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results/output/output.txt

@@ -1,7 +1,7 @@
-PredictionOutput(predictions=array([[ 0.92685175,  1.0960854 , -2.0774417 ],
-       [ 3.4964037 , -1.2045288 , -2.0796945 ],
-       [-0.61372983,  1.8564193 , -1.2534584 ],
+PredictionOutput(predictions=array([[ 1.8757781 ,  0.10563973, -1.8769046 ],
+       [ 2.5787184 , -1.5079138 , -1.0805748 ],
+       [ 0.2447584 ,  1.4224131 , -1.8482531 ],
        ...,
-       [-3.5772083 ,  4.0273037 , -1.4902194 ],
-       [ 3.623575  ,  0.62911695, -4.085694  ],
-       [-2.6262834 ,  3.6536193 , -1.7664702 ]], dtype=float32), label_ids=array([1, 0, 0, ..., 1, 0, 1]), metrics={'test_loss': 0.6572218537330627, 'test_accuracy': 0.8013500482160077, 'test_f1': 0.8029574824687489, 'test_runtime': 42.1188, 'test_samples_per_second': 123.104, 'test_steps_per_second': 15.409})
+       [-1.7147496 , -3.451846  ,  4.9482317 ],
+       [-3.355991  , -2.0938346 ,  4.969881  ],
+       [-2.535666  , -2.9212089 ,  5.2116556 ]], dtype=float32), label_ids=array([1, 0, 0, ..., 2, 2, 2]), metrics={'test_loss': 0.5056426525115967, 'test_accuracy': 0.8241831149444352, 'test_f1': 0.8267838848593851, 'test_runtime': 89.8547, 'test_samples_per_second': 67.097, 'test_steps_per_second': 4.196})