Instructions to use MSLacerda/attribute_mining_mslacerda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MSLacerda/attribute_mining_mslacerda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MSLacerda/attribute_mining_mslacerda")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MSLacerda/attribute_mining_mslacerda") model = AutoModelForTokenClassification.from_pretrained("MSLacerda/attribute_mining_mslacerda", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0e76be19049fb0b3642ce88003fd020457ca0a546d9ecfe6994143bf53b4bd6
- Size of remote file:
- 266 MB
- SHA256:
- 446eab74a72ac8b3468b3937c6538088c54463a89de054c90a8ed4de037412c3
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