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")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dslim/distilbert-NER") model = AutoModelForTokenClassification.from_pretrained("dslim/distilbert-NER") - Inference
- Notebooks
- Google Colab
- Kaggle
ONNX-converted version of the model
#1
by asofter - opened
We 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/1
dslim changed pull request status to merged