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pszemraj
/
opt-125m-email-generation

Text Generation
Transformers
PyTorch
Safetensors
opt
Generated from Trainer
custom-license
non-commercial
email
auto-complete
125m
text-generation-inference
Inference Endpoints
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opt-125m-email-generation / README.md
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metadata
license: other
tags:
  - generated_from_trainer
  - opt
  - custom-license
  - non-commercial
  - email
  - auto-complete
  - 125m
datasets:
  - aeslc
widget:
  - 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. 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: |-
      Good Morning <NAME>,

      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

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