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arabert_cross_vocabulary_task1_fold1

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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_vocabulary_task1_fold1
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_vocabulary_task1_fold1
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: 0.8684
18
- - Qwk: 0.0761
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- - Mse: 0.8559
20
 
21
  ## Model description
22
 
@@ -36,48 +36,92 @@ More information needed
36
 
37
  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
41
  - seed: 42
42
  - 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
45
 
46
  ### Training results
47
 
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- | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse |
49
- |:-------------:|:------:|:----:|:---------------:|:-------:|:------:|
50
- | No log | 0.0323 | 2 | 3.6877 | 0.0063 | 3.7124 |
51
- | No log | 0.0645 | 4 | 1.1155 | -0.0070 | 1.1203 |
52
- | No log | 0.0968 | 6 | 0.6955 | -0.0332 | 0.6928 |
53
- | No log | 0.1290 | 8 | 0.5695 | 0.0031 | 0.5642 |
54
- | No log | 0.1613 | 10 | 0.5455 | 0.0857 | 0.5422 |
55
- | No log | 0.1935 | 12 | 0.5525 | 0.0754 | 0.5480 |
56
- | No log | 0.2258 | 14 | 0.6246 | 0.0259 | 0.6189 |
57
- | No log | 0.2581 | 16 | 0.7583 | 0.0025 | 0.7531 |
58
- | No log | 0.2903 | 18 | 0.7013 | -0.0225 | 0.6949 |
59
- | No log | 0.3226 | 20 | 0.6437 | -0.0653 | 0.6352 |
60
- | No log | 0.3548 | 22 | 0.7347 | 0.0 | 0.7259 |
61
- | No log | 0.3871 | 24 | 0.7826 | 0.0 | 0.7744 |
62
- | No log | 0.4194 | 26 | 0.7467 | 0.0 | 0.7394 |
63
- | No log | 0.4516 | 28 | 0.7010 | 0.0202 | 0.6941 |
64
- | No log | 0.4839 | 30 | 0.7123 | 0.0202 | 0.7047 |
65
- | No log | 0.5161 | 32 | 0.8848 | -0.0091 | 0.8746 |
66
- | No log | 0.5484 | 34 | 1.0222 | 0.0893 | 1.0101 |
67
- | No log | 0.5806 | 36 | 0.9830 | 0.0479 | 0.9708 |
68
- | No log | 0.6129 | 38 | 0.8572 | 0.0761 | 0.8457 |
69
- | No log | 0.6452 | 40 | 0.7135 | -0.0100 | 0.7039 |
70
- | No log | 0.6774 | 42 | 0.5802 | -0.0370 | 0.5737 |
71
- | No log | 0.7097 | 44 | 0.5562 | 0.0091 | 0.5515 |
72
- | No log | 0.7419 | 46 | 0.5554 | 0.0394 | 0.5513 |
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- | No log | 0.7742 | 48 | 0.5528 | 0.0182 | 0.5481 |
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- | No log | 0.8065 | 50 | 0.5621 | 0.0036 | 0.5560 |
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- | No log | 0.8387 | 52 | 0.6051 | 0.0 | 0.5970 |
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- | No log | 0.8710 | 54 | 0.6890 | -0.0100 | 0.6790 |
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- | No log | 0.9032 | 56 | 0.7681 | 0.0151 | 0.7569 |
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- | No log | 0.9355 | 58 | 0.8212 | 0.0051 | 0.8094 |
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- | No log | 0.9677 | 60 | 0.8557 | 0.0761 | 0.8434 |
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- | No log | 1.0 | 62 | 0.8684 | 0.0761 | 0.8559 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
81
 
82
 
83
  ### Framework versions
 
3
  tags:
4
  - generated_from_trainer
5
  model-index:
6
+ - name: arabert_cross_vocabulary_task1_fold0
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_vocabulary_task1_fold0
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: 0.9563
18
+ - Qwk: 0.3128
19
+ - Mse: 0.9563
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 | 4.7632 | 0.0048 | 4.7632 |
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+ | No log | 0.2667 | 4 | 2.1783 | 0.0291 | 2.1783 |
52
+ | No log | 0.4 | 6 | 1.1081 | 0.1198 | 1.1081 |
53
+ | No log | 0.5333 | 8 | 1.2015 | 0.1712 | 1.2015 |
54
+ | No log | 0.6667 | 10 | 1.9526 | 0.1246 | 1.9526 |
55
+ | No log | 0.8 | 12 | 1.0676 | 0.2290 | 1.0676 |
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+ | No log | 0.9333 | 14 | 0.5840 | 0.4151 | 0.5840 |
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+ | No log | 1.0667 | 16 | 0.5753 | 0.4082 | 0.5753 |
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+ | No log | 1.2 | 18 | 0.8160 | 0.2285 | 0.8160 |
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+ | No log | 1.3333 | 20 | 1.0681 | 0.2071 | 1.0681 |
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+ | No log | 1.4667 | 22 | 0.9546 | 0.2910 | 0.9546 |
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+ | No log | 1.6 | 24 | 0.8621 | 0.3677 | 0.8621 |
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+ | No log | 1.7333 | 26 | 0.9054 | 0.3700 | 0.9054 |
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+ | No log | 1.8667 | 28 | 0.9001 | 0.3161 | 0.9001 |
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+ | No log | 2.0 | 30 | 0.7765 | 0.3323 | 0.7765 |
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+ | No log | 2.1333 | 32 | 0.8465 | 0.2482 | 0.8465 |
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+ | No log | 2.2667 | 34 | 0.7701 | 0.2998 | 0.7701 |
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+ | No log | 2.4 | 36 | 0.6622 | 0.3947 | 0.6622 |
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+ | No log | 2.5333 | 38 | 0.8242 | 0.3282 | 0.8242 |
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+ | No log | 2.6667 | 40 | 1.1963 | 0.2592 | 1.1963 |
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+ | No log | 2.8 | 42 | 1.0937 | 0.2903 | 1.0937 |
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+ | No log | 2.9333 | 44 | 0.7877 | 0.3934 | 0.7877 |
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+ | No log | 3.0667 | 46 | 0.5972 | 0.4479 | 0.5972 |
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+ | No log | 3.2 | 48 | 0.6227 | 0.4269 | 0.6227 |
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+ | No log | 3.3333 | 50 | 0.8396 | 0.3376 | 0.8396 |
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+ | No log | 3.4667 | 52 | 1.0463 | 0.2697 | 1.0463 |
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+ | No log | 3.6 | 54 | 0.8738 | 0.3001 | 0.8738 |
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+ | No log | 3.7333 | 56 | 0.6019 | 0.4289 | 0.6019 |
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+ | No log | 3.8667 | 58 | 0.5198 | 0.4722 | 0.5198 |
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+ | No log | 4.0 | 60 | 0.5541 | 0.4554 | 0.5541 |
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+ | No log | 4.1333 | 62 | 0.7597 | 0.3733 | 0.7597 |
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+ | No log | 4.2667 | 64 | 0.9356 | 0.3432 | 0.9356 |
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+ | No log | 4.4 | 66 | 0.8464 | 0.3610 | 0.8464 |
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+ | No log | 4.5333 | 68 | 0.7096 | 0.3687 | 0.7096 |
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+ | No log | 4.6667 | 70 | 0.7102 | 0.3574 | 0.7102 |
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+ | No log | 4.8 | 72 | 0.7078 | 0.3669 | 0.7078 |
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+ | No log | 4.9333 | 74 | 0.8143 | 0.3480 | 0.8143 |
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+ | No log | 5.0667 | 76 | 0.9307 | 0.3211 | 0.9307 |
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+ | No log | 5.2 | 78 | 0.9263 | 0.3242 | 0.9263 |
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+ | No log | 5.3333 | 80 | 0.7661 | 0.3610 | 0.7661 |
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+ | No log | 5.4667 | 82 | 0.6978 | 0.3853 | 0.6978 |
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+ | No log | 5.6 | 84 | 0.8151 | 0.3739 | 0.8151 |
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+ | No log | 5.7333 | 86 | 0.8609 | 0.3869 | 0.8609 |
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+ | No log | 5.8667 | 88 | 0.7966 | 0.3804 | 0.7966 |
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+ | No log | 6.0 | 90 | 0.7527 | 0.3801 | 0.7527 |
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+ | No log | 6.1333 | 92 | 0.7607 | 0.3861 | 0.7607 |
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+ | No log | 6.2667 | 94 | 0.8652 | 0.3306 | 0.8652 |
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+ | No log | 6.4 | 96 | 0.9460 | 0.3135 | 0.9460 |
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+ | No log | 6.5333 | 98 | 1.0831 | 0.2779 | 1.0831 |
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+ | No log | 6.6667 | 100 | 1.0697 | 0.2892 | 1.0697 |
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+ | No log | 6.8 | 102 | 0.9442 | 0.3343 | 0.9442 |
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+ | No log | 6.9333 | 104 | 1.0589 | 0.2994 | 1.0589 |
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+ | No log | 7.0667 | 106 | 1.1776 | 0.2674 | 1.1776 |
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+ | No log | 7.2 | 108 | 1.1644 | 0.2696 | 1.1644 |
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+ | No log | 7.3333 | 110 | 0.9516 | 0.3314 | 0.9516 |
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+ | No log | 7.4667 | 112 | 0.8591 | 0.3636 | 0.8591 |
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+ | No log | 7.6 | 114 | 0.9364 | 0.3335 | 0.9364 |
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+ | No log | 7.7333 | 116 | 0.9971 | 0.3111 | 0.9971 |
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+ | No log | 7.8667 | 118 | 0.9728 | 0.3155 | 0.9728 |
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+ | No log | 8.0 | 120 | 0.8721 | 0.3499 | 0.8721 |
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+ | No log | 8.1333 | 122 | 0.8160 | 0.3628 | 0.8160 |
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+ | No log | 8.2667 | 124 | 0.8194 | 0.3688 | 0.8194 |
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+ | No log | 8.4 | 126 | 0.8317 | 0.3748 | 0.8317 |
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+ | No log | 8.5333 | 128 | 0.8909 | 0.3437 | 0.8909 |
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+ | No log | 8.6667 | 130 | 0.9882 | 0.3225 | 0.9882 |
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+ | No log | 8.8 | 132 | 1.0839 | 0.2850 | 1.0839 |
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+ | No log | 8.9333 | 134 | 1.1055 | 0.2859 | 1.1055 |
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+ | No log | 9.0667 | 136 | 1.1389 | 0.2859 | 1.1389 |
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+ | No log | 9.2 | 138 | 1.1417 | 0.2795 | 1.1417 |
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+ | No log | 9.3333 | 140 | 1.0801 | 0.2923 | 1.0801 |
120
+ | No log | 9.4667 | 142 | 1.0176 | 0.3061 | 1.0176 |
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+ | No log | 9.6 | 144 | 0.9751 | 0.3091 | 0.9751 |
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+ | No log | 9.7333 | 146 | 0.9550 | 0.3128 | 0.9550 |
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+ | No log | 9.8667 | 148 | 0.9569 | 0.3128 | 0.9569 |
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+ | No log | 10.0 | 150 | 0.9563 | 0.3128 | 0.9563 |
125
 
126
 
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  ### Framework versions
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