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---
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 <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
datasets:
- postbot/multi-emails-hq
language:
- en
pipeline_tag: text-generation
---
# emailgen-pythia-410m-deduped
[](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"])
```
---
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