm_factor) to attention_mask_float. I think this would be mathematically + # equivalent and more performant, but there might be a numerical difference. If you're reading this + # and you'd like to experiment and maybe file a PR, feel free! + attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1) + attention_logits *= self.inv_norm_factor + attention_probs = F.softmax(attention_logits + attention_mask_float, dim=-1, dtype=hidden_states.dtype) + # [batch_size, num_heads, q_length, kv_length] + attention_probs = self.attention_dropout(attention_probs) + + if head_mask is not None: + attention_probs = attention_probs * head_mask + + # change view [batch_size, num_heads, q_length, kv_length] + attention_probs_reshaped = attention_probs.view(batch_size, self.num_heads, query_length, kv_length) + + # matmul: [batch_size * num_heads, q_length, head_dim] + context_layer = (attention_probs_reshaped @ value_layer_).flatten(0, 1) + + # change view [batch_size, num_heads, q_length, head_dim] + context_layer = self._merge_heads(context_layer) + + output_tensor = self.dense(context_layer) + + if output_attentions: + return output_tensor, present, attention_probs + else: + return output_tensor, present + + +class FalconMLP(nn.Module): + def __init__(self, config: FalconConfig): + super().__init__() + hidden_size = config.hidden_size + + self.dense_h_to_4h = FalconLinear(hidden_size, 4 * hidden_size, bias=config.bias) + self.act = nn.GELU() + self.dense_4h_to_h = FalconLinear(4 * hidden_size, hidden_size, bias=config.bias) + self.hidden_dropout = config.hidden_dropout + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.act(self.dense_h_to_4h(x)) + x = self.dense_4h_to_h(x) + return x + + +class FalconDecoderLayer(nn.Module): + def __init__(self, config: FalconConfig): + super().__init__() + hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.self_attention = FalconAttention(config) + self.mlp = FalconMLP(config) + self.hidden_dropout = config.hidden_dropout + self.config = config + + if config.new_decoder_architecture: + # The layer norm before self-attention + self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) + # The layer norm before the MLP + self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) + else: + self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) + if not config.parallel_attn: + self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon) + + def forward( + self, + hidden_states: torch.Tensor, + alibi: Optional[torch.Tensor], + attention_mask: torch.Tensor, + layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + head_mask: Optional[torch.Tensor] = None, + use_cache: bool = False, + output_attentions: bool = False, + ): + residual = hidden_states + + if self.config.new_decoder_architecture: + attention_layernorm_out = self.ln_attn(hidden_states) + mlp_layernorm_out = self.ln_mlp(hidden_states) + else: + attention_layernorm_out = self.input_layernorm(hidden_states) + + # Self attention. + attn_outputs = self.self_attention( + attention_layernorm_out, + layer_past=layer_past, + attention_mask=attention_mask, + alibi=alibi, + head_mask=head_mask, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + attention_output = attn_outputs[0] + + if not self.config.new_decoder_architecture: + if self.config.parallel_attn: + mlp_layernorm_out = attention_layernorm_out + else: + residual = dropout_add( + attention_output, residual, self.config.attention_dropout, training=self.training + ) + mlp_layernorm_out = self.post_attention_layernorm(residual) + + outputs = attn_outputs[1:] + + # MLP. + mlp_output = self.mlp(mlp_layernorm_out) + + if self.config.new_decoder_architecture or self.config.parallel_attn: + mlp_output += attention_output + + output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training) + + if use_cache: + outputs = (output,) + outputs + else: + outputs = (output,) + outputs[1:] + + return outputs # hidden_states, present, attentions + + +FALCON_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`FalconConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +FALCON_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`): + `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[2]` + (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. + + If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as + `input_ids`. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.num_hidden_layers`): + Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see + `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have + their past given to this model should not be passed as `input_ids` as they have already been computed. + + Each element of `past_key_values` is a tuple (past_key, past_value): + - past_key: [batch_size * num_heads, head_dim, kv_length] + - past_value: [batch_size * num_heads, kv_length, head_dim] + attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): + Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + + If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see + `past_key_values`). + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. +""" + + +class FalconPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = FalconConfig + base_model_prefix = "transformer" + supports_gradient_checkpointing = True + _no_split_modules = ["FalconDecoderLayer"] + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + if isinstance(module, nn.Linear) or isinstance(module, FalconLinear): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + # Copied from transformers.models.bloom.modeling_bloom.BloomPreTrainedModel._set_gradient_checkpointing with BloomModel->FalconModel + def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False): + if isinstance(module, FalconModel): + module.gradient_checkpointing = value + + @staticmethod + def _convert_cache_to_standard_format( + past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]: + """ + Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size, + num_heads, ...])) + """ + batch_size_times_num_heads, kv_length, head_dim = past_key_value[0][0].shape + # [batch_size * self.num_heads, kv_length, head_dim] -> [batch_size, num_heads, kv_length, head_dim] + # Note that don't want to use self.num_attention_heads because the number of heads may vary depending + # on whether we use multi_query attention. + num_heads = batch_size_times_num_heads // batch_size + return tuple( + ( + layer_past[0].view(batch_size, num_heads, kv_length, head_dim), + layer_past[1].view(batch_size, num_heads, kv_length, head_dim), + ) + for layer_past in past_key_value + ) + + @staticmethod + def _convert_to_rw_cache( + past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]] + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]: + batch_size, num_heads, kv_length, head_dim = past_key_value[0][0].shape + batch_size_times_num_heads = batch_size * num_heads + # [batch_size, num_heads, kv_length, head_dim] -> [batch_size * num_heads, kv_length, head_dim] + return tuple( + ( + layer_past[0].view(batch_size_times_num_heads, kv_length, head_dim), + layer_past[1].view(batch_size_times_num_heads, kv_length, head_dim), + ) + for layer_past in past_key_value + ) + + +@add_start_docstrings( + "The bare Falcon Model transformer outputting raw hidden-states without any specific head on top.", + FALCON_START_DOCSTRING, +) +class FalconModel(FalconPreTrainedModel): + def __init__(self, config: FalconConfig): + super().__init__(config) + + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.use_alibi = config.alibi + + # Embedding + LN Embedding + self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim) + + # Transformer blocks + self.h = nn.ModuleList([FalconDecoderLayer(config) for _ in range(config.num_hidden_layers)]) + + # Final Layer Norm + self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) + + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.word_embeddings + + @staticmethod + def _prepare_attn_mask( + attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int + ) -> torch.BoolTensor: + # Create a causal mask + # The attention mask we receive as input should cover the whole extended sequence, including any past + # cache, so its shape should be [batch_size, seq_length + past_key_values_length] + # The output shape will be [batch_size, 1, seq_length, seq_length + past_key_values_length] + if input_shape[1] + past_key_values_length != attention_mask.shape[1]: + raise ValueError( + "Attention mask shape should be (batch_size, seq_length + past_key_values_length)" + f" but is {attention_mask.shape} with input_ids shape {input_shape} and past length" + f" {past_key_values_length}." + ) + combined_attention_mask = None + device = attention_mask.device + _, seq_length = input_shape + + if seq_length > 1: + combined_attention_mask = _make_causal_mask( + input_shape, device=device, past_key_values_length=past_key_values_length + ) + + # [batch_size, seq_length + past_key_values_length] -> [batch_size, 1, seq_length, seq_length + past_key_values_length] + expanded_attn_mask = _expand_mask(attention_mask, past_key_values_length=past_key_values_length) + combined_attention_mask = ( + expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask + ) + + return combined_attention_mask + + def set_input_embeddings(self, new_embeddings: torch.Tensor): + self.word_embeddings = new_embeddings + + @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPastAndCrossAttentions, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.LongTensor] = None, + inputs_embeds: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if past_key_values is None: + past_key_values = tuple([None] * len(self.h)) + else: + past_key_values = self._convert_to_rw_cache(past_key_values) + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape batch_size x num_heads x N x N + # head_mask has shape n_layer x batch x num_heads x N x N + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + hidden_states = inputs_embeds + + presents = () if use_cache else None + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + + # Compute alibi tensor: check build_alibi_tensor documentation + past_key_values_length = 0 + if past_key_values[0] is not None: + past_key_values_length = past_key_values[0][0].shape[1] # 1 because RW-cache, not standard format + if attention_mask is None: + attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=hidden_states.device) + else: + attention_mask = attention_mask.to(hidden_states.device) + + if self.use_alibi: + alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype) + else: + alibi = None + + causal_mask = self._prepare_attn_mask( + attention_mask, + input_shape=(batch_size, seq_length), + past_key_values_length=past_key_values_length, + ) + + for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + # None for past_key_value + return module(*inputs, use_cache=use_cache, output_attentions=output_attentions) + + return custom_forward + + outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + hidden_states, + alibi, + causal_mask, + head_mask[i], + ) + else: + outputs = block( + hidden_states, + layer_past=layer_past, + attention_mask=causal_mask, + head_mask=head_mask[i], + use_cache=use_cache, + output_attentions=output_attentions, + alibi=alibi, + ) + + hidden_states = outputs[0] + if use_cache is True: + presents = presents + (outputs[1],) + + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) + + # Add last hidden state + hidden_states = self.ln_f(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if presents is not None: + presents = self._convert_cache_to_standard_format(presents, batch_size) + + if not return_dict: + return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=presents, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +@add_start_docstrings( + "The Falcon Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings).", + FALCON_START_DOCSTRING, +) +class FalconForCausalLM(FalconPreTrainedModel): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config: FalconConfig): + super().__init__(config) + self.transformer = FalconModel(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings: torch.Tensor): + self.lm_head = new_embeddings + + def prepare_inputs_for_generation( + self, + input_ids: torch.LongTensor, + past_key_values: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + **kwargs, + ) -> dict: + if past_key_values is not None: + input_ids = input_ids[:, -1:] + + return { + "input_ids": input_ids, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + + @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=CausalLMOutputWithCrossAttentions, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + """ + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + + lm_logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + batch_size, seq_length, vocab_size = shift_logits.shape + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct( + shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length) + ) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + def _reorder_cache( + self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor + ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: + """ + This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or + [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct + beam_idx at every generation step. + + Output shares the same memory storage as `past`. + """ + + # Get a copy of `beam_idx` on all the devices where we need those indices. + device_to_beam_idx = { + past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past + } + reordered_past = tuple( + ( + layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]), + layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]), + ) + for layer_past in past + ) + return reordered_past + + +@add_start_docstrings( + """ + The Falcon Model transformer with a sequence classification head on top (linear layer). + + [`FalconForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-1) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """, + FALCON_START_DOCSTRING, +) +class FalconForSequenceClassification(FalconPreTrainedModel): + def __init__(self, config: FalconConfig): + super().__init__(config) + self.num_labels = config.num_labels + self.transformer = FalconModel(config) + self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=SequenceClassifierOutputWithPast, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size = input_ids.shape[0] + else: + batch_size = inputs_embeds.shape[0] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1 + else: + sequence_lengths = -1 + logger.warning( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + "unexpected if using padding tokens in conjunction with `inputs_embeds.`" + ) + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + +@add_start_docstrings( + """ + Falcon Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for + Named-Entity-Recognition (NER) tasks. + """, + FALCON_START_DOCSTRING, +) +class FalconForTokenClassification(FalconPreTrainedModel): + def __init__(self, config: FalconConfig): + super().__init__(config) + self.num_labels = config.num_labels + + self.transformer = FalconModel(config) + if getattr(config, "classifier_dropout", None) is not None: + classifier_dropout = config.classifier_dropout + elif getattr(config, "hidden_dropout", None) is not None: + classifier_dropout = config.hidden_dropout + else: + classifier_dropout = 0.1 + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=TokenClassifierOutput, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, + attention_mask: Optional[torch.Tensor] = None, + head_mask: Optional[torch.Tensor] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + hidden_states = self.dropout(hidden_states) + logits = self.classifier(hidden_states) + + loss = None + if labels is not None: + batch_size, seq_length = labels.shape + loss_fct = CrossEntropyLoss() + loss = loss_fct( + logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length) + ) + + if not return_dict: + output = (logits,) + transformer_outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + +@add_start_docstrings( + """ + The Falcon Model transformer with a span classification head on top for extractive question-answering tasks like + SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). + """, + FALCON_START_DOCSTRING, +) +class FalconForQuestionAnswering(FalconPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.transformer = FalconModel(config) + self.qa_outputs = nn.Linear(config.hidden_size, 2) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, QuestionAnsweringModelOutput]: + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.transformer( + input_ids, + attention_mask=attention_mask, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/pytorch_model.bin b/pytorch_model.bin new file mode 100644 index 0000000..797cca2 --- /dev/null +++ b/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9d0ecb355f9c3466d943c32ded461e2e04f241d66019a7f27e39356cf9683aa +size 13843529713 diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..24f43d8 --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,16 @@ +{ + "additional_special_tokens": [ + ">>TITLE<<", + ">>ABSTRACT<<", + ">>INTRODUCTION<<", + ">>SUMMARY<<", + ">>COMMENT<<", + ">>ANSWER<<", + ">>QUESTION<<", + ">>DOMAIN<<", + ">>PREFIX<<", + ">>SUFFIX<<", + ">>MIDDLE<<" + ], + "eos_token": "<|endoftext|>" +} diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..24f2d2e --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,129970 @@ +{ + "version": "1.0", + "truncation": null, + "padding": null, + "added_tokens": [ + { + "id": 0, + "content": ">>TITLE<<", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 1, + "content": ">>ABSTRACT<<", + "single_word": false, + "lstrip": false, + "rstrip": 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16343, + "Ġcustomize": 16344, + "²³": 16345, + "Ġheeft": 16346, + "Ġenvelop": 16347, + "ého": 16348, + "Following": 16349, + "ĠPills": 16350, + "ĠMalaysia": 16351, + "ĠOperations": 16352, + "Ġbored": 16353, + "Ġpriced": 16354, + "Ġembedded": 16355, + "Ġappliances": 16356, + "ĠFamil": 16357, + "åİŁ": 16358, + "ĠMeanwhile": 16359, + "ĠTaiwan": 16360, + "Ġcorps": 16361, + "Ġclosure": 16362, + "olutely": 16363, + "ĠSwiss": 16364, + "olitan": 16365, + "Ġorientation": 16366, + "ĠLGBT": 16367, + "kar": 16368, + "IDS": 16369, + "Tree": 16370, + "UND": 16371, + "ĠGulf": 16372, + "Ġneeding": 16373, + "orp": 16374, + "than": 16375, + "-(": 16376, + "Ġsolving": 16377, + "Ġnotable": 16378, + "ĠGray": 16379, + "Ġcement": 16380, + "ĠExpert": 16381, + "atura": 16382, + "ĠIntel": 16383, + "Ġlig": 16384, + "Ġpresenting": 16385, + "Ġbasics": 16386, + "ainted": 16387, + "Ġcertific": 16388, + "Ġdove": 16389, + "Ġdegli": 16390, + "LOG": 16391, + "åĩł": 16392, + "ĠOtherwise": 16393, + "Adv": 16394, + "claimed": 16395, + "uters": 16396, + "Ġkeywords": 16397, + "rison": 16398, + "Ġvessels": 16399, + "jes": 16400, + "ĠPortland": 16401, + "Ġprints": 16402, + "ritional": 16403, + "eff": 16404, + "ieg": 16405, + "ĠNash": 16406, + "Ġgenerating": 16407, + "azines": 16408, + "Ġtherapeutic": 16409, + "template": 16410, + "ĠBluetooth": 16411, + "Ġchlor": 16412, + "æ¸ħ": 16413, + "Ġdesperate": 16414, + "Ġvou": 16415, + "owship": 16416, + "posed": 16417, + "ĠKennedy": 16418, + "Ġpanc": 16419, + "ĠLad": 16420, + "expl": 16421, + "ĠMcG": 16422, + "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠ": 16423, + "uggest": 16424, + "Ġautre": 16425, + "Ġbump": 16426, + "Ġvalued": 16427, + "ciÃ": 16428, + "Ġnouve": 16429, + "Ġdistinction": 16430, + "Ġprevents": 16431, + "Ġteens": 16432, + "Ġpond": 16433, + "sure": 16434, + "Ġdominant": 16435, + "Ġimplies": 16436, + "ĠMississippi": 16437, + "åĮĸ": 16438, + "Ġwhispered": 16439, + "Ġmindset": 16440, + "Ġconvention": 16441, + "Ġlifting": 16442, + "Task": 16443, + "ĠNevertheless": 16444, + "Ġzones": 16445, + "ĠAlaska": 16446, + "urable": 16447, + "äºĶ": 16448, + "ĠBarcel": 16449, + "ĠCrim": 16450, + "ĠYe": 16451, + "ifferent": 16452, + "rones": 16453, + "Ġsail": 16454, + "Ġforming": 16455, + "Ġapplicants": 16456, + "ĠUniversal": 16457, + "Ġjako": 16458, + "ĠRy": 16459, + "Ġpréc": 16460, + "Ġbitter": 16461, + "ĠVel": 16462, + "Ġattendance": 16463, + "Bes": 16464, + "Ġrestoration": 16465, + "Ġstato": 16466, + "978": 16467, + "Ġmeer": 16468, + "Ġjudges": 16469, + "Ġmeter": 16470, + "mac": 16471, + "Ġmanuscript": 16472, + "Ġfootage": 16473, + "Ġcontrolling": 16474, + "ouncing": 16475, + "Ġreass": 16476, + "ĠCole": 16477, + "Ġalgorithms": 16478, + "Ġcontam": 16479, + "è¿ĻäºĽ": 16480, + "inite": 16481, + "ĠVision": 16482, + "$(": 16483, + "Ġcharts": 16484, + "Ġchampionship": 16485, + "Ġell": 16486, + "Ġthermal": 16487, + "ĠRoberts": 16488, + "iane": 16489, + "165": 16490, + "Ġinjection": 16491, + "Ġcollaborative": 16492, + "Settings": 16493, + "ĠWer": 16494, + "Ġpartially": 16495, + "ĠQuestion": 16496, + "ĠRic": 16497, + "Ġindependently": 16498, + "ĠGot": 16499, + "fare": 16500, + "storm": 16501, + "Ġsop": 16502, + "Ġobtaining": 16503, + "Ġvendor": 16504, + "Ġanni": 16505, + "Ġabsorb": 16506, + "Ġmaximize": 16507, + "ĠChamber": 16508, + "Ġdiagnostic": 16509, + "ĠVancouver": 16510, + "ĠTan": 16511, + "mentation": 16512, + "rise": 16513, + "Ġwax": 16514, + "ientes": 16515, + "Ġmonster": 16516, + "RY": 16517, + "Ġtouching": 16518, + "aty": 16519, + "Ġpipeline": 16520, + "Ġthreshold": 16521, + "åıĺ": 16522, + "Ã¥n": 16523, + "cker": 16524, + "Ġfirmly": 16525, + "ĠPred": 16526, + "arians": 16527, + "æīį": 16528, + "Ġbinary": 16529, + "Ġdeclined": 16530, + "arde": 16531, + "etics": 16532, + "ologic": 16533, + "ĠEnc": 16534, + "ĠBot": 16535, + "Ġdrunk": 16536, + "ĠDak": 16537, + "âĢĶ\"": 16538, + "Miss": 16539, + "ĠHab": 16540, + "Ġsixth": 16541, + "iens": 16542, + "Ġjar": 16543, + "nea": 16544, + "ĠYOUR": 16545, + "Ġsuiv": 16546, + "ĠStories": 16547, + "Ġhormone": 16548, + "Ġgrief": 16549, + "Ġbears": 16550, + "Ġbriefly": 16551, + "noÅĽci": 16552, + "ĠOrleans": 16553, + "KS": 16554, + "DAY": 16555, + "vd": 16556, + "Ġelevated": 16557, + "ĠActually": 16558, + "Ġcleaner": 16559, + "ĠClimate": 16560, + "ĠNYC": 16561, + "144": 16562, + "Ġconditioning": 16563, + "Ġshar": 16564, + "Ġkicked": 16565, + "hicle": 16566, + "èĴ": 16567, + "133": 16568, + "Ġlazy": 16569, + "ĠSaudi": 16570, + "Ġtasty": 16571, + "ĠWhatsApp": 16572, + "adal": 16573, + "Ġherbs": 16574, + "Research": 16575, + "DU": 16576, + "Ġshades": 16577, + "emás": 16578, + "Ġsensors": 16579, + "Ġthy": 16580, + "å§ĭ": 16581, + "available": 16582, + "ĠTechnologies": 16583, + "ivel": 16584, + "Empty": 16585, + "ischer": 16586, + "ĠMun": 16587, + "ĠHi": 16588, + "Ġcargo": 16589, + "Ġkinda": 16590, + "Dem": 16591, + "prim": 16592, + "stru": 16593, + "Meanwhile": 16594, + "GP": 16595, + "ardi": 16596, + "ĠAlice": 16597, + "ĠKay": 16598, + "Ġcircles": 16599, + "Ġafterwards": 16600, + "Ġowns": 16601, + "Ġfright": 16602, + "ĠTut": 16603, + "Ġtomato": 16604, + "Ġrotation": 16605, + "ĠWoman": 16606, + "ĠJennifer": 16607, + "Bro": 16608, + "ĠJavaScript": 16609, + "Ġshore": 16610, + "Ġvisitor": 16611, + "Ġvamp": 16612, + "Ġsuspended": 16613, + "uity": 16614, + "Ġgluten": 16615, + "Ġdisappeared": 16616, + "Ġrab": 16617, + "Ġsteep": 16618, + "Ġlightly": 16619, + "Ġaesthetic": 16620, + "ĠPython": 16621, + "Ġrealiz": 16622, + "Ġcleaned": 16623, + "Por": 16624, + "Ġadjustable": 16625, + "Elect": 16626, + "Ġak": 16627, + "ĠYan": 16628, + "Ġllev": 16629, + "Ġreasonably": 16630, + "Ġbeaches": 16631, + "Ġsurveys": 16632, + "ĠLind": 16633, + "Live": 16634, + "tein": 16635, + "Ġadop": 16636, + "ĠRetrie": 16637, + "ĠMuslims": 16638, + "æľº": 16639, + "iker": 16640, + "Ġdoses": 16641, + "ĠMatch": 16642, + "Ġoverlook": 16643, + "azer": 16644, + "Ġcerc": 16645, + "kow": 16646, + "Ġjazz": 16647, + "Ġdelayed": 16648, + "ĠBright": 16649, + "Ġchrist": 16650, + "Ġinvis": 16651, + "Ġcombines": 16652, + "Ġhabe": 16653, + "Ġdistant": 16654, + "Ġimaging": 16655, + "ĠFine": 16656, + "usters": 16657, + "Ġunh": 16658, + "-->": 16659, + "Ġanalyses": 16660, + "anka": 16661, + "otation": 16662, + ".]": 16663, + "disc": 16664, + "ammar": 16665, + "orde": 16666, + "Ġdeployment": 16667, + "Ġeducated": 16668, + "ĠJustin": 16669, + "Ġrecruitment": 16670, + "ĠRussell": 16671, + "Ġnurses": 16672, + "åĶ": 16673, + "infect": 16674, + "dates": 16675, + "Ġrally": 16676, + "Ġaj": 16677, + "icion": 16678, + "Header": 16679, + "Ġjson": 16680, + "Effect": 16681, + "tu": 16682, + "Ġchron": 16683, + "ĠJahr": 16684, + "scope": 16685, + "Ġsegments": 16686, + "Commerce": 16687, + "åħµ": 16688, + "Meet": 16689, + "Ġurge": 16690, + "lat": 16691, + "ĠPf": 16692, + "Ġvitamins": 16693, + "Ġhi": 16694, + "Ġbases": 16695, + "Ġdietary": 16696, + "erta": 16697, + "ffe": 16698, + "Ġjournalist": 16699, + "åIJĪ": 16700, + "bild": 16701, + "Ġprospective": 16702, + "rze": 16703, + "Thread": 16704, + "Ġglucose": 16705, + "ochem": 16706, + "<<.": 16707, + "abad": 16708, + "Ġfeas": 16709, + "ĠSettings": 16710, + "Ġpartnerships": 16711, + "ĠIsa": 16712, + "Record": 16713, + "ĠStars": 16714, + "Ġconoc": 16715, + "arness": 16716, + "lar": 16717, + "Ġtym": 16718, + "Ġmolecular": 16719, + "Ġdeleted": 16720, + "ĠKam": 16721, + "ĠSorry": 16722, + "Ġthrilled": 16723, + "ĠTruth": 16724, + "Ġbrowse": 16725, + "Ġkeine": 16726, + "Body": 16727, + "custom": 16728, + "address": 16729, + "Ġcaptain": 16730, + "ieurs": 16731, + "Ġstruggles": 16732, + "Ġhike": 16733, + "cken": 16734, + "Ġtwin": 16735, + "çļĦä": 16736, + "Collection": 16737, + "Ġsponsored": 16738, + "bye": 16739, + "ĠSyria": 16740, + "yles": 16741, + "ĠSwitzerland": 16742, + "HC": 16743, + "ĠIch": 16744, + "aky": 16745, + "Ġrepet": 16746, + "chem": 16747, + "ilib": 16748, + "åľº": 16749, + "oped": 16750, + "Ġassociations": 16751, + "hma": 16752, + "Ġrecovered": 16753, + "Know": 16754, + "ĠSusan": 16755, + "Ġwarmth": 16756, + "Ġoutline": 16757, + "ĠlÃł": 16758, + "ĠCelebr": 16759, + "ĠCleveland": 16760, + "Stop": 16761, + "connected": 16762, + "iÄħ": 16763, + "Ġtactics": 16764, + "save": 16765, + "Ġkomm": 16766, + "Ġseasonal": 16767, + "Ġheel": 16768, + "Ġgrave": 16769, + "Ġstreams": 16770, + "Ġcheek": 16771, + "itas": 16772, + "Ġdealt": 16773, + "Space": 16774, + "Ġlibert": 16775, + "IGN": 16776, + "Ġhydrogen": 16777, + "Ġretro": 16778, + "Ġcorruption": 16779, + "Ġspa": 16780, + "ĠEval": 16781, + "bind": 16782, + "Ġprayers": 16783, + "Ġclouds": 16784, + "ĠTruck": 16785, + "comed": 16786, + "usamm": 16787, + "Ġcomparing": 16788, + "ĠBah": 16789, + "178": 16790, + "ĠLiver": 16791, + "Ġsituated": 16792, + "clock": 16793, + "Ġbasement": 16794, + "ĠHoliday": 16795, + "Ġanth": 16796, + "Ġdisabilities": 16797, + "ĠHealthcare": 16798, + "imated": 16799, + "ĠNep": 16800, + "ĠBond": 16801, + "esc": 16802, + "ASH": 16803, + "ĠGL": 16804, + "rams": 16805, + "å©": 16806, + "ĠAlber": 16807, + "ĠThai": 16808, + "Ġcommod": 16809, + "anut": 16810, + "ĠTower": 16811, + "breaking": 16812, + "Ġfragr": 16813, + "Ġexpend": 16814, + "Ġjug": 16815, + "know": 16816, + "Ġshowcase": 16817, + "Ġconfusing": 16818, + "Ġdistricts": 16819, + "ĠCyber": 16820, + "Ġdragon": 16821, + "chair": 16822, + "Ġprivilege": 16823, + "Ġcounting": 16824, + "ĠGun": 16825, + "Ġmaxim": 16826, + "Ġborders": 16827, + "ĠSwitch": 16828, + "Ġassists": 16829, + "ĠTher": 16830, + "ĠIniti": 16831, + "145": 16832, + "Hash": 16833, + "inance": 16834, + "Ġdescribing": 16835, + "Ġchaos": 16836, + "155": 16837, + "Ġbucket": 16838, + "ĠHO": 16839, + "sv": 16840, + "ĠVolume": 16841, + "icut": 16842, + "ĠGrade": 16843, + "Ġtransit": 16844, + "Ġslides": 16845, + "Ġcollapse": 16846, + "Ġfifty": 16847, + "ĠFlex": 16848, + "Ġparticipant": 16849, + "Ġdupl": 16850, + "Ġdispute": 16851, + "Ġurgent": 16852, + "Ġchemistry": 16853, + "Ġtokens": 16854, + "Ġzap": 16855, + "ç»ĵ": 16856, + "asis": 16857, + "ĠMos": 16858, + "urred": 16859, + "Track": 16860, + "ĠHeritage": 16861, + "Ġconcentrate": 16862, + "Ġlyrics": 16863, + "Ġace": 16864, + "ĠHalf": 16865, + "ĠOverall": 16866, + "ĠArthur": 16867, + "ĠAx": 16868, + "ontin": 16869, + "Ġpound": 16870, + "Ġplac": 16871, + "Ġfolk": 16872, + "Ġnonetheless": 16873, + "Ġunderstands": 16874, + "Ġnationwide": 16875, + "Ġcyl": 16876, + "assador": 16877, + "ĠLinkedIn": 16878, + "Ġstressed": 16879, + "ĠOt": 16880, + "OME": 16881, + "Ġrepay": 16882, + "inars": 16883, + "Ġrolls": 16884, + "ĠJos": 16885, + "Ġhash": 16886, + "Thursday": 16887, + "ĠFÃ": 16888, + "nered": 16889, + "lit": 16890, + "Try": 16891, + "Ġobsc": 16892, + "ĠSL": 16893, + "ĠBrooklyn": 16894, + "Ġniche": 16895, + "Ġeastern": 16896, + "ĠFaith": 16897, + "chte": 16898, + "Ġattain": 16899, + "Website": 16900, + "ĠTes": 16901, + "Ġincorporated": 16902, + "Ġamenities": 16903, + "Ġbackpack": 16904, + "Ġtens": 16905, + "note": 16906, + "oubtedly": 16907, + "Ġunpre": 16908, + "Ġdialog": 16909, + "Af": 16910, + "Ġadapted": 16911, + "Ġsummar": 16912, + "Ġmul": 16913, + "ĠAaron": 16914, + "Ġindividually": 16915, + "rose": 16916, + "Ġrecycling": 16917, + "Ġcrap": 16918, + "Ġleaned": 16919, + "Tok": 16920, + "Ġpension": 16921, + "unde": 16922, + "Ġphysicians": 16923, + "Ġinterval": 16924, + "Ġsenza": 16925, + "nehmen": 16926, + "ĠLeft": 16927, + "North": 16928, + "ĠLag": 16929, + "Ġaccordingly": 16930, + "olt": 16931, + "ographical": 16932, + "Ġpork": 16933, + "alph": 16934, + "Ġtrag": 16935, + "Ġmatched": 16936, + "cert": 16937, + "Ġnewer": 16938, + "ATED": 16939, + "Ġsynthetic": 16940, + "Bon": 16941, + "Ġslim": 16942, + "Player": 16943, + "Ġkiller": 16944, + "stration": 16945, + "Ġqueries": 16946, + "aired": 16947, + "once": 16948, + "Pod": 16949, + "Ġbru": 16950, + "Ġbubble": 16951, + "Send": 16952, + "Ġmuss": 16953, + "Ġtrap": 16954, + "å¢": 16955, + "ĠSpa": 16956, + "Ġvessel": 16957, + "Ġcoding": 16958, + "Ġupt": 16959, + "Ġpunt": 16960, + "ĠStadium": 16961, + "Ġminerals": 16962, + "Ġsurprisingly": 16963, + "Ġoffline": 16964, + "Ġpracticing": 16965, + "nels": 16966, + "Ġcomplain": 16967, + "Ġwaar": 16968, + "urdy": 16969, + "Ġvalve": 16970, + "False": 16971, + "ĠHind": 16972, + "ĠSoph": 16973, + "ĠIM": 16974, + "Ġwrist": 16975, + "aines": 16976, + "Ġfarming": 16977, + "Ġqué": 16978, + "ysical": 16979, + "ĠPublished": 16980, + "Ġboil": 16981, + "ohyd": 16982, + "Ġtailored": 16983, + "ĠBring": 16984, + "because": 16985, + "ât": 16986, + "ĠĠĠĠĠĠĠĠĠĠĠĠ": 16987, + "closed": 16988, + "Ġlecture": 16989, + "Ġugly": 16990, + "Ġunderground": 16991, + "Ġgrill": 16992, + "ĠAgent": 16993, + "ĠhabÃŃa": 16994, + "ätt": 16995, + "ĠBos": 16996, + "Ġwrest": 16997, + "outheast": 16998, + "Ġstatistical": 16999, + "nah": 17000, + "ulu": 17001, + "ĠOpportun": 17002, + "Ġsized": 17003, + "ĠFix": 17004, + "BB": 17005, + "eted": 17006, + "Ġfridge": 17007, + "seg": 17008, + "iele": 17009, + "Ġapproaching": 17010, + "Ġeinf": 17011, + "Ġcongr": 17012, + "Ġmedian": 17013, + "Ġblast": 17014, + "Ġnovels": 17015, + "Ġthankful": 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17068, + "Ġfestiv": 17069, + "该": 17070, + "bag": 17071, + "Ġperspectives": 17072, + "ĠPrize": 17073, + "ĠMÃ": 17074, + "ĠprÃ": 17075, + "Ġgrades": 17076, + "Ġdeparture": 17077, + "Ġlistings": 17078, + "Ġescal": 17079, + "ĠKhan": 17080, + "888": 17081, + "vs": 17082, + "ĠGummies": 17083, + "Ġcircular": 17084, + "Ġtravelling": 17085, + "Ġmetals": 17086, + "Ġcreator": 17087, + "Ġmé": 17088, + "ĠVert": 17089, + "Ġhyg": 17090, + "*,": 17091, + "ĠMosc": 17092, + "Ġconstitu": 17093, + "uche": 17094, + "izon": 17095, + "Ġjustify": 17096, + "ĠZone": 17097, + "ĠGrowth": 17098, + "æ¢": 17099, + "Fore": 17100, + "ĠPalm": 17101, + "Ġslice": 17102, + "Ġdaughters": 17103, + "vec": 17104, + "Ġprotocols": 17105, + "match": 17106, + "town": 17107, + "Ġopponents": 17108, + "ĠRegular": 17109, + "Short": 17110, + "iko": 17111, + "brand": 17112, + "his": 17113, + "Ġbanned": 17114, + "ĠHomes": 17115, + "ĠGrey": 17116, + "ĠPrograms": 17117, + "Ġgloves": 17118, + "uent": 17119, + "ĠHawaii": 17120, + "iasm": 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"Ġmachen": 21677, + "ĠBangladesh": 21678, + "Ġmandate": 21679, + "Repository": 21680, + "Ġcontroversy": 21681, + "metry": 21682, + "ĠMathemat": 21683, + "reo": 21684, + "Ġimpair": 21685, + "Ġfreed": 21686, + "ĠEra": 21687, + "Ġincoming": 21688, + "ĊĊĉĉĉ": 21689, + "King": 21690, + "aru": 21691, + "Ġpse": 21692, + "Ġlac": 21693, + "Ġcelui": 21694, + "Ġrencont": 21695, + "ĠBaseball": 21696, + "halb": 21697, + "Ġanalyzing": 21698, + "Ġencuent": 21699, + "ĠTHAT": 21700, + "izarre": 21701, + "pun": 21702, + "iban": 21703, + "ilde": 21704, + "Ġbicycle": 21705, + "Ġwitch": 21706, + "Ġcompost": 21707, + "Ġcham": 21708, + "ilians": 21709, + "Ġyoungest": 21710, + "itime": 21711, + "anic": 21712, + "ĠMarkets": 21713, + "ĠListen": 21714, + "Ġforgiveness": 21715, + "ĠRuth": 21716, + "lage": 21717, + "Ġdarker": 21718, + "ĠFrequ": 21719, + "Ġbrains": 21720, + "zeug": 21721, + "Ġgates": 21722, + "NP": 21723, + "ĠAls": 21724, + "ND": 21725, + "TEST": 21726, + "ĠiTunes": 21727, + "æ¨": 21728, + "ĠRSS": 21729, + "ĠVolunte": 21730, + "Ġdoctrine": 21731, + "Ġmalware": 21732, + "Ġrecalled": 21733, + "esthes": 21734, + "Ġglorious": 21735, + "ĠHillary": 21736, + "clipse": 21737, + "Ġdere": 21738, + "frastructure": 21739, + "Ġwp": 21740, + "Ġroku": 21741, + "Ġdisclose": 21742, + "Ġhumidity": 21743, + "Para": 21744, + "ĠTW": 21745, + "ĠPero": 21746, + "ĠBBQ": 21747, + "æŃ¥": 21748, + "Ġdrown": 21749, + "åĨĻ": 21750, + "ĠMAN": 21751, + "Single": 21752, + "Language": 21753, + "bery": 21754, + "ĠSubs": 21755, + "Peter": 21756, + "Ġgrind": 21757, + "tar": 21758, + "riages": 21759, + "Ġviewer": 21760, + "driver": 21761, + "Ġmagnet": 21762, + "ĠLaboratory": 21763, + "Ġartif": 21764, + "amped": 21765, + "Ġharassment": 21766, + "Ġopera": 21767, + "Ġdeclaration": 21768, + "ikh": 21769, + "ĠBatman": 21770, + "Ġspatial": 21771, + "ĠDiagn": 21772, + "åĪ°äºĨ": 21773, + "Sem": 21774, + "rev": 21775, + "ĠEdwards": 21776, + "Ġmio": 21777, + "Ġcharter": 21778, + "Ġclearance": 21779, + "Ġdistinguished": 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"Ġapologize": 21832, + "sheet": 21833, + "ilon": 21834, + "emor": 21835, + "ĠLit": 21836, + "Ģå§ĭ": 21837, + "bral": 21838, + "success": 21839, + "educ": 21840, + "esto": 21841, + "Match": 21842, + "filename": 21843, + "ĠAdvertising": 21844, + "Ġpatches": 21845, + "ropract": 21846, + "空": 21847, + "dl": 21848, + "ĠPressure": 21849, + "ĠSheriff": 21850, + "Ġspicy": 21851, + "Icon": 21852, + "elesc": 21853, + "vine": 21854, + "ĠSI": 21855, + "ĠConcept": 21856, + "ĠCoin": 21857, + "Ġpatron": 21858, + "vÃŃ": 21859, + "Ġposters": 21860, + "Ġinsist": 21861, + "Ġacquiring": 21862, + "\"-": 21863, + "example": 21864, + "ĠDefault": 21865, + "