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results/output/output.txt

@@ -0,0 +1,155 @@
+PredictionOutput(predictions=(array([[ -4.7763996,  -7.3299537,  -8.229666 , ...,  -9.947512 ,
+         -9.985412 ,  -9.970522 ],
+       [ -6.152384 ,  -8.710275 ,  -7.204643 , ..., -10.221756 ,
+        -10.231641 , -10.225595 ],
+       [ -5.463583 ,  -8.39563  ,  -7.535887 , ..., -10.297186 ,
+        -10.296385 , -10.29574  ],
+       ...,
+       [ -5.295748 ,  -8.208842 ,  -7.796765 , ...,  -9.975782 ,
+         -9.978882 ,  -9.967005 ],
+       [ -6.0375853,  -7.9741883,  -7.2292676, ...,  -9.839136 ,
+         -9.851196 ,  -9.853931 ],
+       [ -5.582914 ,  -8.257044 ,  -8.031302 , ..., -10.088282 ,
+        -10.095609 , -10.094077 ]], dtype=float32), array([[-4.1512284, -6.8793297, -6.9050155, ..., -8.972267 , -8.9641285,
+        -8.972673 ],
+       [-5.1330533, -8.541055 , -6.6401396, ..., -9.634152 , -9.618495 ,
+        -9.619524 ],
+       [-3.919358 , -8.057231 , -7.1191206, ..., -9.548897 , -9.54535  ,
+        -9.548545 ],
+       ...,
+       [-4.591824 , -7.922638 , -8.018359 , ..., -9.215978 , -9.208929 ,
+        -9.2211275],
+       [-5.152248 , -8.594802 , -8.105948 , ..., -9.408519 , -9.404899 ,
+        -9.399779 ],
+       [-4.6275067, -8.129721 , -7.8613944, ..., -9.534545 , -9.514279 ,
+        -9.518814 ]], dtype=float32)), label_ids=(array([ 30,  15,  70,  12,  12,  49,  11,  19,   7,  25,  23,  32, 390,
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+        42,  31,  16,  46,  11,  62,  12,   8,  33,  25,  33,  12])), metrics={'test_loss': 1.282210350036621, 'test_exact_match': 55.542590431738624, 'test_f1': 69.1131446166609, 'test_runtime': 85.5425, 'test_samples_per_second': 10.018, 'test_steps_per_second': 1.263})