Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use kbalde/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kbalde/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kbalde/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kbalde/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("kbalde/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kbalde/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/kbalde/bert-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://kbalde/bert-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kbalde/bert-finetuned-ner/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 76e7fde7ac5d088042cff59c04c31b5bc8d4e59d1e7a3fa9f88dcd1d684ae0a7
- Size of remote file:
- 3.38 kB
- SHA256:
- 5884e730297f81b072846d599364d7e5e7e7525f1b674a6ead7221246a137316
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