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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         |