> 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

{"license": "other", "tags": ["generated_from_trainer", "opt", "custom-license", "non-commercial", "email", "auto-complete", "125m"], "datasets": ["aeslc"], "widget": [{"text": "Hey <NAME>,\n\nThank 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>,\n\nI 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>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because", "example_title": "festival"}, {"text": "Good Morning <NAME>,\n\nI 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"}
transformers pytorch safetensors opt text-generation generated_from_trainer custom-license non-commercial email auto-complete 125m dataset:aeslc base_model:facebook/opt-125m license:other autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #opt #text-generation #generated_from_trainer #custom-license #non-commercial #email #auto-complete #125m #dataset-aeslc #base_model-facebook/opt-125m #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


> 
> 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?


* 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



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Tokenizers 0.12.1



### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #safetensors #opt #text-generation #generated_from_trainer #custom-license #non-commercial #email #auto-complete #125m #dataset-aeslc #base_model-facebook/opt-125m #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #safetensors #opt #text-generation #generated_from_trainer #custom-license #non-commercial #email #auto-complete #125m #dataset-aeslc #base_model-facebook/opt-125m #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cu113
* Tokenizers 0.12.1