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")# pip install -U transformers accelerate # 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 training_args.bin from ksaml/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/ksaml/bert-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://ksaml/bert-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ksaml/bert-finetuned-ner/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- ccaeab19d6a13c00865d355bf5bf00dde969f501815ee37a212b8eed81f8a5a6
- Size of remote file:
- 3.52 kB
- SHA256:
- 70c70fe91d977d468ee36db3fd376bbf586548146182808eb87be98adcd7ff09
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