Instructions to use dslim/distilbert-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dslim/distilbert-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dslim/distilbert-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dslim/distilbert-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/distilbert-NER", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Fix config to use the correct B/I/O label names
Token labels were previously configured as generic LABEL_0 to LABEL_8; changed to the correct B/I/O tags so transformers ner pipeline can correctly aggregate the tokens into chunks.9fb26d1
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ONNX-converted version of the model (#1)
Verified signature- ONNX-converted version of the model (6bac0e41f0675ba542c73e521258590e0bd442fd) Co-authored-by: Oleksandr Yaremchuk <asofter@users.noreply.huggingface.co>2be6cea
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End of training
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initial commit
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