metadata

language:
  - en
license: apache-2.0
tags:
  - generated_from_trainer
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
    example_title: grocery
pipeline_tag: 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

Metric	Value
Avg.	26.65
ARC (25-shot)	27.9
HellaSwag (10-shot)	40.04
MMLU (5-shot)	27.35
TruthfulQA (0-shot)	38.2
Winogrande (5-shot)	52.09
GSM8K (5-shot)	0.0
DROP (3-shot)	0.99