Fill-Mask
Transformers
PyTorch
JAX
English
bert
exbert
medical
biomedical-nlp
pubmed
literature-mining
drug-discovery
aurigene
Instructions to use Aurigene-AI/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aurigene-AI/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Aurigene-AI/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Aurigene-AI/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext") model = AutoModelForMaskedLM.from_pretrained("Aurigene-AI/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Aurigene-AI/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Aurigene-AI/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Aurigene-AI/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Aurigene-AI/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- c7407be1dbf15bbf95ae01ebf9bdf4df3c02846eec18d416c1fcbd7aed56ee12
- Size of remote file:
- 438 MB
- SHA256:
- 84761403b655e7d865093297cc57d574c5ec7ce705917f9d7011683c79f5fc41
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