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gpt2-medium-emailgen

colab

Why write the entire email when you can generate (most of) it?

from transformers import pipeline

model_tag = "postbot/gpt2-medium-emailgen"
generator = pipeline(
              'text-generation', 
              model=model_tag, 
            )
            
prompt = """
Hello, 

Following up on the bubblegum shipment."""

result = generator(
    prompt,
    max_length=64,
    do_sample=False,
    early_stopping=True,
) # generate
print(result[0]['generated_text'])

about

This model is a fine-tuned version of gpt2-medium on the postbot/multi-emails-100k dataset. It achieves the following results on the evaluation set:

  • Loss: 1.5840

Model description

More information needed

Intended uses & limitations

  • this is intended as a tool to save time writing predictable emails and not to write emails without a human-in-the-loop. validate that your email is factually correct before sending it to others.

Training and evaluation data

  • the dataset is essentially a hand-curated/augmented expansion to the classic aeslc dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 8
  • 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: 0.02
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
1.8701 1.0 789 1.8378
1.5065 2.0 1578 1.6176
1.1873 3.0 2367 1.5840

Framework versions

  • Transformers 4.22.2
  • Pytorch 1.10.0+cu113
  • Datasets 2.5.1
  • Tokenizers 0.12.1
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Dataset used to train postbot/gpt2-medium-emailgen

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