Instructions to use raygx/GNePT-NepSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raygx/GNePT-NepSA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raygx/GNePT-NepSA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("raygx/GNePT-NepSA") model = AutoModelForSequenceClassification.from_pretrained("raygx/GNePT-NepSA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from raygx/GNePT-NepSA: direct link, hf CLI and curl.
- Browser
- Download file 497 MB
-
https://huggingface.co/raygx/GNePT-NepSA/resolve/main/tf_model.h5
- Command line
-
hf download hf://raygx/GNePT-NepSA/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/raygx/GNePT-NepSA/resolve/main/tf_model.h5
497 MB
- Xet hash:
- 102a456f871da05a7101fbc5fa167a391e993f1669d6ab14e35efd80af0ace97
- Size of remote file:
- 497 MB
- SHA256:
- 52c8ba370824f38303e7ee7a00d384a266a6c884bc1c0a40d7bf8d50e72f0b39
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