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:
- dd614405c4f83137468cab6189ecad765f9d0c7fdb2a227328d100955584fcf9
- Size of remote file:
- 438 MB
- SHA256:
- 5796ebe357175563d5dfe5b6d1a97050787e077857cff9d9dc9c88c0e3a0fd4a
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