postbot_-_distilgpt2-emailgen-gguf

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distilgpt2-emailgen - GGUF

• Model creator: https://huggingface.co/postbot/
• Original model: https://huggingface.co/postbot/distilgpt2-emailgen/

Original model description:

license: apache-2.0 tags:

• generated_from_trainer

• distilgpt2

• email generation

• email datasets:

• aeslc

• postbot/multi_emails widget:

• 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 parameters: 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 base_model: distilgpt2

---

distilgpt2-emailgen

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

from transformers import pipeline

model_tag = "postbot/distilgpt2-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'])

• try it in a Google Colab notebook
• Use it in bash/cmd with this gist :)

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 distilgpt2 on a dataset of 50k emails, including the classic aeslc dataset.

It achieves the following results on the evaluation set:

• Loss: 2.6247

Intended uses & limitations

The intended use of this model is to provide suggestions to "autocomplete" the rest of your email. Said another way, it should serve as a tool to write predictable emails faster. It is not intended to write entire emails; at least some input is required to guide the direction of the model.

Please verify any suggestions by the model for A) False claims and B) negation statements before accepting/sending something.

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: 32
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.02
• num_epochs: 5

Training results

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.0+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1

Open LLM Leaderboard Evaluation Results

Detailed results can be found here