Instructions to use NekoCoders/x5-ner-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NekoCoders/x5-ner-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="NekoCoders/x5-ner-final")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("NekoCoders/x5-ner-final") model = AutoModelForTokenClassification.from_pretrained("NekoCoders/x5-ner-final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.onnx_data from NekoCoders/x5-ner-final: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/NekoCoders/x5-ner-final/resolve/main/model.onnx_data
- Command line
-
hf download hf://NekoCoders/x5-ner-final/model.onnx_data
-
curl -L -o model.onnx_data https://huggingface.co/NekoCoders/x5-ner-final/resolve/main/model.onnx_data
2.24 GB
- Xet hash:
- 9576cab375b95ad7e14cf6940a07c374eec50fe9cc51ceb9ff49444501ea6f95
- Size of remote file:
- 2.24 GB
- SHA256:
- 8c7f3b4ae25ba74bd80ce367da4b1e256f1ce5d37d04dd248c53ca7a4f779a77
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