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)# 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
- Xet hash:
- 729f864aa84a397d1e93140e8c9ef9e73bdc857676174dc246fa70803e237ecd
- Size of remote file:
- 647 MB
- SHA256:
- 1d5142045649b6596f024a1d40503faf4013fa27dd64c51e8974618856273361
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