Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use kamalkraj/bert-base-cased-ner-conll2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kamalkraj/bert-base-cased-ner-conll2003 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kamalkraj/bert-base-cased-ner-conll2003")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kamalkraj/bert-base-cased-ner-conll2003") model = AutoModelForTokenClassification.from_pretrained("kamalkraj/bert-base-cased-ner-conll2003", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8dc9f571eb924d1a17c3d0af9669ab88fae8d084af96898102c6cb9adf94537
- Size of remote file:
- 3.18 kB
- SHA256:
- 3e961a6c53fbf93139404c116b2a79dc2933e316344c0b9d20f8ebf4fe417b68
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.