Original model description:

license: other tags:

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

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

colab notebook for testing/use

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