Instructions to use pruas/BENT-PubMEdBERT-NER-Variant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pruas/BENT-PubMEdBERT-NER-Variant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pruas/BENT-PubMEdBERT-NER-Variant")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pruas/BENT-PubMEdBERT-NER-Variant") model = AutoModelForTokenClassification.from_pretrained("pruas/BENT-PubMEdBERT-NER-Variant", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from pruas/BENT-PubMEdBERT-NER-Variant: direct link, hf CLI and curl.
- Browser
- Download file 871 MB
-
https://huggingface.co/pruas/BENT-PubMEdBERT-NER-Variant/resolve/main/optimizer.pt
- Command line
-
hf download hf://pruas/BENT-PubMEdBERT-NER-Variant/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/pruas/BENT-PubMEdBERT-NER-Variant/resolve/main/optimizer.pt
871 MB
- Xet hash:
- 5847b236c83cb432f7370eb4cd66aafbae6ffd1869f1fec752de4940f4b6e1bc
- Size of remote file:
- 871 MB
- SHA256:
- a97c7e990e095dfeb687fc5cbb54298ea3f7e906adcf56628d2cd165be0dcb7c
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