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results/output/output.txt

@@ -1,28 +1,28 @@
-PredictionOutput(predictions=(array([[-6.3505344, -4.6940236, -6.667218 , ..., -7.764988 , -7.560247 ,
-        -6.9135256],
-       [-6.390448 , -5.461608 , -5.2113676, ..., -6.760744 , -6.893809 ,
-        -6.7644644],
-       [-5.5488567, -6.5585084, -7.0522833, ..., -7.5694523, -8.397385 ,
-        -7.4925356],
+PredictionOutput(predictions=(array([[-6.3246927, -5.654762 , -6.941552 , ..., -7.5898046, -7.740813 ,
+        -6.1349645],
+       [-4.834963 , -3.745935 , -3.2166266, ..., -7.0255365, -7.0781636,
+        -6.014003 ],
+       [-7.103022 , -7.007348 , -6.887471 , ..., -7.0100193, -7.818549 ,
+        -6.481393 ],
        ...,
-       [-7.66147  , -7.4324007, -5.6565456, ..., -8.473821 , -8.389811 ,
-        -7.2251577],
-       [-6.7677727, -6.7053394, -6.733639 , ..., -7.688794 , -8.541024 ,
-        -7.0397964],
-       [-6.2875986, -5.957848 , -5.894283 , ..., -7.3068485, -6.605853 ,
-        -6.4457445]], dtype=float32), array([[-4.9971795, -5.180228 , -4.646949 , ..., -6.49852  , -7.406561 ,
-        -6.010501 ],
-       [-6.2658114, -6.755742 , -6.2572107, ..., -7.732583 , -6.957442 ,
-        -6.3796597],
-       [-5.1496167, -6.935574 , -6.111067 , ..., -7.1776004, -6.7117023,
-        -6.2112265],
+       [-7.2380886, -6.9530168, -5.9637184, ..., -8.142187 , -8.049299 ,
+        -6.345413 ],
+       [-6.465349 , -5.6243043, -5.1069393, ..., -7.737566 , -8.114063 ,
+        -6.3944073],
+       [-5.9997034, -5.400169 , -5.759265 , ..., -7.4665504, -7.329249 ,
+        -6.0164046]], dtype=float32), array([[-6.2120194, -5.8926864, -5.0663767, ..., -7.058113 , -7.489437 ,
+        -6.2375684],
+       [-4.927747 , -4.759913 , -4.731585 , ..., -6.1144805, -5.997227 ,
+        -6.007777 ],
+       [-6.646918 , -6.937681 , -6.4157243, ..., -6.842525 , -6.376035 ,
+        -6.4495506],
        ...,
-       [-7.3494554, -7.435437 , -7.239635 , ..., -7.5206027, -7.6423273,
-        -6.394255 ],
-       [-5.5616317, -6.4712167, -6.4952044, ..., -7.5600934, -6.360442 ,
-        -6.051618 ],
-       [-6.018609 , -6.45537  , -6.3951693, ..., -7.610481 , -7.6773567,
-        -6.512492 ]], dtype=float32)), label_ids=(array([ 26,  15,  66,  11,  11,  43,  11,  19,   7,  25,  24,  26, 359,
+       [-7.0611844, -7.0813217, -7.2140346, ..., -7.3944006, -7.430308 ,
+        -6.522217 ],
+       [-6.0665016, -5.8312573, -5.276547 , ..., -7.2962427, -7.2165346,
+        -6.3499103],
+       [-6.1140056, -6.394855 , -6.4566636, ..., -7.1827383, -7.099182 ,
+        -5.9224005]], dtype=float32)), label_ids=(array([ 26,  15,  66,  11,  11,  43,  11,  19,   7,  25,  24,  26, 359,
          0,  15, 112,  19, 228,  89,  13,   7,  22,  31,  16,  36,  70,
         23,  34,  32,  56,  29,  53,  15,  32,  49,  41, 151,  24,  17,
         89,  64,  78,  34, 152,  21, 194,  52,  19,  10,  92,  52,  24,
@@ -152,4 +152,4 @@ PredictionOutput(predictions=(array([[-6.3505344, -4.6940236, -6.667218 , ..., -
         13,  53,  56,  63,  96,  17,  33, 121,  60,  56,  35,  23,  45,
        162,  69,  22,  21,  11,  67,  28,  36,  30,  80,  18,  35,  17,
         39,  28,  43, 332,  15,  31,  28,  78,  49,  52,  46,  49,  25,
-        41,  32,  18,  46,  11,  66,  10,   8,  34,  23,  33,  12])), metrics={'test_loss': 1.2703553438186646, 'test_exact_match': 54.14235705950992, 'test_f1': 66.94243382085831, 'test_runtime': 41.1366, 'test_samples_per_second': 20.833, 'test_steps_per_second': 2.625})
+        41,  32,  18,  46,  11,  66,  10,   8,  34,  23,  33,  12])), metrics={'test_loss': 1.3518387079238892, 'test_exact_match': 51.5752625437573, 'test_f1': 63.9944639976603, 'test_runtime': 46.2175, 'test_samples_per_second': 18.543, 'test_steps_per_second': 2.337})