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) - Notebooks
- Google Colab
- Kaggle
Manually set `pipeline_tag` in model metadata
#1
by mattupson - opened
Trying to resolve the issue of the model not working in the Hosted Inference API and Inference Endpoints, which complains that it can't detect the model's pipeline type. By manually setting it, we should overcome this problem.
mattupson changed pull request title from Manuall set pipeline_tag in model metadata to Manually set pipeline_tag in model metadata
mattupson changed pull request title from Manually set pipeline_tag in model metadata to Manually set `pipeline_tag` in model metadata
π
nsorros changed pull request status to merged