Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use Kibalama/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kibalama/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Kibalama/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Kibalama/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Kibalama/bert-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32641fd5617c34ff9e3e8e12090738060932cef871e93ad596e0a4fac907cf4b
- Size of remote file:
- 5.37 kB
- SHA256:
- 31650b2b4fa051888685933005053febca15caf8c3e339fd5715d727fdabbcb7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.