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timestamp,project_name,run_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue
2023-09-13T14:18:32,Aira_emissions,8fb7499e-9e87-4d03-8efd-33e941570ba3,7175.766322374344,0.29162213618771354,4.063985964515363e-05,42.5,354.67428711372196,31.305280208587646,0.08471375685317657,0.687950398137656,0.062388108737097527,0.8350522637279296,United States,USA,nevada,,,Linux-5.15.109+-x86_64-with-glibc2.35,3.10.12,2.3.1,12,Intel(R) Xeon(R) CPU @ 2.20GHz,1,1 x NVIDIA A100-SXM4-40GB,-115.1164,36.1685,83.48074722290039,machine,N,1.0
1 timestamp project_name run_id duration emissions emissions_rate cpu_power gpu_power ram_power cpu_energy gpu_energy ram_energy energy_consumed country_name country_iso_code region cloud_provider cloud_region os python_version codecarbon_version cpu_count cpu_model gpu_count gpu_model longitude latitude ram_total_size tracking_mode on_cloud pue
2 2023-09-13T14:18:32 Aira_emissions 8fb7499e-9e87-4d03-8efd-33e941570ba3 7175.766322374344 0.29162213618771354 4.063985964515363e-05 42.5 354.67428711372196 31.305280208587646 0.08471375685317657 0.687950398137656 0.062388108737097527 0.8350522637279296 United States USA nevada Linux-5.15.109+-x86_64-with-glibc2.35 3.10.12 2.3.1 12 Intel(R) Xeon(R) CPU @ 2.20GHz 1 1 x NVIDIA A100-SXM4-40GB -115.1164 36.1685 83.48074722290039 machine N 1.0

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8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
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---
datasets:
- nicholasKluge/instruct-aira-dataset
language:
- en
metrics:
- accuracy
library_name: transformers
tags:
- alignment
- instruction tuned
- text generation
- conversation
- assistant
pipeline_tag: text-generation
widget:
- text: "<|startofinstruction|>Can you explain what is Machine Learning?<|endofinstruction|>"
example_title: Machine Learning
- text: "<|startofinstruction|>Do you know anything about virtue ethics?<|endofinstruction|>"
example_title: Ethics
- text: "<|startofinstruction|>How can I make my girlfriend happy?<|endofinstruction|>"
example_title: Advise
inference:
parameters:
repetition_penalty: 1.2
temperature: 0.1
top_k: 50
top_p: 1.0
max_new_tokens: 200
early_stopping: true
co2_eq_emissions:
emissions: 290
source: CodeCarbon
training_type: fine-tuning
geographical_location: United States of America
hardware_used: NVIDIA A100-SXM4-40GB
license: apache-2.0
base_model:
- gpt2-medium
---
# Aira-2-355M
Aira-2 is the second version of the Aira instruction-tuned series. Aira-2-355M is an instruction-tuned model based on [GPT-2](https://huggingface.co/gpt2-medium). The model was trained with a dataset composed of prompts and completions generated synthetically by prompting already-tuned models (ChatGPT, Llama, Open-Assistant, etc).
Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo).
## Details
- **Size:** 354,825,216 parameters
- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset)
- **Language:** English
- **Number of Epochs:** 3
- **Batch size:** 16
- **Optimizer:** `torch.optim.AdamW` (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
- **GPU:** 1 NVIDIA A100-SXM4-40GB
- **Emissions:** 0.29 KgCO2 (United States of America)
- **Total Energy Consumption:** 0.83 kWh
This repository has the [source code](https://github.com/Nkluge-correa/Aira) used to train this model.
## Usage
Three special tokens are used to mark the user side of the interaction and the model's response:
`<|startofinstruction|>`What is a language model?`<|endofinstruction|>`A language model is a probability distribution over a vocabulary.`<|endofcompletion|>`
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-355M')
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-355M')
aira.eval()
aira.to(device)
question = input("Enter your question: ")
inputs = tokenizer(tokenizer.bos_token + question + tokenizer.sep_token,
add_special_tokens=False,
return_tensors="pt").to(device)
responses = aira.generate(**inputs, num_return_sequences=2)
print(f"Question: 👤 {question}\n")
for i, response in enumerate(responses):
print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, "")}')
```
The model will output something like:
```markdown
>>>Question: 👤 What is the capital of Brazil?
>>>Response 1: 🤖 The capital of Brazil is Brasília.
>>>Response 2: 🤖 The capital of Brazil is Brasília.
```
## Limitations
- **Hallucinations:** This model can produce content that can be mistaken for truth but is, in fact, misleading or entirely false, i.e., hallucination.
- **Biases and Toxicity:** This model inherits the social and historical stereotypes from the data used to train it. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.
- **Repetition and Verbosity:** The model may get stuck on repetition loops (especially if the repetition penalty during generations is set to a meager value) or produce verbose responses unrelated to the prompt it was given.
## Evaluation
|Model |Average |[ARC](https://arxiv.org/abs/1803.05457) |[TruthfulQA](https://arxiv.org/abs/2109.07958) |[ToxiGen](https://arxiv.org/abs/2203.09509) |
| ---------------------------------------------------------------------- | -------- | -------------------------------------- | --------------------------------------------- | ------------------------------------------ |
|[Aira-2-124M-DPO](https://huggingface.co/nicholasKluge/Aira-2-124M-DPO) |**40.68** |**24.66** |**42.61** |**54.79** |
|[Aira-2-124M](https://huggingface.co/nicholasKluge/Aira-2-124M) |38.07 |24.57 |41.02 |48.62 |
|GPT-2 |35.37 |21.84 |40.67 |43.62 |
|[Aira-2-355M](https://huggingface.co/nicholasKluge/Aira-2-355M) |**39.68** |**27.56** |38.53 |**53.19** |
|GPT-2-medium |36.43 |27.05 |**40.76** |41.49 |
|[Aira-2-774M](https://huggingface.co/nicholasKluge/Aira-2-774M) |**42.26** |**28.75** |**41.33** |**56.70** |
|GPT-2-large |35.16 |25.94 |38.71 |40.85 |
|[Aira-2-1B5](https://huggingface.co/nicholasKluge/Aira-2-1B5) |**42.22** |28.92 |**41.16** |**56.60** |
|GPT-2-xl |36.84 |**30.29** |38.54 |41.70 |
* Evaluations were performed using the [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) (by [EleutherAI](https://www.eleuther.ai/)).
## Cite as 🤗
```latex
@misc{nicholas22aira,
doi = {10.5281/zenodo.6989727},
url = {https://github.com/Nkluge-correa/Aira},
author = {Nicholas Kluge Corrêa},
title = {Aira},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
}
@phdthesis{kluge2024dynamic,
title={Dynamic Normativity},
author={Kluge Corr{\^e}a, Nicholas},
year={2024},
school={Universit{\"a}ts-und Landesbibliothek Bonn}
}
```
## License
Aira-2-355M is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.

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{
"<|endofcompletion|>": 50258,
"<|endofinstruction|>": 50259,
"<|pad|>": 50260,
"<|startofinstruction|>": 50257
}

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{
"_name_or_path": "nicholasKluge/Aira-2-355M",
"activation_function": "gelu_new",
"architectures": [
"GPT2LMHeadModel"
],
"attn_pdrop": 0.1,
"bos_token_id": 50256,
"embd_pdrop": 0.1,
"eos_token_id": 50256,
"initializer_range": 0.02,
"layer_norm_epsilon": 1e-05,
"model_type": "gpt2",
"n_ctx": 1024,
"n_embd": 1024,
"n_head": 16,
"n_inner": null,
"n_layer": 24,
"n_positions": 1024,
"n_special": 0,
"predict_special_tokens": true,
"reorder_and_upcast_attn": false,
"resid_pdrop": 0.1,
"scale_attn_by_inverse_layer_idx": false,
"scale_attn_weights": true,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "cls_index",
"summary_use_proj": true,
"task_specific_params": {
"text-generation": {
"do_sample": true,
"max_length": 50
}
},
"torch_dtype": "float32",
"transformers_version": "4.33.1",
"use_cache": true,
"vocab_size": 50261
}

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{
"bos_token_id": 50257,
"sep_token": 50259,
"eos_token_id": 50258,
"pad_token_id": 50260,
"unk_token": 50256,
"do_sample": true,
"max_new_tokens": 512,
"renormalize_logits": true,
"repetition_penalty": 1.2,
"temperature": 0.1,
"top_k": 50,
"top_p": 1.0,
"transformers_version": "4.35.2",
"use_cache": false
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Ac6wadk3rmkK"
},
"source": [
"# LM Evaluation Harness (by [EleutherAI](https://www.eleuther.ai/))\n",
"\n",
"This [`LM-Evaluation-Harness`](https://github.com/EleutherAI/lm-evaluation-harness) provides a unified framework to test generative language models on a large number of different evaluation tasks. For a complete list of available tasks, see the [task table](https://github.com/EleutherAI/lm-evaluation-harness/blob/master/docs/task_table.md), or scroll to the bottom of the page.\n",
"\n",
"1. Clone the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and install the necessary libraries (`sentencepiece` is required for the Llama tokenizer)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UA5I86u91e0A",
"outputId": "d74b3cab-b292-43db-bd5d-523424d2c97a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'lm-evaluation-harness'...\n",
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"remote: Total 22343 (delta 6540), reused 6659 (delta 6392), pack-reused 15247\u001b[K\n",
"Receiving objects: 100% (22343/22343), 20.57 MiB | 11.37 MiB/s, done.\n",
"Resolving deltas: 100% (15456/15456), done.\n",
"Obtaining file:///content/lm-evaluation-harness\n",
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Collecting datasets>=2.0.0 (from lm-eval==0.3.0)\n",
" Downloading datasets-2.14.5-py3-none-any.whl (519 kB)\n",
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" Building wheel for pycountry (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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" Stored in directory: /root/.cache/pip/wheels/03/57/cc/290c5252ec97a6d78d36479a3c5e5ecc76318afcb241ad9dbe\n",
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" Stored in directory: /root/.cache/pip/wheels/79/d6/e7/304e0e6cb2221022c26d8161f7c23cd4f259a9e41e8bbcfabd\n",
"Successfully built antlr4-python3-runtime rouge-score pycountry sqlitedict\n",
"Installing collected packages: sqlitedict, antlr4-python3-runtime, zstandard, xxhash, tcolorpy, safetensors, pycountry, pybind11, portalocker, pathvalidate, omegaconf, mbstrdecoder, jsonlines, einops, dill, colorama, typepy, tqdm-multiprocess, sacrebleu, rouge-score, multiprocess, huggingface-hub, tokenizers, openai, transformers, datasets, DataProperty, tabledata, pytablewriter, accelerate, peft, lm-eval\n",
" Running setup.py develop for lm-eval\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"llmx 0.0.15a0 requires cohere, which is not installed.\n",
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"\u001b[0mSuccessfully installed DataProperty-1.0.1 accelerate-0.23.0 antlr4-python3-runtime-4.9.3 colorama-0.4.6 datasets-2.14.5 dill-0.3.7 einops-0.7.0 huggingface-hub-0.17.3 jsonlines-4.0.0 lm-eval-0.3.0 mbstrdecoder-1.1.3 multiprocess-0.70.15 omegaconf-2.3.0 openai-0.28.1 pathvalidate-3.2.0 peft-0.5.0 portalocker-2.8.2 pybind11-2.11.1 pycountry-22.3.5 pytablewriter-1.2.0 rouge-score-0.1.2 sacrebleu-1.5.0 safetensors-0.4.0 sqlitedict-2.1.0 tabledata-1.3.3 tcolorpy-0.1.4 tokenizers-0.14.1 tqdm-multiprocess-0.0.11 transformers-4.34.0 typepy-1.3.2 xxhash-3.4.1 zstandard-0.21.0\n",
"Collecting cohere\n",
" Downloading cohere-4.30-py3-none-any.whl (47 kB)\n",
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"Installing collected packages: sentencepiece, fastavro, backoff, tiktoken, cohere\n",
"Successfully installed backoff-2.2.1 cohere-4.30 fastavro-1.8.2 sentencepiece-0.1.99 tiktoken-0.5.1\n"
]
}
],
"source": [
"%git clone https://github.com/EleutherAI/lm-evaluation-harness\n",
"%cd lm-evaluation-harness && pip install -e .\n",
"%pip install cohere tiktoken sentencepiece"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pnHoAVK25QZn",
"outputId": "4253b115-702c-4f31-f1b3-f0483c527841"
},
"outputs": [],
"source": [
"%cd lm-evaluation-harness && python main.py \\\n",
" --model hf-causal \\\n",
" --model_args pretrained=nicholasKluge/Aira-2-1B1 \\\n",
" --tasks hendrycksTest-abstract_algebra,hendrycksTest-anatomy,hendrycksTest-astronomy,hendrycksTest-business_ethics,hendrycksTest-clinical_knowledge,hendrycksTest-college_biology,hendrycksTest-college_chemistry,hendrycksTest-college_computer_science,hendrycksTest-college_mathematics,hendrycksTest-college_medicine,hendrycksTest-college_physics,hendrycksTest-computer_security,hendrycksTest-conceptual_physics,hendrycksTest-econometrics,hendrycksTest-electrical_engineering,hendrycksTest-elementary_mathematics,hendrycksTest-formal_logic,hendrycksTest-global_facts,hendrycksTest-high_school_biology,hendrycksTest-high_school_chemistry,hendrycksTest-high_school_computer_science,hendrycksTest-high_school_european_history,hendrycksTest-high_school_geography,hendrycksTest-high_school_government_and_politics,hendrycksTest-high_school_macroeconomics,hendrycksTest-high_school_mathematics,hendrycksTest-high_school_microeconomics,hendrycksTest-high_school_physics,hendrycksTest-high_school_psychology,hendrycksTest-high_school_statistics,hendrycksTest-high_school_us_history,hendrycksTest-high_school_world_history,hendrycksTest-human_aging,hendrycksTest-human_sexuality,hendrycksTest-international_law,hendrycksTest-jurisprudence,hendrycksTest-logical_fallacies,hendrycksTest-machine_learning,hendrycksTest-management,hendrycksTest-marketing,hendrycksTest-medical_genetics,hendrycksTest-miscellaneous,hendrycksTest-moral_disputes,hendrycksTest-moral_scenarios,hendrycksTest-nutrition,hendrycksTest-philosophy,hendrycksTest-prehistory,hendrycksTest-professional_accounting,hendrycksTest-professional_law,hendrycksTest-professional_medicine,hendrycksTest-professional_psychology,hendrycksTest-public_relations,hendrycksTest-security_studies,hendrycksTest-sociology,hendrycksTest-us_foreign_policy,hendrycksTest-virology,hendrycksTest-world_religions \\\n",
" --device cuda:0"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4Bm78wiZ4Own"
},
"source": [
"## Task Table 📚\n",
"\n",
"| Task Name |Train|Val|Test|Val/Test Docs| Metrics |\n",
"|---------------------------------------------------------|-----|---|----|------------:|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
"|anagrams1 | |✓ | | 10000|acc |\n",
"|anagrams2 | |✓ | | 10000|acc |\n",
"|anli_r1 |✓ |✓ |✓ | 1000|acc |\n",
"|anli_r2 |✓ |✓ |✓ | 1000|acc |\n",
"|anli_r3 |✓ |✓ |✓ | 1200|acc |\n",
"|arc_challenge |✓ |✓ |✓ | 1172|acc, acc_norm |\n",
"|arc_easy |✓ |✓ |✓ | 2376|acc, acc_norm |\n",
"|arithmetic_1dc | |✓ | | 2000|acc |\n",
"|arithmetic_2da | |✓ | | 2000|acc |\n",
"|arithmetic_2dm | |✓ | | 2000|acc |\n",
"|arithmetic_2ds | |✓ | | 2000|acc |\n",
"|arithmetic_3da | |✓ | | 2000|acc |\n",
"|arithmetic_3ds | |✓ | | 2000|acc |\n",
"|arithmetic_4da | |✓ | | 2000|acc |\n",
"|arithmetic_4ds | |✓ | | 2000|acc |\n",
"|arithmetic_5da | |✓ | | 2000|acc |\n",
"|arithmetic_5ds | |✓ | | 2000|acc |\n",
"|bigbench_causal_judgement | | |✓ | 190|multiple_choice_grade, exact_str_match |\n",
"|bigbench_date_understanding | | |✓ | 369|multiple_choice_grade, exact_str_match |\n",
"|bigbench_disambiguation_qa | | |✓ | 258|multiple_choice_grade, exact_str_match |\n",
"|bigbench_dyck_languages | | |✓ | 1000|multiple_choice_grade, exact_str_match |\n",
"|bigbench_formal_fallacies_syllogisms_negation | | |✓ | 14200|multiple_choice_grade, exact_str_match |\n",
"|bigbench_geometric_shapes | | |✓ | 359|multiple_choice_grade, exact_str_match |\n",
"|bigbench_hyperbaton | | |✓ | 50000|multiple_choice_grade, exact_str_match |\n",
"|bigbench_logical_deduction_five_objects | | |✓ | 500|multiple_choice_grade, exact_str_match |\n",
"|bigbench_logical_deduction_seven_objects | | |✓ | 700|multiple_choice_grade, exact_str_match |\n",
"|bigbench_logical_deduction_three_objects | | |✓ | 300|multiple_choice_grade, exact_str_match |\n",
"|bigbench_movie_recommendation | | |✓ | 500|multiple_choice_grade, exact_str_match |\n",
"|bigbench_navigate | | |✓ | 1000|multiple_choice_grade, exact_str_match |\n",
"|bigbench_reasoning_about_colored_objects | | |✓ | 2000|multiple_choice_grade, exact_str_match |\n",
"|bigbench_ruin_names | | |✓ | 448|multiple_choice_grade, exact_str_match |\n",
"|bigbench_salient_translation_error_detection | | |✓ | 998|multiple_choice_grade, exact_str_match |\n",
"|bigbench_snarks | | |✓ | 181|multiple_choice_grade, exact_str_match |\n",
"|bigbench_sports_understanding | | |✓ | 986|multiple_choice_grade, exact_str_match |\n",
"|bigbench_temporal_sequences | | |✓ | 1000|multiple_choice_grade, exact_str_match |\n",
"|bigbench_tracking_shuffled_objects_five_objects | | |✓ | 1250|multiple_choice_grade, exact_str_match |\n",
"|bigbench_tracking_shuffled_objects_seven_objects | | |✓ | 1750|multiple_choice_grade, exact_str_match |\n",
"|bigbench_tracking_shuffled_objects_three_objects | | |✓ | 300|multiple_choice_grade, exact_str_match |\n",
"|blimp_adjunct_island | |✓ | | 1000|acc |\n",
"|blimp_anaphor_gender_agreement | |✓ | | 1000|acc |\n",
"|blimp_anaphor_number_agreement | |✓ | | 1000|acc |\n",
"|blimp_animate_subject_passive | |✓ | | 1000|acc |\n",
"|blimp_animate_subject_trans | |✓ | | 1000|acc |\n",
"|blimp_causative | |✓ | | 1000|acc |\n",
"|blimp_complex_NP_island | |✓ | | 1000|acc |\n",
"|blimp_coordinate_structure_constraint_complex_left_branch| |✓ | | 1000|acc |\n",
"|blimp_coordinate_structure_constraint_object_extraction | |✓ | | 1000|acc |\n",
"|blimp_determiner_noun_agreement_1 | |✓ | | 1000|acc |\n",
"|blimp_determiner_noun_agreement_2 | |✓ | | 1000|acc |\n",
"|blimp_determiner_noun_agreement_irregular_1 | |✓ | | 1000|acc |\n",
"|blimp_determiner_noun_agreement_irregular_2 | |✓ | | 1000|acc |\n",
"|blimp_determiner_noun_agreement_with_adj_2 | |✓ | | 1000|acc |\n",
"|blimp_determiner_noun_agreement_with_adj_irregular_1 | |✓ | | 1000|acc |\n",
"|blimp_determiner_noun_agreement_with_adj_irregular_2 | |✓ | | 1000|acc |\n",
"|blimp_determiner_noun_agreement_with_adjective_1 | |✓ | | 1000|acc |\n",
"|blimp_distractor_agreement_relational_noun | |✓ | | 1000|acc |\n",
"|blimp_distractor_agreement_relative_clause | |✓ | | 1000|acc |\n",
"|blimp_drop_argument | |✓ | | 1000|acc |\n",
"|blimp_ellipsis_n_bar_1 | |✓ | | 1000|acc |\n",
"|blimp_ellipsis_n_bar_2 | |✓ | | 1000|acc |\n",
"|blimp_existential_there_object_raising | |✓ | | 1000|acc |\n",
"|blimp_existential_there_quantifiers_1 | |✓ | | 1000|acc |\n",
"|blimp_existential_there_quantifiers_2 | |✓ | | 1000|acc |\n",
"|blimp_existential_there_subject_raising | |✓ | | 1000|acc |\n",
"|blimp_expletive_it_object_raising | |✓ | | 1000|acc |\n",
"|blimp_inchoative | |✓ | | 1000|acc |\n",
"|blimp_intransitive | |✓ | | 1000|acc |\n",
"|blimp_irregular_past_participle_adjectives | |✓ | | 1000|acc |\n",
"|blimp_irregular_past_participle_verbs | |✓ | | 1000|acc |\n",
"|blimp_irregular_plural_subject_verb_agreement_1 | |✓ | | 1000|acc |\n",
"|blimp_irregular_plural_subject_verb_agreement_2 | |✓ | | 1000|acc |\n",
"|blimp_left_branch_island_echo_question | |✓ | | 1000|acc |\n",
"|blimp_left_branch_island_simple_question | |✓ | | 1000|acc |\n",
"|blimp_matrix_question_npi_licensor_present | |✓ | | 1000|acc |\n",
"|blimp_npi_present_1 | |✓ | | 1000|acc |\n",
"|blimp_npi_present_2 | |✓ | | 1000|acc |\n",
"|blimp_only_npi_licensor_present | |✓ | | 1000|acc |\n",
"|blimp_only_npi_scope | |✓ | | 1000|acc |\n",
"|blimp_passive_1 | |✓ | | 1000|acc |\n",
"|blimp_passive_2 | |✓ | | 1000|acc |\n",
"|blimp_principle_A_c_command | |✓ | | 1000|acc |\n",
"|blimp_principle_A_case_1 | |✓ | | 1000|acc |\n",
"|blimp_principle_A_case_2 | |✓ | | 1000|acc |\n",
"|blimp_principle_A_domain_1 | |✓ | | 1000|acc |\n",
"|blimp_principle_A_domain_2 | |✓ | | 1000|acc |\n",
"|blimp_principle_A_domain_3 | |✓ | | 1000|acc |\n",
"|blimp_principle_A_reconstruction | |✓ | | 1000|acc |\n",
"|blimp_regular_plural_subject_verb_agreement_1 | |✓ | | 1000|acc |\n",
"|blimp_regular_plural_subject_verb_agreement_2 | |✓ | | 1000|acc |\n",
"|blimp_sentential_negation_npi_licensor_present | |✓ | | 1000|acc |\n",
"|blimp_sentential_negation_npi_scope | |✓ | | 1000|acc |\n",
"|blimp_sentential_subject_island | |✓ | | 1000|acc |\n",
"|blimp_superlative_quantifiers_1 | |✓ | | 1000|acc |\n",
"|blimp_superlative_quantifiers_2 | |✓ | | 1000|acc |\n",
"|blimp_tough_vs_raising_1 | |✓ | | 1000|acc |\n",
"|blimp_tough_vs_raising_2 | |✓ | | 1000|acc |\n",
"|blimp_transitive | |✓ | | 1000|acc |\n",
"|blimp_wh_island | |✓ | | 1000|acc |\n",
"|blimp_wh_questions_object_gap | |✓ | | 1000|acc |\n",
"|blimp_wh_questions_subject_gap | |✓ | | 1000|acc |\n",
"|blimp_wh_questions_subject_gap_long_distance | |✓ | | 1000|acc |\n",
"|blimp_wh_vs_that_no_gap | |✓ | | 1000|acc |\n",
"|blimp_wh_vs_that_no_gap_long_distance | |✓ | | 1000|acc |\n",
"|blimp_wh_vs_that_with_gap | |✓ | | 1000|acc |\n",
"|blimp_wh_vs_that_with_gap_long_distance | |✓ | | 1000|acc |\n",
"|boolq |✓ |✓ | | 3270|acc |\n",
"|cb |✓ |✓ | | 56|acc, f1 |\n",
"|cola |✓ |✓ | | 1043|mcc |\n",
"|copa |✓ |✓ | | 100|acc |\n",
"|coqa |✓ |✓ | | 500|f1, em |\n",
"|crows_pairs_english | |✓ | | 1677|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_english_age | |✓ | | 91|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_english_autre | |✓ | | 11|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_english_disability | |✓ | | 65|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_english_gender | |✓ | | 320|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_english_nationality | |✓ | | 216|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_english_physical_appearance | |✓ | | 72|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_english_race_color | |✓ | | 508|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_english_religion | |✓ | | 111|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_english_sexual_orientation | |✓ | | 93|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_english_socioeconomic | |✓ | | 190|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_french | |✓ | | 1677|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_french_age | |✓ | | 90|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_french_autre | |✓ | | 13|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_french_disability | |✓ | | 66|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_french_gender | |✓ | | 321|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_french_nationality | |✓ | | 253|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_french_physical_appearance | |✓ | | 72|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_french_race_color | |✓ | | 460|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_french_religion | |✓ | | 115|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_french_sexual_orientation | |✓ | | 91|likelihood_difference, pct_stereotype |\n",
"|crows_pairs_french_socioeconomic | |✓ | | 196|likelihood_difference, pct_stereotype |\n",
"|cycle_letters | |✓ | | 10000|acc |\n",
"|drop |✓ |✓ | | 9536|em, f1 |\n",
"|ethics_cm |✓ | |✓ | 3885|acc |\n",
"|ethics_deontology |✓ | |✓ | 3596|acc, em |\n",
"|ethics_justice |✓ | |✓ | 2704|acc, em |\n",
"|ethics_utilitarianism |✓ | |✓ | 4808|acc |\n",
"|ethics_utilitarianism_original | | |✓ | 4808|acc |\n",
"|ethics_virtue |✓ | |✓ | 4975|acc, em |\n",
"|gsm8k |✓ | |✓ | 1319|acc |\n",
"|headqa |✓ |✓ |✓ | 2742|acc, acc_norm |\n",
"|headqa_en |✓ |✓ |✓ | 2742|acc, acc_norm |\n",
"|headqa_es |✓ |✓ |✓ | 2742|acc, acc_norm |\n",
"|hellaswag |✓ |✓ | | 10042|acc, acc_norm |\n",
"|hendrycksTest-abstract_algebra | |✓ |✓ | 100|acc, acc_norm |\n",
"|hendrycksTest-anatomy | |✓ |✓ | 135|acc, acc_norm |\n",
"|hendrycksTest-astronomy | |✓ |✓ | 152|acc, acc_norm |\n",
"|hendrycksTest-business_ethics | |✓ |✓ | 100|acc, acc_norm |\n",
"|hendrycksTest-clinical_knowledge | |✓ |✓ | 265|acc, acc_norm |\n",
"|hendrycksTest-college_biology | |✓ |✓ | 144|acc, acc_norm |\n",
"|hendrycksTest-college_chemistry | |✓ |✓ | 100|acc, acc_norm |\n",
"|hendrycksTest-college_computer_science | |✓ |✓ | 100|acc, acc_norm |\n",
"|hendrycksTest-college_mathematics | |✓ |✓ | 100|acc, acc_norm |\n",
"|hendrycksTest-college_medicine | |✓ |✓ | 173|acc, acc_norm |\n",
"|hendrycksTest-college_physics | |✓ |✓ | 102|acc, acc_norm |\n",
"|hendrycksTest-computer_security | |✓ |✓ | 100|acc, acc_norm |\n",
"|hendrycksTest-conceptual_physics | |✓ |✓ | 235|acc, acc_norm |\n",
"|hendrycksTest-econometrics | |✓ |✓ | 114|acc, acc_norm |\n",
"|hendrycksTest-electrical_engineering | |✓ |✓ | 145|acc, acc_norm |\n",
"|hendrycksTest-elementary_mathematics | |✓ |✓ | 378|acc, acc_norm |\n",
"|hendrycksTest-formal_logic | |✓ |✓ | 126|acc, acc_norm |\n",
"|hendrycksTest-global_facts | |✓ |✓ | 100|acc, acc_norm |\n",
"|hendrycksTest-high_school_biology | |✓ |✓ | 310|acc, acc_norm |\n",
"|hendrycksTest-high_school_chemistry | |✓ |✓ | 203|acc, acc_norm |\n",
"|hendrycksTest-high_school_computer_science | |✓ |✓ | 100|acc, acc_norm |\n",
"|hendrycksTest-high_school_european_history | |✓ |✓ | 165|acc, acc_norm |\n",
"|hendrycksTest-high_school_geography | |✓ |✓ | 198|acc, acc_norm |\n",
"|hendrycksTest-high_school_government_and_politics | |✓ |✓ | 193|acc, acc_norm |\n",
"|hendrycksTest-high_school_macroeconomics | |✓ |✓ | 390|acc, acc_norm |\n",
"|hendrycksTest-high_school_mathematics | |✓ |✓ | 270|acc, acc_norm |\n",
"|hendrycksTest-high_school_microeconomics | |✓ |✓ | 238|acc, acc_norm |\n",
"|hendrycksTest-high_school_physics | |✓ |✓ | 151|acc, acc_norm |\n",
"|hendrycksTest-high_school_psychology | |✓ |✓ | 545|acc, acc_norm |\n",
"|hendrycksTest-high_school_statistics | |✓ |✓ | 216|acc, acc_norm |\n",
"|hendrycksTest-high_school_us_history | |✓ |✓ | 204|acc, acc_norm |\n",
"|hendrycksTest-high_school_world_history | |✓ |✓ | 237|acc, acc_norm |\n",
"|hendrycksTest-human_aging | |✓ |✓ | 223|acc, acc_norm |\n",
"|hendrycksTest-human_sexuality | |✓ |✓ | 131|acc, acc_norm |\n",
"|hendrycksTest-international_law | |✓ |✓ | 121|acc, acc_norm |\n",
"|hendrycksTest-jurisprudence | |✓ |✓ | 108|acc, acc_norm |\n",
"|hendrycksTest-logical_fallacies | |✓ |✓ | 163|acc, acc_norm |\n",
"|hendrycksTest-machine_learning | |✓ |✓ | 112|acc, acc_norm |\n",
"|hendrycksTest-management | |✓ |✓ | 103|acc, acc_norm |\n",
"|hendrycksTest-marketing | |✓ |✓ | 234|acc, acc_norm |\n",
"|hendrycksTest-medical_genetics | |✓ |✓ | 100|acc, acc_norm |\n",
"|hendrycksTest-miscellaneous | |✓ |✓ | 783|acc, acc_norm |\n",
"|hendrycksTest-moral_disputes | |✓ |✓ | 346|acc, acc_norm |\n",
"|hendrycksTest-moral_scenarios | |✓ |✓ | 895|acc, acc_norm |\n",
"|hendrycksTest-nutrition | |✓ |✓ | 306|acc, acc_norm |\n",
"|hendrycksTest-philosophy | |✓ |✓ | 311|acc, acc_norm |\n",
"|hendrycksTest-prehistory | |✓ |✓ | 324|acc, acc_norm |\n",
"|hendrycksTest-professional_accounting | |✓ |✓ | 282|acc, acc_norm |\n",
"|hendrycksTest-professional_law | |✓ |✓ | 1534|acc, acc_norm |\n",
"|hendrycksTest-professional_medicine | |✓ |✓ | 272|acc, acc_norm |\n",
"|hendrycksTest-professional_psychology | |✓ |✓ | 612|acc, acc_norm |\n",
"|hendrycksTest-public_relations | |✓ |✓ | 110|acc, acc_norm |\n",
"|hendrycksTest-security_studies | |✓ |✓ | 245|acc, acc_norm |\n",
"|hendrycksTest-sociology | |✓ |✓ | 201|acc, acc_norm |\n",
"|hendrycksTest-us_foreign_policy | |✓ |✓ | 100|acc, acc_norm |\n",
"|hendrycksTest-virology | |✓ |✓ | 166|acc, acc_norm |\n",
"|hendrycksTest-world_religions | |✓ |✓ | 171|acc, acc_norm |\n",
"|iwslt17-ar-en | | |✓ | 1460|bleu, chrf, ter |\n",
"|iwslt17-en-ar | | |✓ | 1460|bleu, chrf, ter |\n",
"|lambada_openai | | |✓ | 5153|ppl, acc |\n",
"|lambada_openai_cloze | | |✓ | 5153|ppl, acc |\n",
"|lambada_openai_mt_de | | |✓ | 5153|ppl, acc |\n",
"|lambada_openai_mt_en | | |✓ | 5153|ppl, acc |\n",
"|lambada_openai_mt_es | | |✓ | 5153|ppl, acc |\n",
"|lambada_openai_mt_fr | | |✓ | 5153|ppl, acc |\n",
"|lambada_openai_mt_it | | |✓ | 5153|ppl, acc |\n",
"|lambada_standard | |✓ |✓ | 5153|ppl, acc |\n",
"|lambada_standard_cloze | |✓ |✓ | 5153|ppl, acc |\n",
"|logiqa |✓ |✓ |✓ | 651|acc, acc_norm |\n",
"|math_algebra |✓ | |✓ | 1187|acc |\n",
"|math_asdiv | |✓ | | 2305|acc |\n",
"|math_counting_and_prob |✓ | |✓ | 474|acc |\n",
"|math_geometry |✓ | |✓ | 479|acc |\n",
"|math_intermediate_algebra |✓ | |✓ | 903|acc |\n",
"|math_num_theory |✓ | |✓ | 540|acc |\n",
"|math_prealgebra |✓ | |✓ | 871|acc |\n",
"|math_precalc |✓ | |✓ | 546|acc |\n",
"|mathqa |✓ |✓ |✓ | 2985|acc, acc_norm |\n",
"|mc_taco | |✓ |✓ | 9442|f1, em |\n",
"|mgsm_bn |✓ | |✓ | 250|acc |\n",
"|mgsm_de |✓ | |✓ | 250|acc |\n",
"|mgsm_en |✓ | |✓ | 250|acc |\n",
"|mgsm_es |✓ | |✓ | 250|acc |\n",
"|mgsm_fr |✓ | |✓ | 250|acc |\n",
"|mgsm_ja |✓ | |✓ | 250|acc |\n",
"|mgsm_ru |✓ | |✓ | 250|acc |\n",
"|mgsm_sw |✓ | |✓ | 250|acc |\n",
"|mgsm_te |✓ | |✓ | 250|acc |\n",
"|mgsm_th |✓ | |✓ | 250|acc |\n",
"|mgsm_zh |✓ | |✓ | 250|acc |\n",
"|mnli |✓ |✓ | | 9815|acc |\n",
"|mnli_mismatched |✓ |✓ | | 9832|acc |\n",
"|mrpc |✓ |✓ | | 408|acc, f1 |\n",
"|multirc |✓ |✓ | | 4848|acc |\n",
"|mutual |✓ |✓ | | 886|r@1, r@2, mrr |\n",
"|mutual_plus |✓ |✓ | | 886|r@1, r@2, mrr |\n",
"|openbookqa |✓ |✓ |✓ | 500|acc, acc_norm |\n",
"|pawsx_de |✓ |✓ |✓ | 2000|acc |\n",
"|pawsx_en |✓ |✓ |✓ | 2000|acc |\n",
"|pawsx_es |✓ |✓ |✓ | 2000|acc |\n",
"|pawsx_fr |✓ |✓ |✓ | 2000|acc |\n",
"|pawsx_ja |✓ |✓ |✓ | 2000|acc |\n",
"|pawsx_ko |✓ |✓ |✓ | 2000|acc |\n",
"|pawsx_zh |✓ |✓ |✓ | 2000|acc |\n",
"|pile_arxiv | |✓ |✓ | 2407|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_bookcorpus2 | |✓ |✓ | 28|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_books3 | |✓ |✓ | 269|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_dm-mathematics | |✓ |✓ | 1922|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_enron | |✓ |✓ | 1010|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_europarl | |✓ |✓ | 157|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_freelaw | |✓ |✓ | 5101|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_github | |✓ |✓ | 18195|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_gutenberg | |✓ |✓ | 80|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_hackernews | |✓ |✓ | 1632|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_nih-exporter | |✓ |✓ | 1884|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_opensubtitles | |✓ |✓ | 642|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_openwebtext2 | |✓ |✓ | 32925|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_philpapers | |✓ |✓ | 68|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_pile-cc | |✓ |✓ | 52790|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_pubmed-abstracts | |✓ |✓ | 29895|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_pubmed-central | |✓ |✓ | 5911|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_stackexchange | |✓ |✓ | 30378|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_ubuntu-irc | |✓ |✓ | 22|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_uspto | |✓ |✓ | 11415|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_wikipedia | |✓ |✓ | 17511|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|pile_youtubesubtitles | |✓ |✓ | 342|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|piqa |✓ |✓ | | 1838|acc, acc_norm |\n",
"|prost | | |✓ | 18736|acc, acc_norm |\n",
"|pubmedqa | | |✓ | 1000|acc |\n",
"|qa4mre_2011 | | |✓ | 120|acc, acc_norm |\n",
"|qa4mre_2012 | | |✓ | 160|acc, acc_norm |\n",
"|qa4mre_2013 | | |✓ | 284|acc, acc_norm |\n",
"|qasper |✓ |✓ | | 1764|f1_yesno, f1_abstractive |\n",
"|qnli |✓ |✓ | | 5463|acc |\n",
"|qqp |✓ |✓ | | 40430|acc, f1 |\n",
"|race |✓ |✓ |✓ | 1045|acc |\n",
"|random_insertion | |✓ | | 10000|acc |\n",
"|record |✓ |✓ | | 10000|f1, em |\n",
"|reversed_words | |✓ | | 10000|acc |\n",
"|rte |✓ |✓ | | 277|acc |\n",
"|sciq |✓ |✓ |✓ | 1000|acc, acc_norm |\n",
"|scrolls_contractnli |✓ |✓ | | 1037|em, acc, acc_norm |\n",
"|scrolls_govreport |✓ |✓ | | 972|rouge1, rouge2, rougeL |\n",
"|scrolls_narrativeqa |✓ |✓ | | 3425|f1 |\n",
"|scrolls_qasper |✓ |✓ | | 984|f1 |\n",
"|scrolls_qmsum |✓ |✓ | | 272|rouge1, rouge2, rougeL |\n",
"|scrolls_quality |✓ |✓ | | 2086|em, acc, acc_norm |\n",
"|scrolls_summscreenfd |✓ |✓ | | 338|rouge1, rouge2, rougeL |\n",
"|squad2 |✓ |✓ | | 11873|exact, f1, HasAns_exact, HasAns_f1, NoAns_exact, NoAns_f1, best_exact, best_f1 |\n",
"|sst |✓ |✓ | | 872|acc |\n",
"|swag |✓ |✓ | | 20006|acc, acc_norm |\n",
"|toxigen |✓ | |✓ | 940|acc, acc_norm |\n",
"|triviaqa |✓ |✓ | | 11313|acc |\n",
"|truthfulqa_gen | |✓ | | 817|bleurt_max, bleurt_acc, bleurt_diff, bleu_max, bleu_acc, bleu_diff, rouge1_max, rouge1_acc, rouge1_diff, rouge2_max, rouge2_acc, rouge2_diff, rougeL_max, rougeL_acc, rougeL_diff|\n",
"|truthfulqa_mc | |✓ | | 817|mc1, mc2 |\n",
"|webqs |✓ | |✓ | 2032|acc |\n",
"|wic |✓ |✓ | | 638|acc |\n",
"|wikitext |✓ |✓ |✓ | 62|word_perplexity, byte_perplexity, bits_per_byte |\n",
"|winogrande |✓ |✓ | | 1267|acc |\n",
"|wmt14-en-fr | | |✓ | 3003|bleu, chrf, ter |\n",
"|wmt14-fr-en | | |✓ | 3003|bleu, chrf, ter |\n",
"|wmt16-de-en | | |✓ | 2999|bleu, chrf, ter |\n",
"|wmt16-en-de | | |✓ | 2999|bleu, chrf, ter |\n",
"|wmt16-en-ro | | |✓ | 1999|bleu, chrf, ter |\n",
"|wmt16-ro-en | | |✓ | 1999|bleu, chrf, ter |\n",
"|wmt20-cs-en | | |✓ | 664|bleu, chrf, ter |\n",
"|wmt20-de-en | | |✓ | 785|bleu, chrf, ter |\n",
"|wmt20-de-fr | | |✓ | 1619|bleu, chrf, ter |\n",
"|wmt20-en-cs | | |✓ | 1418|bleu, chrf, ter |\n",
"|wmt20-en-de | | |✓ | 1418|bleu, chrf, ter |\n",
"|wmt20-en-iu | | |✓ | 2971|bleu, chrf, ter |\n",
"|wmt20-en-ja | | |✓ | 1000|bleu, chrf, ter |\n",
"|wmt20-en-km | | |✓ | 2320|bleu, chrf, ter |\n",
"|wmt20-en-pl | | |✓ | 1000|bleu, chrf, ter |\n",
"|wmt20-en-ps | | |✓ | 2719|bleu, chrf, ter |\n",
"|wmt20-en-ru | | |✓ | 2002|bleu, chrf, ter |\n",
"|wmt20-en-ta | | |✓ | 1000|bleu, chrf, ter |\n",
"|wmt20-en-zh | | |✓ | 1418|bleu, chrf, ter |\n",
"|wmt20-fr-de | | |✓ | 1619|bleu, chrf, ter |\n",
"|wmt20-iu-en | | |✓ | 2971|bleu, chrf, ter |\n",
"|wmt20-ja-en | | |✓ | 993|bleu, chrf, ter |\n",
"|wmt20-km-en | | |✓ | 2320|bleu, chrf, ter |\n",
"|wmt20-pl-en | | |✓ | 1001|bleu, chrf, ter |\n",
"|wmt20-ps-en | | |✓ | 2719|bleu, chrf, ter |\n",
"|wmt20-ru-en | | |✓ | 991|bleu, chrf, ter |\n",
"|wmt20-ta-en | | |✓ | 997|bleu, chrf, ter |\n",
"|wmt20-zh-en | | |✓ | 2000|bleu, chrf, ter |\n",
"|wnli |✓ |✓ | | 71|acc |\n",
"|wsc |✓ |✓ | | 104|acc |\n",
"|wsc273 | | |✓ | 273|acc |\n",
"|xcopa_et | |✓ |✓ | 500|acc |\n",
"|xcopa_ht | |✓ |✓ | 500|acc |\n",
"|xcopa_id | |✓ |✓ | 500|acc |\n",
"|xcopa_it | |✓ |✓ | 500|acc |\n",
"|xcopa_qu | |✓ |✓ | 500|acc |\n",
"|xcopa_sw | |✓ |✓ | 500|acc |\n",
"|xcopa_ta | |✓ |✓ | 500|acc |\n",
"|xcopa_th | |✓ |✓ | 500|acc |\n",
"|xcopa_tr | |✓ |✓ | 500|acc |\n",
"|xcopa_vi | |✓ |✓ | 500|acc |\n",
"|xcopa_zh | |✓ |✓ | 500|acc |\n",
"|xnli_ar |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_bg |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_de |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_el |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_en |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_es |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_fr |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_hi |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_ru |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_sw |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_th |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_tr |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_ur |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_vi |✓ |✓ |✓ | 5010|acc |\n",
"|xnli_zh |✓ |✓ |✓ | 5010|acc |\n",
"|xstory_cloze_ar |✓ |✓ | | 1511|acc |\n",
"|xstory_cloze_en |✓ |✓ | | 1511|acc |\n",
"|xstory_cloze_es |✓ |✓ | | 1511|acc |\n",
"|xstory_cloze_eu |✓ |✓ | | 1511|acc |\n",
"|xstory_cloze_hi |✓ |✓ | | 1511|acc |\n",
"|xstory_cloze_id |✓ |✓ | | 1511|acc |\n",
"|xstory_cloze_my |✓ |✓ | | 1511|acc |\n",
"|xstory_cloze_ru |✓ |✓ | | 1511|acc |\n",
"|xstory_cloze_sw |✓ |✓ | | 1511|acc |\n",
"|xstory_cloze_te |✓ |✓ | | 1511|acc |\n",
"|xstory_cloze_zh |✓ |✓ | | 1511|acc |\n",
"|xwinograd_en | | |✓ | 2325|acc |\n",
"|xwinograd_fr | | |✓ | 83|acc |\n",
"|xwinograd_jp | | |✓ | 959|acc |\n",
"|xwinograd_pt | | |✓ | 263|acc |\n",
"|xwinograd_ru | | |✓ | 315|acc |\n",
"|xwinograd_zh | | |✓ | 504|acc |\n",
"| Ceval-valid-computer_network | | ✓ | | 19 | acc |\n",
"| Ceval-valid-operating_system | | ✓ | | 19 | acc |\n",
"| Ceval-valid-computer_architecture | | ✓ | | 21 | acc |\n",
"| Ceval-valid-college_programming | | ✓ | | 37 | acc |\n",
"| Ceval-valid-college_physics | | ✓ | | 19 | acc |\n",
"| Ceval-valid-college_chemistry | | ✓ | | 24 | acc |\n",
"| Ceval-valid-advanced_mathematics | | ✓ | | 19 | acc |\n",
"| Ceval-valid-probability_and_statistics | | ✓ | | 18 | acc |\n",
"| Ceval-valid-discrete_mathematics | | ✓ | | 16 | acc |\n",
"| Ceval-valid-electrical_engineer | | ✓ | | 37 | acc |\n",
"| Ceval-valid-metrology_engineer | | ✓ | | 24 | acc |\n",
"| Ceval-valid-high_school_mathematics | | ✓ | | 18 | acc |\n",
"| Ceval-valid-high_school_physics | | ✓ | | 19 | acc |\n",
"| Ceval-valid-high_school_chemistry | | ✓ | | 19 | acc |\n",
"| Ceval-valid-high_school_biology | | ✓ | | 19 | acc |\n",
"| Ceval-valid-middle_school_mathematics | | ✓ | | 19 | acc |\n",
"| Ceval-valid-middle_school_biology | | ✓ | | 21 | acc |\n",
"| Ceval-valid-middle_school_physics | | ✓ | | 19 | acc |\n",
"| Ceval-valid-middle_school_chemistry | | ✓ | | 20 | acc |\n",
"| Ceval-valid-veterinary_medicine | | ✓ | | 23 | acc |\n",
"| Ceval-valid-college_economics | | ✓ | | 55 | acc |\n",
"| Ceval-valid-business_administration | | ✓ | | 33 | acc |\n",
"| Ceval-valid-marxism | | ✓ | | 19 | acc |\n",
"| Ceval-valid-mao_zedong_thought | | ✓ | | 24 | acc |\n",
"| Ceval-valid-education_science | | ✓ | | 29 | acc |\n",
"| Ceval-valid-teacher_qualification | | ✓ | | 44 | acc |\n",
"| Ceval-valid-high_school_politics | | ✓ | | 19 | acc |\n",
"| Ceval-valid-high_school_geography | | ✓ | | 19 | acc |\n",
"| Ceval-valid-middle_school_politics | | ✓ | | 21 | acc |\n",
"| Ceval-valid-middle_school_geography | | ✓ | | 12 | acc |\n",
"| Ceval-valid-modern_chinese_history | | ✓ | | 23 | acc |\n",
"| Ceval-valid-ideological_and_moral_cultivation | | ✓ | | 19 | acc |\n",
"| Ceval-valid-logic | | ✓ | | 22 | acc |\n",
"| Ceval-valid-law | | ✓ | | 24 | acc |\n",
"| Ceval-valid-chinese_language_and_literature | | ✓ | | 23 | acc |\n",
"| Ceval-valid-art_studies | | ✓ | | 33 | acc |\n",
"| Ceval-valid-professional_tour_guide | | ✓ | | 29 | acc |\n",
"| Ceval-valid-legal_professional | | ✓ | | 23 | acc |\n",
"| Ceval-valid-high_school_chinese | | ✓ | | 19 | acc |\n",
"| Ceval-valid-high_school_history | | ✓ | | 20 | acc |\n",
"| Ceval-valid-middle_school_history | | ✓ | | 22 | acc |\n",
"| Ceval-valid-civil_servant | | ✓ | | 47 | acc |\n",
"| Ceval-valid-sports_science | | ✓ | | 19 | acc |\n",
"| Ceval-valid-plant_protection | | ✓ | | 22 | acc |\n",
"| Ceval-valid-basic_medicine | | ✓ | | 19 | acc |\n",
"| Ceval-valid-clinical_medicine | | ✓ | | 22 | acc |\n",
"| Ceval-valid-urban_and_rural_planner | | ✓ | | 46 | acc |\n",
"| Ceval-valid-accountant | | ✓ | | 49 | acc |\n",
"| Ceval-valid-fire_engineer | | ✓ | | 31 | acc |\n",
"| Ceval-valid-environmental_impact_assessment_engineer | | ✓ | | 31 | acc |\n",
"| Ceval-valid-tax_accountant | | ✓ | | 49 | acc |\n",
"| Ceval-valid-physician | | ✓ | | 49 | acc |"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

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