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arabert_cross_relevance_task5_fold6

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README.md CHANGED
@@ -3,20 +3,20 @@ base_model: aubmindlab/bert-base-arabertv02
3
  tags:
4
  - generated_from_trainer
5
  model-index:
6
- - name: arabert_cross_relevance_task5_fold6
7
  results: []
8
  ---
9
 
10
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
11
  should probably proofread and complete it, then remove this comment. -->
12
 
13
- # arabert_cross_relevance_task5_fold6
14
 
15
  This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
16
  It achieves the following results on the evaluation set:
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- - Loss: 0.2705
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- - Qwk: 0.2603
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- - Mse: 0.2706
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  ## Model description
22
 
@@ -36,47 +36,92 @@ More information needed
36
 
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 1
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  ### Training results
47
 
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  | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse |
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  |:-------------:|:------:|:----:|:---------------:|:------:|:------:|
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- | No log | 0.0328 | 2 | 0.3383 | 0.0864 | 0.3385 |
51
- | No log | 0.0656 | 4 | 0.3773 | 0.1304 | 0.3766 |
52
- | No log | 0.0984 | 6 | 0.3324 | 0.0694 | 0.3308 |
53
- | No log | 0.1311 | 8 | 0.3651 | 0.0942 | 0.3628 |
54
- | No log | 0.1639 | 10 | 0.3821 | 0.1263 | 0.3813 |
55
- | No log | 0.1967 | 12 | 0.3185 | 0.0967 | 0.3188 |
56
- | No log | 0.2295 | 14 | 0.2905 | 0.0703 | 0.2911 |
57
- | No log | 0.2623 | 16 | 0.3264 | 0.1381 | 0.3268 |
58
- | No log | 0.2951 | 18 | 0.3480 | 0.1648 | 0.3482 |
59
- | No log | 0.3279 | 20 | 0.3180 | 0.1381 | 0.3179 |
60
- | No log | 0.3607 | 22 | 0.2907 | 0.0805 | 0.2904 |
61
- | No log | 0.3934 | 24 | 0.2784 | 0.1094 | 0.2782 |
62
- | No log | 0.4262 | 26 | 0.2763 | 0.1269 | 0.2762 |
63
- | No log | 0.4590 | 28 | 0.2755 | 0.1455 | 0.2754 |
64
- | No log | 0.4918 | 30 | 0.2737 | 0.2205 | 0.2737 |
65
- | No log | 0.5246 | 32 | 0.2694 | 0.2104 | 0.2693 |
66
- | No log | 0.5574 | 34 | 0.2712 | 0.2889 | 0.2712 |
67
- | No log | 0.5902 | 36 | 0.2740 | 0.3051 | 0.2740 |
68
- | No log | 0.6230 | 38 | 0.2751 | 0.2701 | 0.2752 |
69
- | No log | 0.6557 | 40 | 0.2787 | 0.2819 | 0.2787 |
70
- | No log | 0.6885 | 42 | 0.2795 | 0.2894 | 0.2795 |
71
- | No log | 0.7213 | 44 | 0.2781 | 0.2856 | 0.2782 |
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- | No log | 0.7541 | 46 | 0.2805 | 0.2927 | 0.2806 |
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- | No log | 0.7869 | 48 | 0.2799 | 0.2927 | 0.2799 |
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- | No log | 0.8197 | 50 | 0.2752 | 0.2855 | 0.2752 |
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- | No log | 0.8525 | 52 | 0.2736 | 0.2642 | 0.2737 |
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- | No log | 0.8852 | 54 | 0.2719 | 0.2642 | 0.2720 |
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- | No log | 0.9180 | 56 | 0.2712 | 0.2642 | 0.2713 |
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- | No log | 0.9508 | 58 | 0.2711 | 0.2642 | 0.2711 |
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- | No log | 0.9836 | 60 | 0.2705 | 0.2603 | 0.2706 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
80
 
81
 
82
  ### Framework versions
 
3
  tags:
4
  - generated_from_trainer
5
  model-index:
6
+ - name: arabert_cross_relevance_task5_fold5
7
  results: []
8
  ---
9
 
10
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
11
  should probably proofread and complete it, then remove this comment. -->
12
 
13
+ # arabert_cross_relevance_task5_fold5
14
 
15
  This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
16
  It achieves the following results on the evaluation set:
17
+ - Loss: 0.1922
18
+ - Qwk: 0.3580
19
+ - Mse: 0.1919
20
 
21
  ## Model description
22
 
 
36
 
37
  The following hyperparameters were used during training:
38
  - learning_rate: 2e-05
39
+ - train_batch_size: 64
40
+ - eval_batch_size: 64
41
  - seed: 42
42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
43
  - lr_scheduler_type: linear
44
+ - num_epochs: 10
45
 
46
  ### Training results
47
 
48
  | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse |
49
  |:-------------:|:------:|:----:|:---------------:|:------:|:------:|
50
+ | No log | 0.1333 | 2 | 0.4644 | 0.1830 | 0.4641 |
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+ | No log | 0.2667 | 4 | 0.3061 | 0.4777 | 0.3055 |
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+ | No log | 0.4 | 6 | 0.3885 | 0.4275 | 0.3875 |
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+ | No log | 0.5333 | 8 | 0.2978 | 0.1841 | 0.2972 |
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+ | No log | 0.6667 | 10 | 0.3244 | 0.2335 | 0.3239 |
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+ | No log | 0.8 | 12 | 0.2777 | 0.2095 | 0.2771 |
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+ | No log | 0.9333 | 14 | 0.2770 | 0.1911 | 0.2763 |
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+ | No log | 1.0667 | 16 | 0.3068 | 0.3049 | 0.3060 |
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+ | No log | 1.2 | 18 | 0.2650 | 0.2682 | 0.2644 |
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+ | No log | 1.3333 | 20 | 0.2449 | 0.2365 | 0.2444 |
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+ | No log | 1.4667 | 22 | 0.2306 | 0.2508 | 0.2302 |
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+ | No log | 1.6 | 24 | 0.2334 | 0.2651 | 0.2331 |
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+ | No log | 1.7333 | 26 | 0.2396 | 0.2770 | 0.2393 |
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+ | No log | 1.8667 | 28 | 0.2278 | 0.2629 | 0.2275 |
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+ | No log | 2.0 | 30 | 0.2272 | 0.2894 | 0.2268 |
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+ | No log | 2.1333 | 32 | 0.2158 | 0.2717 | 0.2154 |
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+ | No log | 2.2667 | 34 | 0.1997 | 0.3126 | 0.1994 |
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+ | No log | 2.4 | 36 | 0.1975 | 0.3434 | 0.1973 |
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+ | No log | 2.5333 | 38 | 0.1903 | 0.3467 | 0.1901 |
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+ | No log | 2.6667 | 40 | 0.1879 | 0.3399 | 0.1876 |
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+ | No log | 2.8 | 42 | 0.2111 | 0.3205 | 0.2107 |
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+ | No log | 2.9333 | 44 | 0.2195 | 0.3003 | 0.2190 |
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+ | No log | 3.0667 | 46 | 0.2169 | 0.3061 | 0.2165 |
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+ | No log | 3.2 | 48 | 0.2026 | 0.3222 | 0.2023 |
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+ | No log | 3.3333 | 50 | 0.1934 | 0.3422 | 0.1932 |
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+ | No log | 3.4667 | 52 | 0.1933 | 0.3499 | 0.1932 |
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+ | No log | 3.6 | 54 | 0.1846 | 0.3467 | 0.1844 |
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+ | No log | 3.7333 | 56 | 0.1877 | 0.3716 | 0.1875 |
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+ | No log | 3.8667 | 58 | 0.1997 | 0.3725 | 0.1994 |
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+ | No log | 4.0 | 60 | 0.2019 | 0.3697 | 0.2015 |
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+ | No log | 4.1333 | 62 | 0.1966 | 0.3432 | 0.1964 |
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+ | No log | 4.2667 | 64 | 0.1874 | 0.3434 | 0.1873 |
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+ | No log | 4.4 | 66 | 0.1840 | 0.3478 | 0.1839 |
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+ | No log | 4.5333 | 68 | 0.1812 | 0.3489 | 0.1811 |
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+ | No log | 4.6667 | 70 | 0.1768 | 0.3640 | 0.1766 |
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+ | No log | 4.8 | 72 | 0.1794 | 0.3697 | 0.1791 |
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+ | No log | 4.9333 | 74 | 0.1855 | 0.3408 | 0.1852 |
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+ | No log | 5.0667 | 76 | 0.1947 | 0.3577 | 0.1944 |
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+ | No log | 5.2 | 78 | 0.1924 | 0.3596 | 0.1922 |
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+ | No log | 5.3333 | 80 | 0.1912 | 0.3500 | 0.1909 |
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+ | No log | 5.4667 | 82 | 0.1911 | 0.3544 | 0.1908 |
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+ | No log | 5.6 | 84 | 0.1909 | 0.3475 | 0.1907 |
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+ | No log | 5.7333 | 86 | 0.1914 | 0.3531 | 0.1912 |
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+ | No log | 5.8667 | 88 | 0.1937 | 0.3477 | 0.1935 |
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+ | No log | 6.0 | 90 | 0.1905 | 0.3498 | 0.1903 |
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+ | No log | 6.1333 | 92 | 0.1863 | 0.3478 | 0.1861 |
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+ | No log | 6.2667 | 94 | 0.1855 | 0.3587 | 0.1852 |
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+ | No log | 6.4 | 96 | 0.1840 | 0.3632 | 0.1837 |
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+ | No log | 6.5333 | 98 | 0.1808 | 0.3812 | 0.1805 |
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+ | No log | 6.6667 | 100 | 0.1789 | 0.3737 | 0.1786 |
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+ | No log | 6.8 | 102 | 0.1813 | 0.3848 | 0.1810 |
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+ | No log | 6.9333 | 104 | 0.1837 | 0.3797 | 0.1834 |
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+ | No log | 7.0667 | 106 | 0.1848 | 0.3579 | 0.1845 |
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+ | No log | 7.2 | 108 | 0.1883 | 0.3484 | 0.1880 |
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+ | No log | 7.3333 | 110 | 0.1935 | 0.3642 | 0.1933 |
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+ | No log | 7.4667 | 112 | 0.1935 | 0.3624 | 0.1932 |
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+ | No log | 7.6 | 114 | 0.1895 | 0.3453 | 0.1892 |
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+ | No log | 7.7333 | 116 | 0.1882 | 0.3475 | 0.1878 |
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+ | No log | 7.8667 | 118 | 0.1885 | 0.3565 | 0.1881 |
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+ | No log | 8.0 | 120 | 0.1890 | 0.3621 | 0.1886 |
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+ | No log | 8.1333 | 122 | 0.1888 | 0.3677 | 0.1884 |
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+ | No log | 8.2667 | 124 | 0.1882 | 0.3724 | 0.1878 |
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+ | No log | 8.4 | 126 | 0.1888 | 0.3733 | 0.1884 |
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+ | No log | 8.5333 | 128 | 0.1883 | 0.3686 | 0.1879 |
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+ | No log | 8.6667 | 130 | 0.1881 | 0.3658 | 0.1877 |
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+ | No log | 8.8 | 132 | 0.1886 | 0.3517 | 0.1883 |
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+ | No log | 8.9333 | 134 | 0.1903 | 0.3624 | 0.1900 |
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+ | No log | 9.0667 | 136 | 0.1928 | 0.3633 | 0.1925 |
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+ | No log | 9.2 | 138 | 0.1945 | 0.3650 | 0.1942 |
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+ | No log | 9.3333 | 140 | 0.1953 | 0.3650 | 0.1950 |
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+ | No log | 9.4667 | 142 | 0.1951 | 0.3650 | 0.1949 |
121
+ | No log | 9.6 | 144 | 0.1943 | 0.3641 | 0.1940 |
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+ | No log | 9.7333 | 146 | 0.1931 | 0.3589 | 0.1928 |
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+ | No log | 9.8667 | 148 | 0.1925 | 0.3580 | 0.1922 |
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+ | No log | 10.0 | 150 | 0.1922 | 0.3580 | 0.1919 |
125
 
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  ### Framework versions
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