README.md

---
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>,


    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: 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
base_model: gpt2-medium
---


# 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