Instructions to use judithrosell/BioBERT_BioNLP13CG_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use judithrosell/BioBERT_BioNLP13CG_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="judithrosell/BioBERT_BioNLP13CG_NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("judithrosell/BioBERT_BioNLP13CG_NER") model = AutoModelForTokenClassification.from_pretrained("judithrosell/BioBERT_BioNLP13CG_NER") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1de737dd80dc013a952a7317a21b6a774543d3db0365a4bcefec551df66d5745
- Size of remote file:
- 4.6 kB
- SHA256:
- 9322292294ddea19db9a47ee14bea764c0142c788890c65dfd53b4cf71fcae74
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.