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
File size: 661 Bytes
07f1ef2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"format": 2,
"run_id": "run_20260928-070402",
"encoder": "answerdotai/ModernBERT-base",
"tokenizer": "hf",
"max_len": 512,
"max_question_tokens": 96,
"max_option_tokens": 32,
"max_options": 10,
"head_layers": 1,
"temperatures": {
"binary": 1.0594,
"choice": 1.0631,
"score": 1.0022,
"multi": 0.8276
},
"metrics": {
"test_calibrated": {
"acc": 0.7778176740474766,
"ece": 0.025550670609045353
},
"zeroshot_calibrated": {
"acc": 0.6121272507294855,
"ece": 0.04295260971909359
}
},
"multi_threshold": 0.5,
"created": "20260928-125453",
"name": "watersheep-20260928-125452"
}
|