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license: apache-2.0
  - generated_from_trainer
  - distilgpt2
  - email generation
  - email
  - aeslc
  - postbot/multi-emails-100k
  - text: >-
      Good Morning Professor Beans,

      Hope you are doing well. I just wanted to reach out and ask if
      differential calculus will be on the exam
    example_title: email to prof
  - 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. I wanted to reach out and ask about
      office hours
    example_title: office hours
  - 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 Harold,

      I was wondering when the next
    example_title: event
  - text: URGENT - I need the TPS reports
    example_title: URGENT
  - text: |-
      Hi Archibald,

      I hope this email finds you extremely well.
    example_title: emails that find you
  - text: |-
      Hello there.

      I just wanted to reach out and check in to
    example_title: checking in
  - text: >-
      Hello <NAME>,

      I hope this email finds you well. I wanted to reach out and see if you've
      enjoyed your time with us
    example_title: work well
  - text: >-
      Hi <NAME>,

      I hope this email finds you well. I wanted to reach out and see if we
      could catch up
    example_title: catch up
  - text: >-
      I'm <NAME> and I just moved into the area and wanted to reach out and get
      some details on where I could get groceries and
    example_title: grocery
  min_length: 4
  max_length: 128
  length_penalty: 0.8
  no_repeat_ngram_size: 2
  do_sample: false
  num_beams: 8
  early_stopping: true
  repetition_penalty: 5.5

distilgpt2-emailgen: V2


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

from transformers import pipeline

model_tag = "postbot/distilgpt2-emailgen-V2"
generator = pipeline(
prompt = """

Following up on the bubblegum shipment."""

result = generator(
) # generate

Model description

This model is a fine-tuned version of distilgpt2 on the postbot/multi-emails-100k dataset. It achieves the following results on the evaluation set:

  • Loss: 1.9126

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters (run 1/2)


Training hyperparameters (run 2/2)

The following hyperparameters were used during training:

  • learning_rate: 0.0006
  • 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: 0.01
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss
1.9045 1.0 789 2.0006
1.8115 2.0 1578 1.9557
1.8501 3.0 2367 1.9110
1.7376 4.0 3156 1.9126

Framework versions

  • Transformers 4.22.2
  • Pytorch 1.10.0+cu113
  • Datasets 2.5.1
  • Tokenizers 0.12.1