Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use devangb4/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devangb4/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="devangb4/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("devangb4/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("devangb4/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff53f4a8603002e910ced8272646be1fdb3bf3beacde0a0e8b76a223611c1312
- Size of remote file:
- 5.84 kB
- SHA256:
- aa01491e7b5cb25f59494765ca8df6e01bd466f61d09bc47b668866666e1e42c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.