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Model: pszemraj/opt-125m-email-generation Source: Original Platform
2026-06-07 07:08:16 +08:00
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+---
+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](https://colab.research.google.com/gist/pszemraj/033dc9a38da31ced7a0343091ba42e31/email-autocomplete-demo-125m.ipynb) 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](https://colab.research.google.com/gist/pszemraj/033dc9a38da31ced7a0343091ba42e31/email-autocomplete-demo-125m.ipynb) for testing/use
+
+## About
+
+
+This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an `aeslc` dataset.
+
+
+- Emails, phone numbers, etc., were attempted to be excluded in a dataset preparation step using [clean-text](https://pypi.org/project/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](https://gist.github.com/pszemraj/c1b0a76445418b6bbddd5f9633d1bb7f) 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](https://huggingface.co/datasets/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