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Model: postbot/gpt2-medium-emailgen
Source: Original Platform

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.gitignore

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+checkpoint-*/

README.md

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+---
+license: 
+- apache-2.0
+tags:
+- text generation
+- emailgen
+- email generation
+- email
+datasets:
+- aeslc
+- postbot/multi-emails-100k
+
+widget:
+- text: "Good Morning Professor Beans,
+
+Hope you are doing well. I just wanted to reach out and ask if differential calculus will be on the exam"
+  example_title: "email to prof"
+- text: "Hey <NAME>,\n\nThank you for signing up for my weekly newsletter. Before we get started, you'll have to confirm your email address." 
+  example_title: "newsletter"
+- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and ask about office hours" 
+  example_title: "office hours"
+- text: "Greetings <NAME>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because" 
+  example_title: "festival"
+- text: "Good Morning Harold,\n\nI was wondering when the next" 
+  example_title: "event"
+- text: "URGENT - I need the TPS reports"
+  example_title: "URGENT"
+- text: "Hi Archibald,\n\nI hope this email finds you extremely well." 
+  example_title: "emails that find you"
+- text: "Hello there.\n\nI just wanted to reach out and check in to"
+  example_title: "checking in"
+- text: "Hello <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if you've enjoyed your time with us"
+  example_title: "work well"
+- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if we could catch up"
+  example_title: "catch up"
+- text: "I'm <NAME> and I just moved into the area and wanted to reach out and get some details on where I could get groceries and"
+  example_title: "grocery"
+parameters:
+  min_length: 32
+  max_length: 128
+  no_repeat_ngram_size: 2
+  do_sample: True
+  temperature: 0.3
+  top_k: 20
+  top_p: 0.95
+  repetition_penalty: 3.5
+  length_penalty: 0.9
+---
+
+
+# gpt2-medium-emailgen
+
+[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/pszemraj/70058788c6d4b430398c12ee8ba10602/minimal-demo-for-postbot-gpt2-medium-emailgen.ipynb
+)
+
+Why write the entire email when you can generate (most of) it?
+
+```python
+from transformers import pipeline
+
+model_tag = "postbot/gpt2-medium-emailgen"
+generator = pipeline(
+              'text-generation', 
+              model=model_tag, 
+            )
+            
+prompt = """
+Hello, 
+
+Following up on the bubblegum shipment."""
+
+result = generator(
+    prompt,
+    max_length=64,
+    do_sample=False,
+    early_stopping=True,
+) # generate
+print(result[0]['generated_text'])
+```
+
+## about
+
+This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the postbot/multi-emails-100k dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.5840
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+- this is intended as a tool to save time writing predictable emails and not to write emails without a human-in-the-loop. validate that your email is factually correct before sending it to others.
+
+## Training and evaluation data
+
+- the dataset is essentially a hand-curated/augmented expansion to the classic `aeslc` dataset
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.001
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- distributed_type: multi-GPU
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.02
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 1.8701        | 1.0   | 789  | 1.8378          |
+| 1.5065        | 2.0   | 1578 | 1.6176          |
+| 1.1873        | 3.0   | 2367 | 1.5840          |
+
+
+### Framework versions
+
+- Transformers 4.22.2
+- Pytorch 1.10.0+cu113
+- Datasets 2.5.1
+- Tokenizers 0.12.1
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_postbot__gpt2-medium-emailgen)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 25.97   |
+| ARC (25-shot)         | 26.45          |
+| HellaSwag (10-shot)   | 34.31    |
+| MMLU (5-shot)         | 24.1         |
+| TruthfulQA (0-shot)   | 43.96   |
+| Winogrande (5-shot)   | 50.43   |
+| GSM8K (5-shot)        | 0.0        |
+| DROP (3-shot)         | 2.53         |

config.json

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+{
+  "_name_or_path": "gpt2-medium",
+  "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": "4.22.2",
+  "use_cache": false,
+  "vocab_size": 50257
+}

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special_tokens_map.json

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+    "rstrip": false,
+    "single_word": false
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tokenizer_config.json

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+    "normalized": true,
+    "rstrip": false,
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+  "errors": "replace",
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+  "name_or_path": "gpt2-medium",
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+  "tokenizer_class": "GPT2Tokenizer",
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