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/c6c7acf0280b8af5cce6c2a18e9a215f7d2eae57/model.safetensors
- Command line
-
hf download hf://sugarknight/erabi-practical-v1-experimental@c6c7acf0280b8af5cce6c2a18e9a215f7d2eae57/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/sugarknight/erabi-practical-v1-experimental/resolve/c6c7acf0280b8af5cce6c2a18e9a215f7d2eae57/model.safetensors
1.75 GB
- Xet hash:
- 7e192f23b17d908c38a303b97d22531a42115d954b0a7182e1ec947c095a1f29
- Size of remote file:
- 1.75 GB
- SHA256:
- 1ae38ef6c1103f8c021b0d3a974f3b1aedc42d89832d11761e74b95664216251
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