Opt 350m Email Generation

🚀 opt for email generation - 350M

Generate emails effortlessly and save time! This model fine - tunes opt - 350m for email generation, providing a convenient solution for email composition.

🚀 Quick Start

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

from transformers import pipeline

model_tag = "pszemraj/opt-350m-email-generation"
generator = pipeline(
              'text-generation', 
              model=model_tag, 
              use_fast=False,
              do_sample=False,
              early_stopping=True,
            )
            
prompt = """
Hello, 

Following up on the bubblegum shipment."""

generator(
    prompt,
    max_length=64,
) # generate

• Link to notebook on Colab

⚠️ Important Note

For this model, formatting matters. The results may be (significantly) different between the structure outlined above and prompt = "Hey, just wanted to ..." etc.

💡 Usage Tip

If you like the idea of wasting less time on emails, further work on this topic can be found on this hf org page

💻 Usage Examples

Basic Usage

from transformers import pipeline

model_tag = "pszemraj/opt-350m-email-generation"
generator = pipeline(
              'text-generation', 
              model=model_tag, 
              use_fast=False,
              do_sample=False,
              early_stopping=True,
            )
            
prompt = """
Hello, 

Following up on the bubblegum shipment."""

generator(
    prompt,
    max_length=64,
) # generate

📚 Documentation

Model description

• This model is a fine - tuned version of facebook/opt-350m on the aeslc dataset for six epochs.
• 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

Intended uses & limitations

• In their everlasting wisdom, Facebook/Meta has decided to make a custom license for this, specifying several things. See facebook/opt-350m for details.

Training and evaluation data

• The email_body field of train + validation (get more data) from the aeslc dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 6e - 05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi - GPU
• gradient_accumulation_steps: 16
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 6

Framework versions

• Transformers 4.19.2
• Pytorch 1.11.0+cu113
• Tokenizers 0.12.1

📄 License

This model is under a custom license. See facebook/opt-350m for details.

Additional Information

Tags

• generated_from_trainer
• opt
• custom-license
• no-commercial
• email
• auto-complete

Datasets

• aeslc

Widget Examples

Inference Parameters