Instructions to use kalex/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kalex/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kalex/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kalex/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("kalex/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from kalex/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/kalex/bert-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://kalex/bert-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/kalex/bert-finetuned-ner/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- d7b4fbddad25e72717625fd6f4167fbe1ee98f0291e138debb06b84389c9ddbb
- Size of remote file:
- 436 MB
- SHA256:
- f9cded3cb3454490cee1c8efa4e7aa90b062d01da1c601bd0f1993c0477f44f1
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