Instructions to use rjac/biobert-ner-diseases-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjac/biobert-ner-diseases-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rjac/biobert-ner-diseases-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rjac/biobert-ner-diseases-model") model = AutoModelForTokenClassification.from_pretrained("rjac/biobert-ner-diseases-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rjac/biobert-ner-diseases-model: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/rjac/biobert-ner-diseases-model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rjac/biobert-ner-diseases-model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rjac/biobert-ner-diseases-model/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- d3e0c3f345b8f6d205224c6cba63b0ad4cc1470683ea87423377ede9246733ec
- Size of remote file:
- 431 MB
- SHA256:
- 8811e6682ae57a8c50271b61b40f6aafbc684847e535b36085ad756d6631ddc1
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