Instructions to use sschet/biobert_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sschet/biobert_ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sschet/biobert_ner")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("sschet/biobert_ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46b844f350da96b467630d720b8dd9bd45d622cca0ebc3c4ecb85ccb942b81e0
- Size of remote file:
- 433 MB
- SHA256:
- 412399c4d81a36efcc63d3c6eebb37d9a442576b0e637eac08fd45d830b02efa
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