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

widget:
- text: "Hey <NAME>, Thank you for signing up to 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. Let me start by saying that I am a big fan of your work" 
  example_title: "fan"
- 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: "Hey, is it true that" 
  example_title: "is it true that"
- text: "URGENT - I"
  example_title: "URGENT"

inference:
  parameters:
    min_length: 16
    max_length: 64
    length_penalty: 0.7
    no_repeat_ngram_size: 3
    do_sample: False
    num_beams: 4
    early_stopping: True
    repetition_penalty: 2.1
---


# opt for email generation - 350M

- 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 generate 64 tokens - you can generate longer emails by using this in a text-generation `pipeline` object

## Model description

More information needed

## 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

More information needed

## 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

### Training results



### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1