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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6474de428ba11db666e2e02a5a8ea65e48519f2124101ccfc44bfd9dc73c4bb
- Size of remote file:
- 565 MB
- SHA256:
- 6691fb109fca664ced15ee7a1fd3d5fd549565ef350175bdb91c99af120beddc
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