Instructions to use eclec/patentClassificationLongFormerTextrank with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eclec/patentClassificationLongFormerTextrank with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eclec/patentClassificationLongFormerTextrank")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eclec/patentClassificationLongFormerTextrank") model = AutoModelForSequenceClassification.from_pretrained("eclec/patentClassificationLongFormerTextrank", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9da0750cb3d8a6fffb452f3f85b2976a24aa02bb1d5bb11ba6bed8db9099ba65
- Size of remote file:
- 1.74 GB
- SHA256:
- aae0dd58f2b4a5f066406b96abeafea9106bf2c4eebf08bc19b6a297b2423aa3
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