[20260612_231115_xlm_roberta_large_v38g] metrics
Browse files
metrics/20260612_231115_xlm_roberta_large_v38g_metrics.json
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{
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"run_id": "20260612_231115_xlm_roberta_large_v38g",
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"model": "xlm-roberta-large",
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"dataset": "Nexus-Analytics/multilingual-hate-detection-dataset_v38g",
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"labels": {
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"hate": 0,
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"normal": 1
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},
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"hyperparameters": {
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"learning_rate": 2e-05,
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"batch_size": 16,
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"weight_decay": 0.01,
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"warmup_ratio": 0.1,
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"epochs": 8,
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"max_len": 256,
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"seed": 42,
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"label_smoothing": 0.0
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},
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"class_weights": {
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"hate": 0.977995231472283,
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"normal": 1.0230177699262184
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},
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"splits": {
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"train": 19689,
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"val": 3937,
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"test": 3937
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},
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"results": {
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"overall": {
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"accuracy": 0.9903,
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"f1_macro": 0.9903,
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"f1_weighted": 0.9903,
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"per_class": {
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"hate": {
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"precision": 0.99,
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"recall": 0.991,
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"f1": 0.9905,
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| 38 |
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"support": 2000
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},
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"normal": {
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"precision": 0.9907,
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"recall": 0.9897,
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"f1": 0.9902,
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"support": 1937
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}
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},
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"confusion_matrix": [
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[
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1982,
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18
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],
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[
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},
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"en": {
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"accuracy": 0.9982,
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"f1_macro": 0.9982,
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| 61 |
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"f1_weighted": 0.9982,
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"per_class": {
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"hate": {
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"precision": 0.9991,
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| 65 |
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"recall": 0.9974,
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| 66 |
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"f1": 0.9983,
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| 67 |
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"support": 1165
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},
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"normal": {
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| 70 |
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"precision": 0.9972,
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| 71 |
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"recall": 0.9991,
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"f1": 0.9981,
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"support": 1069
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}
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},
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"confusion_matrix": [
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[
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1162,
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3
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],
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[
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1,
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]
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]
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},
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"ar": {
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| 88 |
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"accuracy": 0.98,
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"f1_macro": 0.98,
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| 90 |
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"f1_weighted": 0.98,
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| 91 |
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"per_class": {
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"hate": {
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"precision": 0.9774,
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| 94 |
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"recall": 0.982,
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| 95 |
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"f1": 0.9797,
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| 96 |
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"support": 835
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| 97 |
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},
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"normal": {
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| 99 |
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"precision": 0.9826,
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"recall": 0.9781,
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| 101 |
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"f1": 0.9804,
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| 102 |
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"support": 868
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}
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},
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"confusion_matrix": [
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[
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820,
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],
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[
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19,
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849
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]
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]
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}
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}
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}
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