Instructions to use Debolena/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Debolena/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Debolena/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Debolena/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Debolena/bert-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d97b23340858a800f509c4e810281dcfa09b6abf2dedf229a4579405ed9da6c6
- Size of remote file:
- 5.78 kB
- SHA256:
- a81f3f47383ec3dcb31f38f165bc2f0fbd73751470c0ae419813b5701088639c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.