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
- Xet hash:
- 55c17c68521157ef69baea73d4285377c4b8f18668bb9aa6ffde5da20167808b
- Size of remote file:
- 261 MB
- SHA256:
- 235ecd699f650ca36513b122d11ce5a80339be1388f06c33c2278368b99914e1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.