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README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+- distilgpt2
+- email generation
+- email
+datasets:
+- aeslc
+- postbot/multi_emails
+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>,
+
+
+    Thank 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>,
+
+
+    I hope this email finds you well. I wanted to reach out and ask about office hours'
+  example_title: office hours
+- text: 'Greetings <NAME>,
+
+
+    I hope you had a splendid evening at the Company sausage eating festival. I am
+    reaching out because'
+  example_title: festival
+- text: 'Good Morning Harold,
+
+
+    I was wondering when the next'
+  example_title: event
+- text: URGENT - I need the TPS reports
+  example_title: URGENT
+- text: 'Hi Archibald,
+
+
+    I hope this email finds you extremely well.'
+  example_title: emails that find you
+- text: 'Hello there.
+
+
+    I just wanted to reach out and check in to'
+  example_title: checking in
+- text: 'Hello <NAME>,
+
+
+    I 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>,
+
+
+    I 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: 4
+  max_length: 128
+  length_penalty: 0.8
+  no_repeat_ngram_size: 2
+  do_sample: false
+  num_beams: 8
+  early_stopping: true
+  repetition_penalty: 5.5
+base_model: distilgpt2
+---
+
+
+# distilgpt2-emailgen
+
+Why write the rest of your email when you can generate it?
+
+```python
+from transformers import pipeline
+
+model_tag = "postbot/distilgpt2-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'])
+```
+
+- try it in a [Google Colab](https://colab.research.google.com/gist/pszemraj/91df57e0c2caf1d5273b78576ad2853e/postbot-distilgpt2-emailgen-demo.ipynb) notebook
+- Use it in bash/cmd [with this gist](https://gist.github.com/pszemraj/c1b0a76445418b6bbddd5f9633d1bb7f) :) 
+
+> For this model, formatting matters. The results may be (significantly) different between the structure outlined above and `prompt = "Hey, just wanted to ..."` etc.
+
+## Model description
+
+This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a dataset of 50k emails, including the classic `aeslc` dataset.
+
+It achieves the following results on the evaluation set:
+- Loss: 2.6247
+
+
+## Intended uses & limitations
+
+The intended use of this model is to provide suggestions to "autocomplete" the rest of your email. Said another way, it should serve as a **tool to write predictable emails faster**. It is not intended to write entire emails; at least **some input** is required to guide the direction of the model.
+
+Please verify any suggestions by the model for A) False claims and B) negation statements before accepting/sending something.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 6e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- gradient_accumulation_steps: 32
+- total_train_batch_size: 256
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.02
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 2.8299        | 1.0   | 248  | 2.7971          |
+| 2.6984        | 2.0   | 496  | 2.6826          |
+| 2.7022        | 3.0   | 744  | 2.6361          |
+| 2.6436        | 4.0   | 992  | 2.6245          |
+| 2.6195        | 5.0   | 1240 | 2.6247          |
+
+
+### Framework versions
+
+- Transformers 4.21.1
+- Pytorch 1.12.0+cu113
+- Datasets 2.4.0
+- 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__distilgpt2-emailgen)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 24.89   |
+| ARC (25-shot)         | 21.76          |
+| HellaSwag (10-shot)   | 27.52    |
+| MMLU (5-shot)         | 25.97         |
+| TruthfulQA (0-shot)   | 46.17   |
+| Winogrande (5-shot)   | 51.62   |
+| GSM8K (5-shot)        | 0.0        |
+| DROP (3-shot)         | 1.16         |

distilgpt2-emailgen.Q4_0.gguf

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