Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use datauma/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use datauma/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="datauma/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("datauma/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("datauma/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from datauma/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/datauma/bert-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://datauma/bert-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/datauma/bert-finetuned-ner/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- f172f6615b1144fbf2df248f82df165e18b994d1d5d5b1d9a05ca45c47d4e0a0
- Size of remote file:
- 431 MB
- SHA256:
- e5fede34e76526d2617a897da2f2d501759ad81da8566c4e3eb4db7eba7705be
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.