RichardErkhov/postbot_-_gpt2-medium-emailgen-gguf

Quantization made by Richard Erkhov.

Github

Discord

Request more models

gpt2-medium-emailgen - GGUF

• —Model creator: https://huggingface.co/postbot/
• —Original model: https://huggingface.co/postbot/gpt2-medium-emailgen/

Original model description: --- license:

• —apache-2.0 tags:
• —text generation
• —emailgen
• —email generation
• —email datasets:
• —aeslc
• —postbot/multi-emails-100k

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>,\n\nThank 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>,\n\nI hope this email finds you well. I wanted to reach out and ask about office hours" example_title: "office hours"
• —text: "Greetings <NAME>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because" example_title: "festival"
• —text: "Good Morning Harold,\n\nI was wondering when the next" example_title: "event"
• —text: "URGENT - I need the TPS reports" example_title: "URGENT"
• —text: "Hi Archibald,\n\nI hope this email finds you extremely well." example_title: "emails that find you"
• —text: "Hello there.\n\nI just wanted to reach out and check in to" example_title: "checking in"
• —text: "Hello <NAME>,\n\nI 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>,\n\nI 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" exampletitle: "grocery" parameters: minlength: 32 maxlength: 128 norepeatngramsize: 2 dosample: True temperature: 0.3 topk: 20 topp: 0.95 repetitionpenalty: 3.5 length_penalty: 0.9 ---

gpt2-medium-emailgen


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
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —distributed_type: multi-GPU
• —gradientaccumulationsteps: 8
• —totaltrainbatch_size: 128
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: cosine
• —lrschedulerwarmup_ratio: 0.02
• —num_epochs: 3

Training results

Framework versions

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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here