--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: multi-emails-hq-pythia-410m-deduped-r1 results: [] 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 , 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 , I hope this email finds you well. I wanted to reach out and ask about office hours example_title: office hours - text: >- Greetings , 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 , 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 , 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 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 datasets: - postbot/multi-emails-hq language: - en pipeline_tag: text-generation --- # emailgen-pythia-410m-deduped [![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/pszemraj/94b0e6b95437896f800a65ae2e5f9ab4/emailgen-pythia-410m-deduped.ipynb ) This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/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 ```python 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"]) ``` ---