Instructions to use JuanC513/beto-ner-prostata-bs32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuanC513/beto-ner-prostata-bs32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="JuanC513/beto-ner-prostata-bs32")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("JuanC513/beto-ner-prostata-bs32") model = AutoModelForTokenClassification.from_pretrained("JuanC513/beto-ner-prostata-bs32", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55ff23c0b5ea6fc2d63d8e4e9e4bde517753621a1f2005513895eb9f55716ec9
- Size of remote file:
- 5.2 kB
- SHA256:
- 3cb91ba85fe0dda6c38e4f7c00fc9bbc5d08ee8961b6fefc0675b79ec09f5759
路
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