Instructions to use laiking/biomedbert-outcomes-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use laiking/biomedbert-outcomes-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="laiking/biomedbert-outcomes-ner")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("laiking/biomedbert-outcomes-ner") model = AutoModel.from_pretrained("laiking/biomedbert-outcomes-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 910658d910247ea2c9cd7285bfb5710824964e322068bb1a183d24142b4e7fa4
- Size of remote file:
- 438 MB
- SHA256:
- 245461f9d66b8a242937d987710715f78acc9db86cd1b2793c082c067d4ccee4
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