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:
- db38c8e2be3bdd8e239ff317bfafbbae4f0f3113807dcbce6f08e5f78297b27d
- Size of remote file:
- 709 MB
- SHA256:
- 92380e0a8144cda76d9e04e3b6f4eaa908013b5495e7e0169bd7042f6faac0e5
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