Instructions to use bradgrimm/patent-cpc-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bradgrimm/patent-cpc-predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bradgrimm/patent-cpc-predictor")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bradgrimm/patent-cpc-predictor") model = AutoModel.from_pretrained("bradgrimm/patent-cpc-predictor") - Notebooks
- Google Colab
- Kaggle
Patent CPC Predictor
This is a fine-tuned version of microsoft/deberta-v3-small for predicting Patent CPC codes.
Dataset
Dataset consists of titles and abstracts sampled from granted patent applications: https://www.kaggle.com/datasets/grimmace/sampled-patent-titles
Results
| Category | Accuracy |
|---|---|
| Section | 92% |
| Class | 88% |
| Subclass | 85% |
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