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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sugarknight/erabi-practical-v1-experimental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add Practical V1 GPU ONNX FP16 variant
Browse files- onnx/fp16/model.onnx +3 -0
onnx/fp16/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:84cd8cc9c4830ba2156185a6e9aea3d151733fc0f258fc45a77d00e6289483a7
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size 879507317
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