Token Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use Kriyans/Bert-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kriyans/Bert-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Kriyans/Bert-NER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Kriyans/Bert-NER") model = AutoModelForTokenClassification.from_pretrained("Kriyans/Bert-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Kriyans/Bert-NER: direct link, hf CLI and curl.
- Browser
- Download file 266 MB
-
https://huggingface.co/Kriyans/Bert-NER/resolve/refs%2Fpr%2F21/pytorch_model.bin
- Command line
-
hf download hf://Kriyans/Bert-NER@refs/pr/21/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Kriyans/Bert-NER/resolve/refs%2Fpr%2F21/pytorch_model.bin
266 MB
- Xet hash:
- 6d2f5bb8cf65f70d65c05e3f5d029176f47419d86daa531dbb2f8354dbd088a1
- Size of remote file:
- 266 MB
- SHA256:
- 9e7e005264dc510190e5bf9110316d184cf0a2ebfe442074f8cf5efab1260de0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.