Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use jperezv/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jperezv/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jperezv/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jperezv/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("jperezv/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jperezv/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/jperezv/bert-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jperezv/bert-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jperezv/bert-finetuned-ner/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 6e7ca0c89953f2122ac8939feff9a422730eb9458d6d6757df07365cfed93d27
- Size of remote file:
- 431 MB
- SHA256:
- 10a7f7832150ed4a72c022070d07338e20e67457cb8b7e3b2d68b176b64770cb
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