Zero-Shot Classification
Transformers
ONNX
Safetensors
English
watersheep
feature-extraction
decision-model
calibration
multi-label
jev
jev-alternative
system-one
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
File size: 263 Bytes
07f1ef2 | 1 2 3 4 5 6 7 8 9 10 | from transformers import pipeline
class EndpointHandler:
def __init__(self, path=""):
self.pipe = pipeline(model=path, trust_remote_code=True)
def __call__(self, data):
return self.pipe(data["inputs"], **(data.get("parameters") or {}))
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