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NOTE: there is currently a bug with huggingface API for OPT models. Please use the colab notebook to test :)

opt for email generation - 350M

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(
prompt = """

Following up on the bubblegum shipment."""

) # generate
  • Link to notebook on Colab

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

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
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Dataset used to train pszemraj/opt-350m-email-generation