Instructions to use ksaml/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ksaml/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ksaml/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ksaml/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("ksaml/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ksaml/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/ksaml/bert-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ksaml/bert-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ksaml/bert-finetuned-ner/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 9221db23e44180937d14909bfb3c2144cbe2cc44683dce575d4b43b78e820f1b
- Size of remote file:
- 431 MB
- SHA256:
- a31e1953c18863f324aa713e1e79aab6dfcb47063d943b4529fff03ece26953e
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