Zero-Shot Classification
Transformers
ONNX
English
nli
compliance
popia
south-africa
data-protection
ai-act
Eval Results (legacy)
Instructions to use labrat-aiko/nli-popia-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use labrat-aiko/nli-popia-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="labrat-aiko/nli-popia-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("labrat-aiko/nli-popia-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download calibration.json from labrat-aiko/nli-popia-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.23 kB
-
https://huggingface.co/labrat-aiko/nli-popia-v2/resolve/main/calibration.json
- Command line
-
hf download hf://labrat-aiko/nli-popia-v2/calibration.json
-
curl -L -o calibration.json https://huggingface.co/labrat-aiko/nli-popia-v2/resolve/main/calibration.json
2.23 kB
| { | |
| "model": "labrat-aiko/nli-popia-v2", | |
| "artifact": "onnx/model.onnx", | |
| "method": "single-parameter temperature scaling (Guo et al. 2017) fit by NLL minimization with scipy.optimize.minimize_scalar (bounded, brent)", | |
| "temperature": 2.5492217429081805, | |
| "fit_nll": 0.6346972462907677, | |
| "ece_pre": 0.17068321848737786, | |
| "ece_post": 0.07499005825355136, | |
| "n_fit": 81, | |
| "n_test": 116, | |
| "n_bins": 10, | |
| "fit_frac": 0.4, | |
| "seed": 42, | |
| "eval_paths": [ | |
| "out/nli-popia-v2/eval.jsonl", | |
| "out/nli-popia-v2/eval_v2.jsonl" | |
| ], | |
| "test_bins": [ | |
| { | |
| "lo": 0.0, | |
| "hi": 0.1, | |
| "count": 0, | |
| "accuracy": 0.0, | |
| "confidence": 0.0, | |
| "gap": 0.0 | |
| }, | |
| { | |
| "lo": 0.1, | |
| "hi": 0.2, | |
| "count": 0, | |
| "accuracy": 0.0, | |
| "confidence": 0.0, | |
| "gap": 0.0 | |
| }, | |
| { | |
| "lo": 0.2, | |
| "hi": 0.30000000000000004, | |
| "count": 0, | |
| "accuracy": 0.0, | |
| "confidence": 0.0, | |
| "gap": 0.0 | |
| }, | |
| { | |
| "lo": 0.30000000000000004, | |
| "hi": 0.4, | |
| "count": 1, | |
| "accuracy": 0.0, | |
| "confidence": 0.3856368064880371, | |
| "gap": 0.3856368064880371 | |
| }, | |
| { | |
| "lo": 0.4, | |
| "hi": 0.5, | |
| "count": 6, | |
| "accuracy": 0.8333333333333334, | |
| "confidence": 0.48217907547950745, | |
| "gap": 0.3511542578538259 | |
| }, | |
| { | |
| "lo": 0.5, | |
| "hi": 0.6000000000000001, | |
| "count": 6, | |
| "accuracy": 0.16666666666666666, | |
| "confidence": 0.541632354259491, | |
| "gap": 0.37496568759282434 | |
| }, | |
| { | |
| "lo": 0.6000000000000001, | |
| "hi": 0.7000000000000001, | |
| "count": 10, | |
| "accuracy": 0.8, | |
| "confidence": 0.6515324711799622, | |
| "gap": 0.1484675288200379 | |
| }, | |
| { | |
| "lo": 0.7000000000000001, | |
| "hi": 0.8, | |
| "count": 31, | |
| "accuracy": 0.7741935483870968, | |
| "confidence": 0.7634683847427368, | |
| "gap": 0.010725163644359936 | |
| }, | |
| { | |
| "lo": 0.8, | |
| "hi": 0.9, | |
| "count": 60, | |
| "accuracy": 0.8833333333333333, | |
| "confidence": 0.8509461283683777, | |
| "gap": 0.03238720496495562 | |
| }, | |
| { | |
| "lo": 0.9, | |
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| "count": 2, | |
| "accuracy": 1.0, | |
| "confidence": 0.9019486904144287, | |
| "gap": 0.09805130958557129 | |
| } | |
| ] | |
| } |