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 pytorch_model.bin from kbalde/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/kbalde/bert-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kbalde/bert-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kbalde/bert-finetuned-ner/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 75c7ce6df8a19dfbf49e7b8a6fa13782d0f203ae6f18bf2b6bc456e46dc31a77
- Size of remote file:
- 431 MB
- SHA256:
- 6559de9a1def830ab36dbc8f40b2b8ed9457b7bc607b03245bd839c82a1b24e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.