Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use terzimert/bert-finetuned-ner-balancedData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use terzimert/bert-finetuned-ner-balancedData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="terzimert/bert-finetuned-ner-balancedData")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("terzimert/bert-finetuned-ner-balancedData") model = AutoModelForTokenClassification.from_pretrained("terzimert/bert-finetuned-ner-balancedData", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6eb2836687cb27076b400754d799b43d14f3655f39a1c4d36aa0d2dfe29b82f7
- Size of remote file:
- 3.58 kB
- SHA256:
- 8ac776a156d60c3cb89bf6292784a319fb947ce903360b7a025d7c265462fc4c
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