LiteRT
LiteRT
Polish
English
llama
android
on-device
npu
qualcomm
typed-decisions
classification
calibration
polish
Instructions to use SearchingMan/basal-2.0-mini-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use SearchingMan/basal-2.0-mini-LiteRT with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 3,541 Bytes
49122de | 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | {
"prompt": 2,
"orders": 1,
"method": "temperature per question type and per option-count bucket (choice), by NLL, fitted on the out-of-distribution calibration split only; thresholds per type: the most permissive threshold whose accepted calibration decisions have a one-sided 95% Clopper-Pearson upper bound <= max_error; a type without one defers",
"temperature_per_prim": {
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"noul": 1.8077142767822016,
"score": 1.9152065613971467
},
"temperature_per_bucket": {
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"11-26": 0.9576032806985735,
"27-255": 0.8908987181403392
}
},
"thresholds": {
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"method": "per type: the most permissive threshold whose accepted calibration decisions have a one-sided 95% Clopper-Pearson upper bound <= max_error; a type without one defers",
"per_type": {
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},
"noul": {
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},
"score": {
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"heldout_error": null
}
},
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},
"0.05": {
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"method": "per type: the most permissive threshold whose accepted calibration decisions have a one-sided 95% Clopper-Pearson upper bound <= max_error; a type without one defers",
"per_type": {
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"heldout_error": 0.02714164546225617
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"noul": {
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"heldout_error": 0.032362459546925515
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},
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},
"gates": {
"ece_lofo": {
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"n": 11874,
"gate": 0.05,
"pass": true
},
"noul": {
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"score": {
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"pass": true
}
},
"ece_worst_fold": 0.09937081230552766,
"worst_fold": "agent",
"automated_at": {
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"0.05": 0.10458360232408005
},
"automated_error": {
"0.01": null,
"0.05": 0.029439696106362767
},
"note": "held-out numbers: every evaluation row predicted by the fit without its family"
},
"test": {
"T": {
"choice": {
"acc": 0.697321879737241,
"ece": 0.02472238570054493,
"n": 11874
},
"noul": {
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"score": {
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},
"all": {
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}
},
"note": "evaluation pool (development and test splits), full fit"
},
"n": {
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"many": 1500,
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},
"engine": "fast",
"fitted_on": {
"split": "out-of-distribution calibration split (sealed-seed BH-, JDI- and agent-shaped families, many-option items)",
"orders": 1,
"readouts": "basal-serve --mode fast, bf16"
},
"certified_on": "the same split",
"evaluated_on": "development and test splits, leave-one-family-out and leave-one-language-out"
}
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