Instructions to use akmlva/bge-small-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akmlva/bge-small-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="akmlva/bge-small-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("akmlva/bge-small-ner") model = AutoModelForTokenClassification.from_pretrained("akmlva/bge-small-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from akmlva/bge-small-ner: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/akmlva/bge-small-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://akmlva/bge-small-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/akmlva/bge-small-ner/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 07be832219cbbf23e3a4822abdeed0151fafbce13ffc86c8331f8693c3427132
- Size of remote file:
- 5.2 kB
- SHA256:
- 5a2c22dbab62d8d5720e970d46c1a004f7564f7c883025e9b547f5448235456a
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