Instructions to use fspanda/Medical-Bio-BERT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fspanda/Medical-Bio-BERT2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fspanda/Medical-Bio-BERT2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fspanda/Medical-Bio-BERT2") model = AutoModelForMaskedLM.from_pretrained("fspanda/Medical-Bio-BERT2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from fspanda/Medical-Bio-BERT2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/fspanda/Medical-Bio-BERT2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://fspanda/Medical-Bio-BERT2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/fspanda/Medical-Bio-BERT2/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 0fb566c4df0b0c72552383e4d3f660f041df33d375266f560c63555ab5a1a6cb
- Size of remote file:
- 438 MB
- SHA256:
- bcb0cefc4978099c362942279eff509d5adb2a3c43e8fa9a474a194a0e2863b4
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