---
license: apache-2.0
tags:
- generated_from_trainer
- distilgpt2
- email generation
- email
datasets:
- aeslc
- postbot/multi_emails

widget:
- 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 <NAME>,\n\nI was wondering when the next" 
  example_title: "event"
- text: "URGENT - I need the TPS reports"
  example_title: "URGENT"
- text: "Hi <NAME>,\n\nI hope this email finds you extremely well." 
  example_title: "emails that find you"

parameters:
  min_length: 4
  max_length: 128
  length_penalty: 0.5
  no_repeat_ngram_size: 2
  do_sample: False
  num_beams: 4
  early_stopping: True
  repetition_penalty: 4.5
---


# 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, 
              do_sample=False,
              early_stopping=True,
            )
            
prompt = """
Hello, 

Following up on the bubblegum shipment."""

generator(
    prompt,
    max_length=64,
) # generate
```

A script to use this on CPU/command line can be found [here](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