---
license: apache-2.0
tags:
- generated_from_trainer
- text generation
- 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: 0.4
  top_k: 30
  top_p: 0.9
  repetition_penalty: 3.5
  length_penalty: 0.9
base_model: EleutherAI/gpt-neo-1.3B
---


# gpt-neo-1.3B-emailgen

This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) on the postbot/multi-emails-100k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6930

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8669        | 1.0   | 789  | 1.7866          |
| 1.4049        | 2.0   | 1578 | 1.6930          |


### Framework versions

- Transformers 4.22.2
- Pytorch 1.10.0+cu113
- Tokenizers 0.12.1