---
license: 
- cc-by-nc-sa-4.0
tags:
- text generation
- generated_from_trainer
- 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>,\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 Harold,\n\nI was wondering when the next" 
  example_title: "event"
- text: "URGENT - I need the TPS reports"
  example_title: "URGENT"
- text: "Hi Archibald,\n\nI hope this email finds you extremely well." 
  example_title: "emails that find you"
- text: "Hello there.\n\nI just wanted to reach out and check in to"
  example_title: "checking in"
- text: "Hello <NAME>,\n\nI 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>,\n\nI 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: 20
  top_p: 0.95
  repetition_penalty: 3.5
  length_penalty: 0.9
---


# gpt2-medium-emailgen

This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5840

## 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: 0.001
- 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