Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use Shariar433/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shariar433/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Shariar433/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Shariar433/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Shariar433/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Shariar433/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/Shariar433/bert-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://Shariar433/bert-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Shariar433/bert-finetuned-ner/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 2b9a027e796a79d2f74214d8d0c907833502c74c15118200cc1a1db5f8920169
- Size of remote file:
- 3.96 kB
- SHA256:
- 9d2e788447c7244c2cfeadf9cc9694d69450b11bc61e066c303fcc2ca1bb52dc
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