Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use naufalso/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naufalso/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="naufalso/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("naufalso/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("naufalso/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d3dce51eb4f6eaf6f7ba7a35937665f38a6061c8f8496d0b5e697b0b36b5be8
- Size of remote file:
- 3.52 kB
- SHA256:
- 4c2f42de681b868abf9bb87f6656e1d27eeec19693461c087efae950689fe621
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.