Zero-Shot Classification
Transformers
ONNX
Safetensors
English
watersheep
feature-extraction
decision-model
calibration
multi-label
custom_code
Instructions to use samratduttaofficial/WaterSheep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samratduttaofficial/WaterSheep with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="samratduttaofficial/WaterSheep", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("samratduttaofficial/WaterSheep", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download onnx/model_quantized.onnx from samratduttaofficial/WaterSheep: direct link, hf CLI and curl.
- Browser
- Download file 158 MB
-
https://huggingface.co/samratduttaofficial/WaterSheep/resolve/main/onnx/model_quantized.onnx
- Command line
-
hf download hf://samratduttaofficial/WaterSheep/onnx/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/samratduttaofficial/WaterSheep/resolve/main/onnx/model_quantized.onnx
158 MB
- Xet hash:
- 94d32545f010f95b11d8bb47a7d528abd7cc482a479e8fade844336527a35daf
- Size of remote file:
- 158 MB
- SHA256:
- 160ae91868f919ae0ddc6d1bd509b17d93194102aa33a466095469fbf04dc25b
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