Instructions to use Neurona/cpener-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neurona/cpener-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Neurona/cpener-test")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Neurona/cpener-test") model = AutoModelForTokenClassification.from_pretrained("Neurona/cpener-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload DistilBertForTokenClassification
Browse files- config.json +2 -2
- pytorch_model.bin +2 -2
config.json
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{
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"_name_or_path": "models/
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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": "/dbfs/vulndigger/models/ner_full_notrunc53",
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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": "4.22.2",
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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:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:1f13c87057f0968133c6c422cb5c170614c7e2fa1895092abdd4033f48ad24af
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size 265503605
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