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results/output/output.txt

@@ -0,0 +1,155 @@
+PredictionOutput(predictions=(array([[-7.6776986, -7.077825 , -6.7286625, ..., -8.411105 , -8.054404 ,
+        -6.904107 ],
+       [-5.3674793, -5.533578 , -4.732835 , ..., -8.295293 , -7.8776445,
+        -6.6542554],
+       [-7.227771 , -6.5405884, -5.60233  , ..., -8.069541 , -8.297742 ,
+        -6.7834263],
+       ...,
+       [-6.603417 , -5.7521057, -5.5941296, ..., -7.9073157, -7.974876 ,
+        -6.8349013],
+       [-2.5827937, -4.6935344, -3.5923223, ..., -8.226607 , -7.20708  ,
+        -6.5900707],
+       [-4.113274 , -5.181159 , -4.6069164, ..., -7.1645947, -7.285424 ,
+        -6.61419  ]], dtype=float32), array([[-6.7221494, -6.793156 , -7.8639345, ..., -8.302709 , -8.585441 ,
+        -6.1055636],
+       [-4.158317 , -6.1881523, -7.7105107, ..., -8.127259 , -8.522727 ,
+        -5.5982375],
+       [-6.3788066, -6.5608277, -8.102302 , ..., -8.544028 , -8.346045 ,
+        -5.8999496],
+       ...,
+       [-5.8564353, -7.3260837, -7.3585167, ..., -8.106247 , -8.2489805,
+        -5.7838345],
+       [-1.611078 , -4.7105117, -3.008606 , ..., -6.9613056, -7.8744817,
+        -5.2358365],
+       [-2.4467976, -5.2883115, -4.5215383, ..., -7.6404977, -7.3114285,
+        -5.291327 ]], dtype=float32)), label_ids=(array([ 41,   8,  10,   8,  39,   6,  59,   9,  14,  15,  73,   8,  51,
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+        11,  17,  96,  12,  66,   9,  17,   7, 126,   8,  44,  13,  13,
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+        24, 104,  25,   7,   9,  18,  10,  33,   8, 203,  78,   9, 104,
+        13,  21,  17]), array([ 41,   7,   9,   7,  51,   5,  71,   8,  17,  14,  75,   7,  52,
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+       199,   8,  11,   8,  13,  10,  10,  11,  26,   9,  20,   7,   8,
+        11,  59,  18,  29,  25,  78,  25,  22, 140,  31,  96,  20,  17,
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+        12,  46,  16])), metrics={'test_loss': 1.0233564376831055, 'test_exact_match': 67.9245283018868, 'test_f1': 74.03608244980539, 'test_runtime': 47.2267, 'test_samples_per_second': 17.956, 'test_steps_per_second': 2.244})