Text Classification
Transformers
ONNX
Safetensors
Japanese
English
Chinese
GLiClass
gliclass
choice-classification
experimental
Instructions to use sugarknight/erabi-practical-v1-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sugarknight/erabi-practical-v1-experimental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sugarknight/erabi-practical-v1-experimental")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sugarknight/erabi-practical-v1-experimental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from sugarknight/erabi-practical-v1-experimental: direct link, hf CLI and curl.
- Browser
- Download file 1.75 GB
-
https://huggingface.co/sugarknight/erabi-practical-v1-experimental/resolve/main/model.safetensors
- Command line
-
hf download hf://sugarknight/erabi-practical-v1-experimental/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/sugarknight/erabi-practical-v1-experimental/resolve/main/model.safetensors
1.75 GB
- Xet hash:
- c8a92a5d2632a48b31fd4e93ff2fdc3d8bd6a2f69ecfef4de1132a2021647b2c
- Size of remote file:
- 1.75 GB
- SHA256:
- 959c7c38ff00c40f39ac5ad0e40344117e8caa14dadc511b2128e6f18ff06934
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