Instructions to use jaese/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaese/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jaese/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jaese/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("jaese/bert-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01c7a385cec7623ba7082b38568bbd0c009f945f1fffbddec54d69d86142c801
- Size of remote file:
- 431 MB
- SHA256:
- 60572a6cd67101df407b442a69171fef5ba2a2759752dc7c9670c12a9ceb1e84
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.