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{
  "loss": 0.5047465368745264,
  "accuracy": 0.888235294117647,
  "macro_f1": 0.8805959114265647,
  "labels": [
    "audit_accountability",
    "unrelated"
  ],
  "precision": [
    0.8307692307692308,
    0.9238095238095239
  ],
  "recall": [
    0.8709677419354839,
    0.8981481481481481
  ],
  "f1": [
    0.8503937007874016,
    0.9107981220657277
  ],
  "support": [
    62,
    108
  ],
  "confusion_matrix": [
    [
      54,
      8
    ],
    [
      11,
      97
    ]
  ],
  "classification_report": "                      precision    recall  f1-score   support\n\naudit_accountability       0.83      0.87      0.85        62\n           unrelated       0.92      0.90      0.91       108\n\n            accuracy                           0.89       170\n           macro avg       0.88      0.88      0.88       170\n        weighted avg       0.89      0.89      0.89       170\n"
}

	{
	"loss": 0.5047465368745264,
	"accuracy": 0.888235294117647,
	"macro_f1": 0.8805959114265647,
	"labels": [
	"audit_accountability",
	"unrelated"
	],
	"precision": [
	0.8307692307692308,
	0.9238095238095239
	],
	"recall": [
	0.8709677419354839,
	0.8981481481481481
	],
	"f1": [
	0.8503937007874016,
	0.9107981220657277
	],
	"support": [
	62,
	108
	],
	"confusion_matrix": [
	[
	54,
	8
	],
	[
	11,
	97
	]
	],
	"classification_report": " precision recall f1-score support\n\naudit_accountability 0.83 0.87 0.85 62\n unrelated 0.92 0.90 0.91 108\n\n accuracy 0.89 170\n macro avg 0.88 0.88 0.88 170\n weighted avg 0.89 0.89 0.89 170\n"
	}