---
license: other
tags:
- generated_from_trainer
- opt
- custom-license
- no-commercial
- email
- auto-complete
datasets:
- aeslc

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. Let me start by saying that I am a big fan of your work." 
  example_title: "fan"
- 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 just thinking to myself about how much I love creating value" 
  example_title: "value"
- text: "URGENT - I need"
  example_title: "URGENT"

inference:
  parameters:
    min_length: 4
    max_length: 64
    length_penalty: 0.7
    no_repeat_ngram_size: 3
    do_sample: False
    num_beams: 4
    early_stopping: True
    repetition_penalty: 3.5
---


# opt for email generation - 350M

> If you like the idea of wasting less time on emails, further work on this topic can be found [on this hf org page](https://huggingface.co/postbot)

Why write the rest of your email when you can generate it?

```python
from transformers import pipeline

model_tag = "pszemraj/opt-350m-email-generation"
generator = pipeline(
              'text-generation', 
              model=model_tag, 
              use_fast=False,
              do_sample=False,
              early_stopping=True,
            )
            
prompt = """
Hello, 

Following up on the bubblegum shipment."""

generator(
    prompt,
    max_length=64,
) # generate
```
- [Link to notebook](https://colab.research.google.com/gist/pszemraj/40c46deed730bfca553b8c4b257a7b77/email-autocomplete-demo.ipynb) on Colab
> 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 [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the [aeslc](https://huggingface.co/datasets/aeslc) dataset for six epochs. 
- Emails, phone numbers, etc., were attempted to be excluded in a dataset preparation step using [clean-text](https://pypi.org/project/clean-text/) in Python.
- Note that API is restricted to generating 64 tokens - you can generate longer emails by using this in a text-generation `pipeline` object

## Intended uses & limitations

- in their everlasting wisdom, Facebook/Meta has decided to make a custom license for this, specifying several things. See [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) for details.

## Training and evaluation data

- the `email_body` field of train + validation (get more data) from the [aeslc](https://huggingface.co/datasets/aeslc) dataset.

## 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: 16
- 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: 0.03
- num_epochs: 6

### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1