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 tokenizer.json from DipakBundheliya/ner_bert_model: direct link, hf CLI and curl.
- Browser
- Download file 669 kB
-
https://huggingface.co/DipakBundheliya/ner_bert_model/resolve/896388a7fa1d1a61ae8c0dabc0e28258461bed72/tokenizer.json
- Command line
-
hf download hf://DipakBundheliya/ner_bert_model@896388a7fa1d1a61ae8c0dabc0e28258461bed72/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/DipakBundheliya/ner_bert_model/resolve/896388a7fa1d1a61ae8c0dabc0e28258461bed72/tokenizer.json
669 kB
File too large to display, you can check the raw version instead.