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
Commit History
Add label score in handler 56e1a5f
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Remove the extra list f0368c2
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Remove need for label binarizer 33a4c63
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Fix handler da5a2e2
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Return list of list in handler 6d3d5fc
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Add handler and requirements 5277582
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Add warning that you need latest transformers 7592134
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Shorten example width to fit in screen better 1fc3d25
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Update README.md b8ec696
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add model 731f527
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add model 6e2698b
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Update WellcomeBertMesh with transformers based trained model e842824
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Pass params from saved model d89f434
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Fix model weights 68013a5
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Fix model to receive input_ids 11b09bf
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Add tokenizer 9ec215b
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Make model compatible with output of tokenizer 26d4cd1
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Add _init_weights to model 44b4eae
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Add config param to model 1b85e66
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Fix num_labels 895295d
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Update model b4da537
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Fix config 9c3f2b9
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Update config 31f57bb
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Update model b1f91c5
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Rename model bin to pytorch_model bin daa8910
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Upload model.bin with git-lfs 8435d88
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Upload model.py ba33264
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Upload config.json e68d6e7
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