Text Classification
Transformers
PyTorch
bert
feature-extraction
custom_code
text-embeddings-inference
Instructions to use Wellcome/WellcomeBertMesh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wellcome/WellcomeBertMesh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Wellcome/WellcomeBertMesh", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Wellcome/WellcomeBertMesh", trust_remote_code=True) model = AutoModel.from_pretrained("Wellcome/WellcomeBertMesh", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add label score in handler
Nick Sorros56e1a5f -
Remove the extra list
Nick Sorrosf0368c2 -
Remove need for label binarizer
Nick Sorros33a4c63 -
Fix handler
Nick Sorrosda5a2e2 -
Return list of list in handler
Nick Sorros6d3d5fc -
Add handler and requirements
Nick Sorros5277582