Instructions to use Nooshika/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nooshika/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Nooshika/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Nooshika/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Nooshika/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Nooshika/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/Nooshika/bert-finetuned-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://Nooshika/bert-finetuned-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Nooshika/bert-finetuned-ner/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- b64eff4710e7ed142b71f85b0a24c4af1df643608bd8340a9081e7815384fa2a
- Size of remote file:
- 5.24 kB
- SHA256:
- 876c6e5333a16befc9771e6c8f5f24347af0d70feb679d5a4b8771fc6f2f0efd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.