Instructions to use nassga/nassGanBioMedical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nassga/nassGanBioMedical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nassga/nassGanBioMedical")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nassga/nassGanBioMedical") model = AutoModelForTokenClassification.from_pretrained("nassga/nassGanBioMedical") - Notebooks
- Google Colab
- Kaggle
Upload DistilBertForTokenClassification
Browse files- config.json +2 -2
- pytorch_model.bin +3 -0
config.json
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{
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"_name_or_path": "
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"activation": "gelu",
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"architectures": [
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"DistilBertForTokenClassification"
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"vocab_size": 30522
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}
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{
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"_name_or_path": "./drive/MyDrive/PFE/Medical-NER-model-1",
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"activation": "gelu",
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"architectures": [
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"DistilBertForTokenClassification"
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.30.2",
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"vocab_size": 30522
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:455ff3cfb721f5f7a6a4c4d9fc900af2b5bfac1997280f7bb652d3f909140b54
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size 265737189
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