RichardErkhov/postbot_-_emailgen-pythia-410m-deduped-gguf

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emailgen-pythia-410m-deduped - GGUF

• —Model creator: https://huggingface.co/postbot/
• —Original model: https://huggingface.co/postbot/emailgen-pythia-410m-deduped/

Original model description: --- language:

• —en license: apache-2.0 tags:
• —generatedfromtrainer datasets:
• —postbot/multi-emails-hq metrics:
• —accuracy 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 exampletitle: grocery pipelinetag: text-generation base_model: EleutherAI/pythia-410m-deduped model-index:
• —name: multi-emails-hq-pythia-410m-deduped-r1 results: [] ---

emailgen-pythia-410m-deduped


This model is a fine-tuned version of EleutherAI/pythia-410m-deduped on email data. It achieves the following results on the evaluation set:

• —Loss: 2.1018
• —Accuracy: 0.6157
• —perplexity: 8.181

Model description

• —fine-tuned on dataset of emails for 4 epochs
• —intended use: "text completion" of partially written emails

Usage example

from transformers import pipeline

model_tag = "postbot/emailgen-pythia-410m-deduped"
generator = pipeline(
    "text-generation",
    model=model_tag,
)

prompt = """
Hello, 

Following up on the bubblegum shipment."""

result = generator(
    prompt,
)  # generate
print(result[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here