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  1. README.md +115 -115
  2. config.json +2 -3
  3. pytorch_model.bin +2 -2
  4. training_args.bin +2 -2
README.md CHANGED
@@ -1,6 +1,6 @@
1
  ---
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  license: mit
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- base_model: xlm-roberta-base
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  tags:
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  - generated_from_trainer
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  datasets:
@@ -23,10 +23,10 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.6663001649257834
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  - name: F1
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  type: f1
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- value: 0.6006849005734807
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -34,11 +34,11 @@ should probably proofread and complete it, then remove this comment. -->
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  # scenario-TCR_data-en-massive_all_1_1
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- This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the massive dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.7850
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- - Accuracy: 0.6663
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- - F1: 0.6007
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  ## Model description
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@@ -69,114 +69,114 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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- | No log | 0.28 | 100 | 2.8668 | 0.3066 | 0.1117 |
73
- | No log | 0.56 | 200 | 2.0971 | 0.4939 | 0.2673 |
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- | No log | 0.83 | 300 | 1.7751 | 0.5692 | 0.3808 |
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- | No log | 1.11 | 400 | 1.5796 | 0.6089 | 0.4444 |
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- | 1.8216 | 1.39 | 500 | 1.5696 | 0.6234 | 0.4919 |
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- | 1.8216 | 1.67 | 600 | 1.6751 | 0.6098 | 0.5122 |
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- | 1.8216 | 1.94 | 700 | 1.4933 | 0.6292 | 0.5400 |
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- | 1.8216 | 2.22 | 800 | 1.4954 | 0.6431 | 0.5461 |
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- | 1.8216 | 2.5 | 900 | 1.4810 | 0.6465 | 0.5400 |
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- | 0.5885 | 2.78 | 1000 | 1.7160 | 0.6084 | 0.5306 |
82
- | 0.5885 | 3.06 | 1100 | 1.4351 | 0.6632 | 0.5878 |
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- | 0.5885 | 3.33 | 1200 | 1.5652 | 0.6415 | 0.5632 |
84
- | 0.5885 | 3.61 | 1300 | 1.5121 | 0.6593 | 0.5666 |
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- | 0.5885 | 3.89 | 1400 | 1.5149 | 0.6595 | 0.5815 |
86
- | 0.3358 | 4.17 | 1500 | 1.5927 | 0.6627 | 0.5909 |
87
- | 0.3358 | 4.44 | 1600 | 1.5611 | 0.6656 | 0.5775 |
88
- | 0.3358 | 4.72 | 1700 | 1.7512 | 0.6357 | 0.5696 |
89
- | 0.3358 | 5.0 | 1800 | 1.5428 | 0.6668 | 0.5968 |
90
- | 0.3358 | 5.28 | 1900 | 1.6718 | 0.6638 | 0.5925 |
91
- | 0.2166 | 5.56 | 2000 | 1.7788 | 0.6384 | 0.5716 |
92
- | 0.2166 | 5.83 | 2100 | 1.6970 | 0.6578 | 0.5847 |
93
- | 0.2166 | 6.11 | 2200 | 1.7591 | 0.6460 | 0.5891 |
94
- | 0.2166 | 6.39 | 2300 | 1.7743 | 0.6576 | 0.5895 |
95
- | 0.2166 | 6.67 | 2400 | 1.9936 | 0.6358 | 0.5722 |
96
- | 0.1521 | 6.94 | 2500 | 1.9608 | 0.6341 | 0.5720 |
97
- | 0.1521 | 7.22 | 2600 | 1.8215 | 0.6567 | 0.5845 |
98
- | 0.1521 | 7.5 | 2700 | 2.2601 | 0.6184 | 0.5620 |
99
- | 0.1521 | 7.78 | 2800 | 2.0000 | 0.6492 | 0.5844 |
100
- | 0.1521 | 8.06 | 2900 | 1.8825 | 0.6689 | 0.5884 |
101
- | 0.0972 | 8.33 | 3000 | 1.9969 | 0.6499 | 0.5754 |
102
- | 0.0972 | 8.61 | 3100 | 2.0284 | 0.6475 | 0.5888 |
103
- | 0.0972 | 8.89 | 3200 | 2.0733 | 0.6445 | 0.5778 |
104
- | 0.0972 | 9.17 | 3300 | 2.1821 | 0.6401 | 0.5766 |
105
- | 0.0972 | 9.44 | 3400 | 2.1044 | 0.6540 | 0.5882 |
106
- | 0.0821 | 9.72 | 3500 | 2.2485 | 0.6388 | 0.5783 |
107
- | 0.0821 | 10.0 | 3600 | 2.1973 | 0.6474 | 0.5805 |
108
- | 0.0821 | 10.28 | 3700 | 2.2481 | 0.6441 | 0.5746 |
109
- | 0.0821 | 10.56 | 3800 | 2.3463 | 0.6307 | 0.5712 |
110
- | 0.0821 | 10.83 | 3900 | 2.1873 | 0.6514 | 0.5838 |
111
- | 0.0599 | 11.11 | 4000 | 2.2346 | 0.6465 | 0.5769 |
112
- | 0.0599 | 11.39 | 4100 | 2.1812 | 0.6539 | 0.5863 |
113
- | 0.0599 | 11.67 | 4200 | 2.2318 | 0.6528 | 0.5897 |
114
- | 0.0599 | 11.94 | 4300 | 2.2913 | 0.6413 | 0.5821 |
115
- | 0.0599 | 12.22 | 4400 | 2.1780 | 0.6571 | 0.5899 |
116
- | 0.0465 | 12.5 | 4500 | 2.2604 | 0.6611 | 0.5965 |
117
- | 0.0465 | 12.78 | 4600 | 2.1850 | 0.6650 | 0.5997 |
118
- | 0.0465 | 13.06 | 4700 | 2.2568 | 0.6617 | 0.5962 |
119
- | 0.0465 | 13.33 | 4800 | 2.2311 | 0.6648 | 0.5906 |
120
- | 0.0465 | 13.61 | 4900 | 2.3589 | 0.6568 | 0.5949 |
121
- | 0.0269 | 13.89 | 5000 | 2.5143 | 0.6506 | 0.5905 |
122
- | 0.0269 | 14.17 | 5100 | 2.5963 | 0.6421 | 0.5841 |
123
- | 0.0269 | 14.44 | 5200 | 2.3170 | 0.6703 | 0.6001 |
124
- | 0.0269 | 14.72 | 5300 | 2.3151 | 0.6662 | 0.5984 |
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- | 0.0269 | 15.0 | 5400 | 2.7048 | 0.6390 | 0.5751 |
126
- | 0.0228 | 15.28 | 5500 | 2.3686 | 0.6626 | 0.5990 |
127
- | 0.0228 | 15.56 | 5600 | 2.5169 | 0.6536 | 0.5968 |
128
- | 0.0228 | 15.83 | 5700 | 2.5162 | 0.6500 | 0.5911 |
129
- | 0.0228 | 16.11 | 5800 | 2.5161 | 0.6531 | 0.5955 |
130
- | 0.0228 | 16.39 | 5900 | 2.6153 | 0.6473 | 0.5926 |
131
- | 0.0183 | 16.67 | 6000 | 2.5704 | 0.6455 | 0.5847 |
132
- | 0.0183 | 16.94 | 6100 | 2.8607 | 0.6329 | 0.5718 |
133
- | 0.0183 | 17.22 | 6200 | 2.6057 | 0.6440 | 0.5871 |
134
- | 0.0183 | 17.5 | 6300 | 2.5630 | 0.6575 | 0.5966 |
135
- | 0.0183 | 17.78 | 6400 | 2.6760 | 0.6554 | 0.5934 |
136
- | 0.0127 | 18.06 | 6500 | 2.7133 | 0.6532 | 0.5947 |
137
- | 0.0127 | 18.33 | 6600 | 2.7012 | 0.6522 | 0.5934 |
138
- | 0.0127 | 18.61 | 6700 | 2.6611 | 0.6513 | 0.5855 |
139
- | 0.0127 | 18.89 | 6800 | 2.6626 | 0.6484 | 0.5852 |
140
- | 0.0127 | 19.17 | 6900 | 2.7077 | 0.6482 | 0.5878 |
141
- | 0.0127 | 19.44 | 7000 | 2.6134 | 0.6614 | 0.5913 |
142
- | 0.0127 | 19.72 | 7100 | 2.6991 | 0.6563 | 0.5903 |
143
- | 0.0127 | 20.0 | 7200 | 2.7596 | 0.6500 | 0.5818 |
144
- | 0.0127 | 20.28 | 7300 | 2.6609 | 0.6621 | 0.5922 |
145
- | 0.0127 | 20.56 | 7400 | 2.6349 | 0.6644 | 0.5952 |
146
- | 0.0094 | 20.83 | 7500 | 2.5675 | 0.6701 | 0.5977 |
147
- | 0.0094 | 21.11 | 7600 | 2.6176 | 0.6687 | 0.5987 |
148
- | 0.0094 | 21.39 | 7700 | 2.8201 | 0.6551 | 0.5887 |
149
- | 0.0094 | 21.67 | 7800 | 2.7250 | 0.6604 | 0.5922 |
150
- | 0.0094 | 21.94 | 7900 | 2.7049 | 0.6587 | 0.5939 |
151
- | 0.0061 | 22.22 | 8000 | 2.6681 | 0.6596 | 0.5971 |
152
- | 0.0061 | 22.5 | 8100 | 2.6907 | 0.6608 | 0.5932 |
153
- | 0.0061 | 22.78 | 8200 | 2.7454 | 0.6574 | 0.5912 |
154
- | 0.0061 | 23.06 | 8300 | 2.7095 | 0.6597 | 0.5952 |
155
- | 0.0061 | 23.33 | 8400 | 2.6966 | 0.6606 | 0.5958 |
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- | 0.0028 | 23.61 | 8500 | 2.7210 | 0.6637 | 0.5996 |
157
- | 0.0028 | 23.89 | 8600 | 2.6735 | 0.6631 | 0.5949 |
158
- | 0.0028 | 24.17 | 8700 | 2.6844 | 0.6659 | 0.5969 |
159
- | 0.0028 | 24.44 | 8800 | 2.6903 | 0.6616 | 0.5889 |
160
- | 0.0028 | 24.72 | 8900 | 3.0441 | 0.6395 | 0.5798 |
161
- | 0.0048 | 25.0 | 9000 | 2.8181 | 0.6588 | 0.5940 |
162
- | 0.0048 | 25.28 | 9100 | 2.7249 | 0.6673 | 0.5971 |
163
- | 0.0048 | 25.56 | 9200 | 2.7154 | 0.6674 | 0.5962 |
164
- | 0.0048 | 25.83 | 9300 | 2.6837 | 0.6694 | 0.5972 |
165
- | 0.0048 | 26.11 | 9400 | 2.7153 | 0.6669 | 0.5973 |
166
- | 0.0027 | 26.39 | 9500 | 2.7366 | 0.6664 | 0.5987 |
167
- | 0.0027 | 26.67 | 9600 | 2.7943 | 0.6636 | 0.5959 |
168
- | 0.0027 | 26.94 | 9700 | 2.7079 | 0.6706 | 0.6002 |
169
- | 0.0027 | 27.22 | 9800 | 2.7941 | 0.6651 | 0.5993 |
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- | 0.0027 | 27.5 | 9900 | 2.8876 | 0.6575 | 0.5953 |
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- | 0.0024 | 27.78 | 10000 | 2.8470 | 0.6603 | 0.5958 |
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- | 0.0024 | 28.06 | 10100 | 2.8501 | 0.6606 | 0.5955 |
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- | 0.0024 | 28.33 | 10200 | 2.8663 | 0.6606 | 0.5953 |
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- | 0.0024 | 28.61 | 10300 | 2.8620 | 0.6597 | 0.5948 |
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- | 0.0024 | 28.89 | 10400 | 2.8211 | 0.6629 | 0.5977 |
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- | 0.0019 | 29.17 | 10500 | 2.7943 | 0.6653 | 0.5999 |
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- | 0.0019 | 29.44 | 10600 | 2.7875 | 0.6658 | 0.6000 |
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- | 0.0019 | 29.72 | 10700 | 2.7908 | 0.6657 | 0.6003 |
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- | 0.0019 | 30.0 | 10800 | 2.7850 | 0.6663 | 0.6007 |
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  ### Framework versions
 
1
  ---
2
  license: mit
3
+ base_model: facebook/xlm-v-base
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  tags:
5
  - generated_from_trainer
6
  datasets:
 
23
  metrics:
24
  - name: Accuracy
25
  type: accuracy
26
+ value: 0.7100778333960244
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  - name: F1
28
  type: f1
29
+ value: 0.6550778448597152
30
  ---
31
 
32
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
34
 
35
  # scenario-TCR_data-en-massive_all_1_1
36
 
37
+ This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the massive dataset.
38
  It achieves the following results on the evaluation set:
39
+ - Loss: 2.3802
40
+ - Accuracy: 0.7101
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+ - F1: 0.6551
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43
  ## Model description
44
 
 
69
 
70
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
71
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
72
+ | No log | 0.28 | 100 | 3.6542 | 0.0800 | 0.0085 |
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+ | No log | 0.56 | 200 | 2.9766 | 0.3048 | 0.0953 |
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+ | No log | 0.83 | 300 | 2.4835 | 0.3498 | 0.1168 |
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+ | No log | 1.11 | 400 | 2.1305 | 0.4616 | 0.2154 |
76
+ | 2.7657 | 1.39 | 500 | 1.8889 | 0.5374 | 0.2791 |
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+ | 2.7657 | 1.67 | 600 | 1.7326 | 0.5726 | 0.3208 |
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+ | 2.7657 | 1.94 | 700 | 1.6536 | 0.5870 | 0.3726 |
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+ | 2.7657 | 2.22 | 800 | 1.6709 | 0.5987 | 0.4014 |
80
+ | 2.7657 | 2.5 | 900 | 1.5460 | 0.6337 | 0.4720 |
81
+ | 1.1591 | 2.78 | 1000 | 1.5165 | 0.6434 | 0.4904 |
82
+ | 1.1591 | 3.06 | 1100 | 1.3861 | 0.6736 | 0.5237 |
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+ | 1.1591 | 3.33 | 1200 | 1.3776 | 0.6739 | 0.5320 |
84
+ | 1.1591 | 3.61 | 1300 | 1.3753 | 0.6734 | 0.5521 |
85
+ | 1.1591 | 3.89 | 1400 | 1.4680 | 0.6624 | 0.5368 |
86
+ | 0.6194 | 4.17 | 1500 | 1.3899 | 0.6795 | 0.5520 |
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+ | 0.6194 | 4.44 | 1600 | 1.5509 | 0.6640 | 0.5482 |
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+ | 0.6194 | 4.72 | 1700 | 1.4034 | 0.6837 | 0.5764 |
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+ | 0.6194 | 5.0 | 1800 | 1.4750 | 0.6739 | 0.5814 |
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+ | 0.6194 | 5.28 | 1900 | 1.5321 | 0.6697 | 0.5761 |
91
+ | 0.3858 | 5.56 | 2000 | 1.5022 | 0.6822 | 0.5912 |
92
+ | 0.3858 | 5.83 | 2100 | 1.4612 | 0.6865 | 0.6016 |
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+ | 0.3858 | 6.11 | 2200 | 1.4079 | 0.7034 | 0.6204 |
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+ | 0.3858 | 6.39 | 2300 | 1.5165 | 0.6922 | 0.6296 |
95
+ | 0.3858 | 6.67 | 2400 | 1.6168 | 0.6736 | 0.6157 |
96
+ | 0.259 | 6.94 | 2500 | 1.5425 | 0.6948 | 0.6261 |
97
+ | 0.259 | 7.22 | 2600 | 1.6145 | 0.6796 | 0.6035 |
98
+ | 0.259 | 7.5 | 2700 | 1.5916 | 0.6824 | 0.6175 |
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+ | 0.259 | 7.78 | 2800 | 1.5966 | 0.6977 | 0.6306 |
100
+ | 0.259 | 8.06 | 2900 | 1.4939 | 0.7125 | 0.6274 |
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+ | 0.1759 | 8.33 | 3000 | 1.8425 | 0.6714 | 0.6170 |
102
+ | 0.1759 | 8.61 | 3100 | 1.6688 | 0.6923 | 0.6403 |
103
+ | 0.1759 | 8.89 | 3200 | 1.6218 | 0.6997 | 0.6220 |
104
+ | 0.1759 | 9.17 | 3300 | 1.7825 | 0.6829 | 0.6223 |
105
+ | 0.1759 | 9.44 | 3400 | 1.8706 | 0.6916 | 0.6294 |
106
+ | 0.1162 | 9.72 | 3500 | 1.8082 | 0.6884 | 0.6280 |
107
+ | 0.1162 | 10.0 | 3600 | 1.6708 | 0.7096 | 0.6338 |
108
+ | 0.1162 | 10.28 | 3700 | 1.7170 | 0.7100 | 0.6490 |
109
+ | 0.1162 | 10.56 | 3800 | 1.8575 | 0.6917 | 0.6264 |
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+ | 0.1162 | 10.83 | 3900 | 1.8307 | 0.6959 | 0.6448 |
111
+ | 0.092 | 11.11 | 4000 | 1.9248 | 0.6958 | 0.6359 |
112
+ | 0.092 | 11.39 | 4100 | 1.7551 | 0.7162 | 0.6508 |
113
+ | 0.092 | 11.67 | 4200 | 1.8234 | 0.7072 | 0.6465 |
114
+ | 0.092 | 11.94 | 4300 | 2.1146 | 0.6790 | 0.6285 |
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+ | 0.092 | 12.22 | 4400 | 1.9964 | 0.6909 | 0.6411 |
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+ | 0.0582 | 12.5 | 4500 | 2.0290 | 0.6852 | 0.6313 |
117
+ | 0.0582 | 12.78 | 4600 | 2.0828 | 0.6838 | 0.6355 |
118
+ | 0.0582 | 13.06 | 4700 | 1.9272 | 0.7013 | 0.6312 |
119
+ | 0.0582 | 13.33 | 4800 | 1.9882 | 0.6959 | 0.6334 |
120
+ | 0.0582 | 13.61 | 4900 | 1.9552 | 0.7116 | 0.6511 |
121
+ | 0.0398 | 13.89 | 5000 | 2.0269 | 0.7060 | 0.6451 |
122
+ | 0.0398 | 14.17 | 5100 | 2.1377 | 0.6929 | 0.6414 |
123
+ | 0.0398 | 14.44 | 5200 | 2.1114 | 0.6880 | 0.6373 |
124
+ | 0.0398 | 14.72 | 5300 | 2.1517 | 0.6927 | 0.6438 |
125
+ | 0.0398 | 15.0 | 5400 | 2.2472 | 0.6921 | 0.6499 |
126
+ | 0.0311 | 15.28 | 5500 | 2.1801 | 0.6993 | 0.6557 |
127
+ | 0.0311 | 15.56 | 5600 | 2.1090 | 0.7020 | 0.6458 |
128
+ | 0.0311 | 15.83 | 5700 | 2.0049 | 0.7160 | 0.6590 |
129
+ | 0.0311 | 16.11 | 5800 | 2.2198 | 0.6959 | 0.6460 |
130
+ | 0.0311 | 16.39 | 5900 | 2.1074 | 0.7087 | 0.6519 |
131
+ | 0.0223 | 16.67 | 6000 | 2.0899 | 0.7096 | 0.6563 |
132
+ | 0.0223 | 16.94 | 6100 | 2.1736 | 0.7026 | 0.6546 |
133
+ | 0.0223 | 17.22 | 6200 | 2.1829 | 0.7004 | 0.6496 |
134
+ | 0.0223 | 17.5 | 6300 | 2.2041 | 0.6973 | 0.6450 |
135
+ | 0.0223 | 17.78 | 6400 | 2.1969 | 0.7074 | 0.6566 |
136
+ | 0.0178 | 18.06 | 6500 | 2.4021 | 0.6931 | 0.6515 |
137
+ | 0.0178 | 18.33 | 6600 | 2.2865 | 0.7092 | 0.6619 |
138
+ | 0.0178 | 18.61 | 6700 | 2.3086 | 0.7018 | 0.6504 |
139
+ | 0.0178 | 18.89 | 6800 | 2.2665 | 0.7054 | 0.6535 |
140
+ | 0.0178 | 19.17 | 6900 | 2.2723 | 0.7061 | 0.6525 |
141
+ | 0.0129 | 19.44 | 7000 | 2.2976 | 0.7030 | 0.6483 |
142
+ | 0.0129 | 19.72 | 7100 | 2.3634 | 0.7011 | 0.6514 |
143
+ | 0.0129 | 20.0 | 7200 | 2.3313 | 0.6971 | 0.6464 |
144
+ | 0.0129 | 20.28 | 7300 | 2.4373 | 0.6907 | 0.6439 |
145
+ | 0.0129 | 20.56 | 7400 | 2.2424 | 0.7139 | 0.6588 |
146
+ | 0.0125 | 20.83 | 7500 | 2.2329 | 0.7098 | 0.6547 |
147
+ | 0.0125 | 21.11 | 7600 | 2.2365 | 0.7107 | 0.6607 |
148
+ | 0.0125 | 21.39 | 7700 | 2.2925 | 0.7096 | 0.6593 |
149
+ | 0.0125 | 21.67 | 7800 | 2.3717 | 0.6998 | 0.6486 |
150
+ | 0.0125 | 21.94 | 7900 | 2.4211 | 0.6951 | 0.6479 |
151
+ | 0.0104 | 22.22 | 8000 | 2.3714 | 0.6978 | 0.6434 |
152
+ | 0.0104 | 22.5 | 8100 | 2.3995 | 0.7004 | 0.6503 |
153
+ | 0.0104 | 22.78 | 8200 | 2.3877 | 0.7044 | 0.6521 |
154
+ | 0.0104 | 23.06 | 8300 | 2.4957 | 0.6972 | 0.6482 |
155
+ | 0.0104 | 23.33 | 8400 | 2.2553 | 0.7180 | 0.6591 |
156
+ | 0.0061 | 23.61 | 8500 | 2.3877 | 0.7068 | 0.6560 |
157
+ | 0.0061 | 23.89 | 8600 | 2.4298 | 0.7036 | 0.6557 |
158
+ | 0.0061 | 24.17 | 8700 | 2.3903 | 0.7055 | 0.6516 |
159
+ | 0.0061 | 24.44 | 8800 | 2.3298 | 0.7065 | 0.6493 |
160
+ | 0.0061 | 24.72 | 8900 | 2.3245 | 0.7110 | 0.6535 |
161
+ | 0.0054 | 25.0 | 9000 | 2.3287 | 0.7086 | 0.6494 |
162
+ | 0.0054 | 25.28 | 9100 | 2.4519 | 0.6989 | 0.6427 |
163
+ | 0.0054 | 25.56 | 9200 | 2.4671 | 0.6988 | 0.6421 |
164
+ | 0.0054 | 25.83 | 9300 | 2.5166 | 0.6955 | 0.6447 |
165
+ | 0.0054 | 26.11 | 9400 | 2.4190 | 0.7056 | 0.6500 |
166
+ | 0.0029 | 26.39 | 9500 | 2.4361 | 0.7049 | 0.6511 |
167
+ | 0.0029 | 26.67 | 9600 | 2.4765 | 0.7029 | 0.6496 |
168
+ | 0.0029 | 26.94 | 9700 | 2.5246 | 0.6988 | 0.6460 |
169
+ | 0.0029 | 27.22 | 9800 | 2.4363 | 0.7051 | 0.6491 |
170
+ | 0.0029 | 27.5 | 9900 | 2.4066 | 0.7075 | 0.6514 |
171
+ | 0.0025 | 27.78 | 10000 | 2.3870 | 0.7092 | 0.6556 |
172
+ | 0.0025 | 28.06 | 10100 | 2.4028 | 0.7081 | 0.6539 |
173
+ | 0.0025 | 28.33 | 10200 | 2.3983 | 0.7080 | 0.6537 |
174
+ | 0.0025 | 28.61 | 10300 | 2.3876 | 0.7088 | 0.6552 |
175
+ | 0.0025 | 28.89 | 10400 | 2.4032 | 0.7080 | 0.6542 |
176
+ | 0.0025 | 29.17 | 10500 | 2.4138 | 0.7081 | 0.6544 |
177
+ | 0.0025 | 29.44 | 10600 | 2.3880 | 0.7098 | 0.6555 |
178
+ | 0.0025 | 29.72 | 10700 | 2.3801 | 0.7100 | 0.6552 |
179
+ | 0.0025 | 30.0 | 10800 | 2.3802 | 0.7101 | 0.6551 |
180
 
181
 
182
  ### Framework versions
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@@ -1,5 +1,5 @@
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@@ -141,7 +141,6 @@
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- "output_past": true,
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  "problem_type": "single_label_classification",
@@ -149,5 +148,5 @@
149
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150
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151
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141
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  "position_embedding_type": "absolute",
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  "problem_type": "single_label_classification",
 
148
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151
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