• Model creator: https://huggingface.co/pszemraj/
• Original model: https://huggingface.co/pszemraj/opt-125m-email-generation/

Original model description:

• generated_from_trainer
• opt
• custom-license
• non-commercial
• email
• auto-complete
• 125m datasets:
• aeslc widget:
• text: 'Hey ,
  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 ,
  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 ,
  I hope you had a splendid evening at the Company sausage eating festival. I am reaching out because' example_title: festival
• text: 'Good Morning ,
  I was just thinking to myself about how much I love creating value' example_title: value
• text: URGENT - I need example_title: URGENT 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 use_fast: false base_model: facebook/opt-125m

NOTE: there is currently a bug with huggingface API for OPT models. Please use the colab notebook to test :)

opt for email generation - 125m

from transformers import pipeline
model_tag = "pszemraj/opt-125m-email-generation"
generator = pipeline(
              'text-generation', 
              model=model_tag, 
              use_fast=False,
              do_sample=False,
            )
            
prompt = """
Hello, 
Following up on the bubblegum shipment."""
generator(
    prompt,
    max_length=96,
) # generate

• colab notebook for testing/use

About

• Emails, phone numbers, etc., were attempted to be excluded in a dataset preparation step using 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

• Loss: 2.5552

Intended uses & limitations

• OPT models cannot be used commercially
• here is a GitHub gist for a script to generate emails in the console or to a text file.

Training and evaluation data

• the email_body field of train + validation (get more data) from the aeslc dataset.

Training results

Framework versions

• Transformers 4.20.1
• Pytorch 1.11.0+cu113
• Tokenizers 0.12.1