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---
title: Can I Patent This
emoji: 🏆
colorFrom: gray
colorTo: purple
sdk: streamlit
sdk_version: 1.21.0
app_file: app.py
pinned: false
---
# CS 670 Project - Finetuning Language Models
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Milestone-3 notebook: https://github.com/aye-thuzar/CS670Project/blob/main/CS670_milestone_3_AyeThuzar.ipynb
Hugging Face App: https://huggingface.co/spaces/ayethuzar/can-i-patent-this
Landing Page for the App: https://sites.google.com/view/cs670-finetuning-language-mode/home
App Demonstration Video:
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## Summary
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**milestone1:** https://github.com/aye-thuzar/CS670Project/blob/main/README_milestone_1.md
**milestone2:** https://github.com/aye-thuzar/CS670Project/blob/main/README_milestone-2.md
Dataset: https://github.com/suzgunmirac/hupd
**Data Preprocessing**
I used the load_dataset function to load all the patent applications that were filed to the USPTO in January 2016. We specify the date ranges of the training and validation sets as January 1-21, 2016 and January 22-31, 2016, respectively. This is a smaller dataset.
There are two datasets: train and validation. Here are the steps I did:
- Label-to-index mapping for the decision status field
- map the 'abstract' and 'claims' sections and tokenize them using pretrained('distilbert-base-uncased') tokenizer
- format them
- use DataLoader with batch_size = 16
**milestone3:**
The following notebook has the tuned model. There are 6 classes in the Harvard USPTO patent dataset and I decided to encode them as follow:
decision_to_str = {'REJECTED': 0, 'ACCEPTED': 1, 'PENDING': 1, 'CONT-REJECTED': 0, 'CONT-ACCEPTED': 1, 'CONT-PENDING': 1}
so that I can get a patentability score between 0 and 1.
I use the pertained-model 'distilbert-base-uncased' from the Hugging face hub and tune it with the smaller dataset.
The average accuracy of the validation set is about 89%.
milestone3 notebook: https://github.com/aye-thuzar/CS670Project/blob/main/CS670_milestone_3_AyeThuzar.ipynb
**milestone4:**
Please see Milestone4Documentation.md:
Here is the landing page for my app: https://sites.google.com/view/cs670-finetuning-language-mode/home
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References:
1. https://colab.research.google.com/drive/1_ZsI7WFTsEO0iu_0g3BLTkIkOUqPzCET?usp=sharing#scrollTo=B5wxZNhXdUK6
2. https://huggingface.co/AI-Growth-Lab/PatentSBERTa
3. https://huggingface.co/anferico/bert-for-patents
4. https://huggingface.co/transformers/v3.2.0/custom_datasets.html
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