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SimoneAstarita
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it-no-bio-20251014-t16

Text Classification
Transformers
Safetensors
Italian
new
xlm-roberta
multilingual
social-media
custom_code
text-embeddings-inference
Model card Files
xet
 Community
it-no-bio-20251014-t16
File size: 4,217 Bytes
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{
  "overall": {
    "at_0.5": {
      "precision_macro": 0.8700892857142857,
      "recall_macro": 0.9051808406647117,
      "f1_macro": 0.8860139860139861,
      "precision_weighted": 0.9312226117440842,
      "recall_weighted": 0.9263803680981595,
      "f1_weighted": 0.9280449611737956,
      "accuracy": 0.9263803680981595,
      "confusion_matrix": [
        [
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        ],
        [
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        ]
      ],
      "classification_report": "              precision    recall  f1-score   support\n\n no-recl (0)     0.9688    0.9394    0.9538       132\n    recl (1)     0.7714    0.8710    0.8182        31\n\n    accuracy                         0.9264       163\n   macro avg     0.8701    0.9052    0.8860       163\nweighted avg     0.9312    0.9264    0.9280       163\n",
      "threshold": 0.5
    },
    "at_best_global": {
      "precision_macro": 0.9203323558162269,
      "recall_macro": 0.9203323558162269,
      "f1_macro": 0.9203323558162269,
      "precision_weighted": 0.950920245398773,
      "recall_weighted": 0.950920245398773,
      "f1_weighted": 0.950920245398773,
      "accuracy": 0.950920245398773,
      "confusion_matrix": [
        [
          128,
          4
        ],
        [
          4,
          27
        ]
      ],
      "classification_report": "              precision    recall  f1-score   support\n\n no-recl (0)     0.9697    0.9697    0.9697       132\n    recl (1)     0.8710    0.8710    0.8710        31\n\n    accuracy                         0.9509       163\n   macro avg     0.9203    0.9203    0.9203       163\nweighted avg     0.9509    0.9509    0.9509       163\n",
      "threshold": 0.65
    },
    "at_best_by_lang": {
      "precision_macro": 0.9203323558162269,
      "recall_macro": 0.9203323558162269,
      "f1_macro": 0.9203323558162269,
      "precision_weighted": 0.950920245398773,
      "recall_weighted": 0.950920245398773,
      "f1_weighted": 0.950920245398773,
      "accuracy": 0.950920245398773,
      "confusion_matrix": [
        [
          128,
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        ],
        [
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          27
        ]
      ],
      "classification_report": "              precision    recall  f1-score   support\n\n no-recl (0)     0.9697    0.9697    0.9697       132\n    recl (1)     0.8710    0.8710    0.8710        31\n\n    accuracy                         0.9509       163\n   macro avg     0.9203    0.9203    0.9203       163\nweighted avg     0.9509    0.9509    0.9509       163\n",
      "thresholds_by_lang": {
        "it": 0.65
      }
    }
  },
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    "global_best": {
      "threshold": 0.65,
      "f1_macro": 0.9203323558162269,
      "precision_macro": 0.9203323558162269,
      "recall_macro": 0.9203323558162269
    },
    "by_lang_best": {
      "it": {
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        "f1_macro": 0.9203323558162269,
        "precision_macro": 0.9203323558162269,
        "recall_macro": 0.9203323558162269
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        "n": 163,
        "accuracy": 0.9263803680981595,
        "f1_macro": 0.8860139860139861,
        "precision_macro": 0.8700892857142857,
        "recall_macro": 0.9051808406647117,
        "f1_weighted": 0.9280449611737956,
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        "f1_macro": 0.9203323558162269,
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        "f1_weighted": 0.950920245398773,
        "precision_weighted": 0.950920245398773,
        "recall_weighted": 0.950920245398773
      }
    ],
    "at_best_by_lang": [
      {
        "lang": "it",
        "n": 163,
        "accuracy": 0.950920245398773,
        "f1_macro": 0.9203323558162269,
        "precision_macro": 0.9203323558162269,
        "recall_macro": 0.9203323558162269,
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}