Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use DipakBundheliya/ner_bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DipakBundheliya/ner_bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DipakBundheliya/ner_bert_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DipakBundheliya/ner_bert_model") model = AutoModelForTokenClassification.from_pretrained("DipakBundheliya/ner_bert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from DipakBundheliya/ner_bert_model: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/DipakBundheliya/ner_bert_model/resolve/896388a7fa1d1a61ae8c0dabc0e28258461bed72/training_args.bin
- Command line
-
hf download hf://DipakBundheliya/ner_bert_model@896388a7fa1d1a61ae8c0dabc0e28258461bed72/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/DipakBundheliya/ner_bert_model/resolve/896388a7fa1d1a61ae8c0dabc0e28258461bed72/training_args.bin
4.92 kB
- Xet hash:
- 2eb65cb413334cee14e57bcf8ccf254947c110faf0ac00d613fbd1fa8c9d0c3b
- Size of remote file:
- 4.92 kB
- SHA256:
- 645c536d9788ddacec8dd4733f4460c9f0cfe8e2d432b6032c78ec52a1bcff5f
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