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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ONNX-converted version of the model
Verified signatureWe decided to add this model for the Anonymize Scanner in llm-guard with your model. To have faster inference, we use ONNX models converted using Optimum from HuggingFace. Example of the repo with ONNX built-in: https://huggingface.co/laiyer/deberta-v3-base-prompt-injection Similar PR: https://huggingface.co/philomath-1209/programming-language-identification/discussions/16bac0e4
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End of training
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initial commit
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