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

opt for email generation - 125m

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

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

About

This model is a fine-tuned version of facebook/opt-125m on an aeslc dataset.

  • 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

It achieves the following results on the evaluation set:

  • 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

Training Loss Epoch Step Validation Loss
2.8245 1.0 129 2.8030
2.521 2.0 258 2.6343
2.2074 3.0 387 2.5595
2.0145 4.0 516 2.5552

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.11.0+cu113
  • Tokenizers 0.12.1
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This model can be loaded on the Inference API on-demand.

Dataset used to train pszemraj/opt-125m-email-generation