Instructions to use JuanC513/beto-ner-prostata-bs16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuanC513/beto-ner-prostata-bs16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="JuanC513/beto-ner-prostata-bs16")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("JuanC513/beto-ner-prostata-bs16") model = AutoModelForTokenClassification.from_pretrained("JuanC513/beto-ner-prostata-bs16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42e8fe5d76a82a1c724293d8a1b3a63f28f9d879e5ecbbf99001141e3b0ba3dd
- Size of remote file:
- 5.2 kB
- SHA256:
- 0899b16d80c17a84a078cccaddd7081c4777adc7b04e22d322f0feef4922571a
路
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