Hugging Face's logo

MayBashendy
/
ArabicNewSplits8_usingALLEssays_FineTuningAraBERT_run3_AugV5_k8_task2_organization

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Inference Endpoints
Model card Files Community
MayBashendy commited on
Commit
3da5a34
·
verified ·
1 Parent(s): 2e60745

Training in progress, step 500

Browse files
Files changed (4) hide show
  1. README.md +314 -0
  2. config.json +32 -0
  3. model.safetensors +3 -0
  4. training_args.bin +3 -0
README.md ADDED
@@ -0,0 +1,314 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: transformers
3
+ base_model: aubmindlab/bert-base-arabertv02
4
+ tags:
5
+ - generated_from_trainer
6
+ model-index:
7
+ - name: ArabicNewSplits8_usingALLEssays_FineTuningAraBERT_run3_AugV5_k7_task2_organization
8
+ results: []
9
+ ---
10
+
11
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
+ should probably proofread and complete it, then remove this comment. -->
13
+
14
+ # ArabicNewSplits8_usingALLEssays_FineTuningAraBERT_run3_AugV5_k7_task2_organization
15
+
16
+ This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
17
+ It achieves the following results on the evaluation set:
18
+ - Loss: 0.6328
19
+ - Qwk: 0.5380
20
+ - Mse: 0.6328
21
+ - Rmse: 0.7955
22
+
23
+ ## Model description
24
+
25
+ More information needed
26
+
27
+ ## Intended uses & limitations
28
+
29
+ More information needed
30
+
31
+ ## Training and evaluation data
32
+
33
+ More information needed
34
+
35
+ ## Training procedure
36
+
37
+ ### Training hyperparameters
38
+
39
+ The following hyperparameters were used during training:
40
+ - learning_rate: 2e-05
41
+ - train_batch_size: 8
42
+ - eval_batch_size: 8
43
+ - seed: 42
44
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
45
+ - lr_scheduler_type: linear
46
+ - num_epochs: 100
47
+
48
+ ### Training results
49
+
50
+ | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
51
+ |:-------------:|:-------:|:----:|:---------------:|:-------:|:------:|:------:|
52
+ | No log | 0.0541 | 2 | 4.4177 | -0.0232 | 4.4177 | 2.1018 |
53
+ | No log | 0.1081 | 4 | 2.4446 | 0.0331 | 2.4446 | 1.5635 |
54
+ | No log | 0.1622 | 6 | 1.4120 | -0.0085 | 1.4120 | 1.1883 |
55
+ | No log | 0.2162 | 8 | 1.1233 | -0.0980 | 1.1233 | 1.0599 |
56
+ | No log | 0.2703 | 10 | 0.9070 | 0.0931 | 0.9070 | 0.9524 |
57
+ | No log | 0.3243 | 12 | 0.9856 | 0.0533 | 0.9856 | 0.9928 |
58
+ | No log | 0.3784 | 14 | 1.0822 | -0.0247 | 1.0822 | 1.0403 |
59
+ | No log | 0.4324 | 16 | 0.9235 | 0.0998 | 0.9235 | 0.9610 |
60
+ | No log | 0.4865 | 18 | 1.0599 | 0.0087 | 1.0599 | 1.0295 |
61
+ | No log | 0.5405 | 20 | 0.9621 | 0.0975 | 0.9621 | 0.9808 |
62
+ | No log | 0.5946 | 22 | 0.8111 | 0.2207 | 0.8111 | 0.9006 |
63
+ | No log | 0.6486 | 24 | 0.7660 | 0.2855 | 0.7660 | 0.8752 |
64
+ | No log | 0.7027 | 26 | 0.7455 | 0.2177 | 0.7455 | 0.8634 |
65
+ | No log | 0.7568 | 28 | 0.7294 | 0.2631 | 0.7294 | 0.8541 |
66
+ | No log | 0.8108 | 30 | 0.8128 | 0.3566 | 0.8128 | 0.9015 |
67
+ | No log | 0.8649 | 32 | 1.7383 | 0.1465 | 1.7383 | 1.3185 |
68
+ | No log | 0.9189 | 34 | 1.6950 | 0.1681 | 1.6950 | 1.3019 |
69
+ | No log | 0.9730 | 36 | 1.1035 | 0.3140 | 1.1035 | 1.0505 |
70
+ | No log | 1.0270 | 38 | 1.1002 | 0.2983 | 1.1002 | 1.0489 |
71
+ | No log | 1.0811 | 40 | 0.9777 | 0.3012 | 0.9777 | 0.9888 |
72
+ | No log | 1.1351 | 42 | 1.3158 | 0.1862 | 1.3158 | 1.1471 |
73
+ | No log | 1.1892 | 44 | 1.0061 | 0.2512 | 1.0061 | 1.0030 |
74
+ | No log | 1.2432 | 46 | 0.7876 | 0.2947 | 0.7876 | 0.8875 |
75
+ | No log | 1.2973 | 48 | 0.7621 | 0.3405 | 0.7621 | 0.8730 |
76
+ | No log | 1.3514 | 50 | 0.9254 | 0.2028 | 0.9254 | 0.9620 |
77
+ | No log | 1.4054 | 52 | 1.1212 | 0.1669 | 1.1212 | 1.0589 |
78
+ | No log | 1.4595 | 54 | 1.0159 | 0.1851 | 1.0159 | 1.0079 |
79
+ | No log | 1.5135 | 56 | 0.7467 | 0.3272 | 0.7467 | 0.8641 |
80
+ | No log | 1.5676 | 58 | 0.6675 | 0.3416 | 0.6675 | 0.8170 |
81
+ | No log | 1.6216 | 60 | 0.6904 | 0.3569 | 0.6904 | 0.8309 |
82
+ | No log | 1.6757 | 62 | 0.8232 | 0.2537 | 0.8232 | 0.9073 |
83
+ | No log | 1.7297 | 64 | 0.7470 | 0.3903 | 0.7470 | 0.8643 |
84
+ | No log | 1.7838 | 66 | 0.7627 | 0.3282 | 0.7627 | 0.8734 |
85
+ | No log | 1.8378 | 68 | 0.6798 | 0.3884 | 0.6798 | 0.8245 |
86
+ | No log | 1.8919 | 70 | 0.6340 | 0.3896 | 0.6340 | 0.7962 |
87
+ | No log | 1.9459 | 72 | 0.6520 | 0.4091 | 0.6520 | 0.8075 |
88
+ | No log | 2.0 | 74 | 0.9590 | 0.3812 | 0.9590 | 0.9793 |
89
+ | No log | 2.0541 | 76 | 1.1842 | 0.2864 | 1.1842 | 1.0882 |
90
+ | No log | 2.1081 | 78 | 1.3699 | 0.2527 | 1.3699 | 1.1704 |
91
+ | No log | 2.1622 | 80 | 0.8975 | 0.4352 | 0.8975 | 0.9473 |
92
+ | No log | 2.2162 | 82 | 0.6243 | 0.4225 | 0.6243 | 0.7901 |
93
+ | No log | 2.2703 | 84 | 0.6024 | 0.4259 | 0.6024 | 0.7761 |
94
+ | No log | 2.3243 | 86 | 0.6015 | 0.4085 | 0.6015 | 0.7756 |
95
+ | No log | 2.3784 | 88 | 0.6168 | 0.4409 | 0.6168 | 0.7853 |
96
+ | No log | 2.4324 | 90 | 0.7056 | 0.3746 | 0.7056 | 0.8400 |
97
+ | No log | 2.4865 | 92 | 0.7316 | 0.4007 | 0.7316 | 0.8553 |
98
+ | No log | 2.5405 | 94 | 0.7080 | 0.3807 | 0.7080 | 0.8414 |
99
+ | No log | 2.5946 | 96 | 0.7614 | 0.3293 | 0.7614 | 0.8726 |
100
+ | No log | 2.6486 | 98 | 0.7176 | 0.4097 | 0.7176 | 0.8471 |
101
+ | No log | 2.7027 | 100 | 0.8667 | 0.3397 | 0.8667 | 0.9310 |
102
+ | No log | 2.7568 | 102 | 0.8551 | 0.3063 | 0.8551 | 0.9247 |
103
+ | No log | 2.8108 | 104 | 0.7022 | 0.3765 | 0.7022 | 0.8380 |
104
+ | No log | 2.8649 | 106 | 0.6770 | 0.4229 | 0.6770 | 0.8228 |
105
+ | No log | 2.9189 | 108 | 0.6640 | 0.4162 | 0.6640 | 0.8149 |
106
+ | No log | 2.9730 | 110 | 0.6572 | 0.4381 | 0.6572 | 0.8107 |
107
+ | No log | 3.0270 | 112 | 0.6675 | 0.3930 | 0.6675 | 0.8170 |
108
+ | No log | 3.0811 | 114 | 0.7357 | 0.3524 | 0.7357 | 0.8577 |
109
+ | No log | 3.1351 | 116 | 0.7417 | 0.3976 | 0.7417 | 0.8612 |
110
+ | No log | 3.1892 | 118 | 0.6520 | 0.5025 | 0.6520 | 0.8074 |
111
+ | No log | 3.2432 | 120 | 0.6659 | 0.4973 | 0.6659 | 0.8160 |
112
+ | No log | 3.2973 | 122 | 0.7123 | 0.4879 | 0.7123 | 0.8440 |
113
+ | No log | 3.3514 | 124 | 0.9482 | 0.4865 | 0.9482 | 0.9738 |
114
+ | No log | 3.4054 | 126 | 0.8448 | 0.4929 | 0.8448 | 0.9192 |
115
+ | No log | 3.4595 | 128 | 0.6813 | 0.4347 | 0.6813 | 0.8254 |
116
+ | No log | 3.5135 | 130 | 0.6768 | 0.5334 | 0.6768 | 0.8227 |
117
+ | No log | 3.5676 | 132 | 0.7154 | 0.4608 | 0.7154 | 0.8458 |
118
+ | No log | 3.6216 | 134 | 0.8643 | 0.5087 | 0.8643 | 0.9297 |
119
+ | No log | 3.6757 | 136 | 0.7755 | 0.4483 | 0.7755 | 0.8806 |
120
+ | No log | 3.7297 | 138 | 0.6973 | 0.5056 | 0.6973 | 0.8350 |
121
+ | No log | 3.7838 | 140 | 0.7288 | 0.5044 | 0.7288 | 0.8537 |
122
+ | No log | 3.8378 | 142 | 0.6825 | 0.5082 | 0.6825 | 0.8261 |
123
+ | No log | 3.8919 | 144 | 0.6561 | 0.4011 | 0.6561 | 0.8100 |
124
+ | No log | 3.9459 | 146 | 0.6568 | 0.4594 | 0.6568 | 0.8104 |
125
+ | No log | 4.0 | 148 | 0.6721 | 0.5012 | 0.6721 | 0.8198 |
126
+ | No log | 4.0541 | 150 | 0.6587 | 0.4132 | 0.6587 | 0.8116 |
127
+ | No log | 4.1081 | 152 | 0.8500 | 0.4332 | 0.8500 | 0.9220 |
128
+ | No log | 4.1622 | 154 | 0.7962 | 0.4213 | 0.7962 | 0.8923 |
129
+ | No log | 4.2162 | 156 | 0.6778 | 0.4267 | 0.6778 | 0.8233 |
130
+ | No log | 4.2703 | 158 | 0.6417 | 0.4792 | 0.6417 | 0.8011 |
131
+ | No log | 4.3243 | 160 | 0.7046 | 0.4697 | 0.7046 | 0.8394 |
132
+ | No log | 4.3784 | 162 | 0.8233 | 0.4469 | 0.8233 | 0.9074 |
133
+ | No log | 4.4324 | 164 | 0.9290 | 0.3560 | 0.9290 | 0.9638 |
134
+ | No log | 4.4865 | 166 | 0.7974 | 0.4083 | 0.7974 | 0.8930 |
135
+ | No log | 4.5405 | 168 | 0.6712 | 0.4889 | 0.6712 | 0.8193 |
136
+ | No log | 4.5946 | 170 | 0.5872 | 0.5019 | 0.5872 | 0.7663 |
137
+ | No log | 4.6486 | 172 | 0.5888 | 0.5019 | 0.5888 | 0.7673 |
138
+ | No log | 4.7027 | 174 | 0.6775 | 0.4678 | 0.6775 | 0.8231 |
139
+ | No log | 4.7568 | 176 | 0.7689 | 0.4546 | 0.7689 | 0.8769 |
140
+ | No log | 4.8108 | 178 | 0.6510 | 0.4416 | 0.6510 | 0.8068 |
141
+ | No log | 4.8649 | 180 | 0.6048 | 0.4708 | 0.6048 | 0.7777 |
142
+ | No log | 4.9189 | 182 | 0.5994 | 0.4642 | 0.5994 | 0.7742 |
143
+ | No log | 4.9730 | 184 | 0.6124 | 0.4736 | 0.6124 | 0.7825 |
144
+ | No log | 5.0270 | 186 | 0.6093 | 0.4773 | 0.6093 | 0.7805 |
145
+ | No log | 5.0811 | 188 | 0.6041 | 0.5821 | 0.6041 | 0.7772 |
146
+ | No log | 5.1351 | 190 | 0.6289 | 0.5817 | 0.6289 | 0.7931 |
147
+ | No log | 5.1892 | 192 | 0.6025 | 0.5478 | 0.6025 | 0.7762 |
148
+ | No log | 5.2432 | 194 | 0.6738 | 0.5313 | 0.6738 | 0.8208 |
149
+ | No log | 5.2973 | 196 | 0.7179 | 0.5327 | 0.7179 | 0.8473 |
150
+ | No log | 5.3514 | 198 | 0.6712 | 0.4907 | 0.6712 | 0.8193 |
151
+ | No log | 5.4054 | 200 | 0.6936 | 0.4718 | 0.6936 | 0.8328 |
152
+ | No log | 5.4595 | 202 | 0.6768 | 0.5258 | 0.6768 | 0.8227 |
153
+ | No log | 5.5135 | 204 | 0.6667 | 0.5264 | 0.6667 | 0.8165 |
154
+ | No log | 5.5676 | 206 | 0.6836 | 0.4865 | 0.6836 | 0.8268 |
155
+ | No log | 5.6216 | 208 | 0.6708 | 0.4949 | 0.6708 | 0.8190 |
156
+ | No log | 5.6757 | 210 | 0.6289 | 0.4833 | 0.6289 | 0.7930 |
157
+ | No log | 5.7297 | 212 | 0.5888 | 0.4108 | 0.5888 | 0.7673 |
158
+ | No log | 5.7838 | 214 | 0.6068 | 0.4577 | 0.6068 | 0.7790 |
159
+ | No log | 5.8378 | 216 | 0.6445 | 0.4579 | 0.6445 | 0.8028 |
160
+ | No log | 5.8919 | 218 | 0.6208 | 0.4773 | 0.6208 | 0.7879 |
161
+ | No log | 5.9459 | 220 | 0.6374 | 0.4724 | 0.6374 | 0.7984 |
162
+ | No log | 6.0 | 222 | 0.7722 | 0.4716 | 0.7722 | 0.8788 |
163
+ | No log | 6.0541 | 224 | 0.8547 | 0.4495 | 0.8547 | 0.9245 |
164
+ | No log | 6.1081 | 226 | 0.6979 | 0.4579 | 0.6979 | 0.8354 |
165
+ | No log | 6.1622 | 228 | 0.6321 | 0.4489 | 0.6321 | 0.7950 |
166
+ | No log | 6.2162 | 230 | 0.6284 | 0.4422 | 0.6284 | 0.7927 |
167
+ | No log | 6.2703 | 232 | 0.7027 | 0.4419 | 0.7027 | 0.8383 |
168
+ | No log | 6.3243 | 234 | 0.6647 | 0.4648 | 0.6647 | 0.8153 |
169
+ | No log | 6.3784 | 236 | 0.6027 | 0.4064 | 0.6027 | 0.7764 |
170
+ | No log | 6.4324 | 238 | 0.5976 | 0.4313 | 0.5976 | 0.7731 |
171
+ | No log | 6.4865 | 240 | 0.6191 | 0.4840 | 0.6191 | 0.7868 |
172
+ | No log | 6.5405 | 242 | 0.5940 | 0.4570 | 0.5940 | 0.7707 |
173
+ | No log | 6.5946 | 244 | 0.6113 | 0.4823 | 0.6113 | 0.7819 |
174
+ | No log | 6.6486 | 246 | 0.6531 | 0.4832 | 0.6531 | 0.8081 |
175
+ | No log | 6.7027 | 248 | 0.6567 | 0.5038 | 0.6567 | 0.8104 |
176
+ | No log | 6.7568 | 250 | 0.6102 | 0.4416 | 0.6102 | 0.7811 |
177
+ | No log | 6.8108 | 252 | 0.6454 | 0.5378 | 0.6454 | 0.8034 |
178
+ | No log | 6.8649 | 254 | 0.6559 | 0.5751 | 0.6559 | 0.8099 |
179
+ | No log | 6.9189 | 256 | 0.6110 | 0.4888 | 0.6110 | 0.7817 |
180
+ | No log | 6.9730 | 258 | 0.6110 | 0.5311 | 0.6110 | 0.7817 |
181
+ | No log | 7.0270 | 260 | 0.6072 | 0.5454 | 0.6072 | 0.7792 |
182
+ | No log | 7.0811 | 262 | 0.5951 | 0.5065 | 0.5951 | 0.7715 |
183
+ | No log | 7.1351 | 264 | 0.5926 | 0.4975 | 0.5926 | 0.7698 |
184
+ | No log | 7.1892 | 266 | 0.5959 | 0.5860 | 0.5959 | 0.7719 |
185
+ | No log | 7.2432 | 268 | 0.6844 | 0.5683 | 0.6844 | 0.8273 |
186
+ | No log | 7.2973 | 270 | 0.6879 | 0.5797 | 0.6879 | 0.8294 |
187
+ | No log | 7.3514 | 272 | 0.6444 | 0.5364 | 0.6444 | 0.8028 |
188
+ | No log | 7.4054 | 274 | 0.6360 | 0.5360 | 0.6360 | 0.7975 |
189
+ | No log | 7.4595 | 276 | 0.7052 | 0.5249 | 0.7052 | 0.8398 |
190
+ | No log | 7.5135 | 278 | 0.6852 | 0.5019 | 0.6852 | 0.8277 |
191
+ | No log | 7.5676 | 280 | 0.5779 | 0.5238 | 0.5779 | 0.7602 |
192
+ | No log | 7.6216 | 282 | 0.5701 | 0.5257 | 0.5701 | 0.7551 |
193
+ | No log | 7.6757 | 284 | 0.5823 | 0.5074 | 0.5823 | 0.7631 |
194
+ | No log | 7.7297 | 286 | 0.5542 | 0.5632 | 0.5542 | 0.7444 |
195
+ | No log | 7.7838 | 288 | 0.6014 | 0.4880 | 0.6014 | 0.7755 |
196
+ | No log | 7.8378 | 290 | 0.5924 | 0.5168 | 0.5924 | 0.7697 |
197
+ | No log | 7.8919 | 292 | 0.5761 | 0.5241 | 0.5761 | 0.7590 |
198
+ | No log | 7.9459 | 294 | 0.6026 | 0.5462 | 0.6026 | 0.7763 |
199
+ | No log | 8.0 | 296 | 0.6079 | 0.5301 | 0.6079 | 0.7797 |
200
+ | No log | 8.0541 | 298 | 0.6036 | 0.5684 | 0.6036 | 0.7769 |
201
+ | No log | 8.1081 | 300 | 0.5994 | 0.5185 | 0.5994 | 0.7742 |
202
+ | No log | 8.1622 | 302 | 0.7012 | 0.4614 | 0.7012 | 0.8374 |
203
+ | No log | 8.2162 | 304 | 0.6667 | 0.4708 | 0.6667 | 0.8165 |
204
+ | No log | 8.2703 | 306 | 0.5857 | 0.4959 | 0.5857 | 0.7653 |
205
+ | No log | 8.3243 | 308 | 0.5622 | 0.5414 | 0.5622 | 0.7498 |
206
+ | No log | 8.3784 | 310 | 0.6772 | 0.4375 | 0.6772 | 0.8229 |
207
+ | No log | 8.4324 | 312 | 0.7920 | 0.4722 | 0.7920 | 0.8899 |
208
+ | No log | 8.4865 | 314 | 0.7538 | 0.4899 | 0.7538 | 0.8682 |
209
+ | No log | 8.5405 | 316 | 0.6463 | 0.4549 | 0.6463 | 0.8040 |
210
+ | No log | 8.5946 | 318 | 0.6431 | 0.4882 | 0.6431 | 0.8020 |
211
+ | No log | 8.6486 | 320 | 0.7479 | 0.4346 | 0.7479 | 0.8648 |
212
+ | No log | 8.7027 | 322 | 0.8025 | 0.4328 | 0.8025 | 0.8958 |
213
+ | No log | 8.7568 | 324 | 0.6373 | 0.4891 | 0.6373 | 0.7983 |
214
+ | No log | 8.8108 | 326 | 0.5844 | 0.4287 | 0.5844 | 0.7645 |
215
+ | No log | 8.8649 | 328 | 0.5809 | 0.4851 | 0.5809 | 0.7621 |
216
+ | No log | 8.9189 | 330 | 0.6091 | 0.5101 | 0.6091 | 0.7805 |
217
+ | No log | 8.9730 | 332 | 0.6474 | 0.4956 | 0.6474 | 0.8046 |
218
+ | No log | 9.0270 | 334 | 0.6485 | 0.4990 | 0.6485 | 0.8053 |
219
+ | No log | 9.0811 | 336 | 0.6115 | 0.5344 | 0.6115 | 0.7820 |
220
+ | No log | 9.1351 | 338 | 0.6098 | 0.5444 | 0.6098 | 0.7809 |
221
+ | No log | 9.1892 | 340 | 0.6005 | 0.5189 | 0.6005 | 0.7749 |
222
+ | No log | 9.2432 | 342 | 0.5684 | 0.5507 | 0.5684 | 0.7539 |
223
+ | No log | 9.2973 | 344 | 0.5632 | 0.5497 | 0.5632 | 0.7504 |
224
+ | No log | 9.3514 | 346 | 0.5715 | 0.5518 | 0.5715 | 0.7560 |
225
+ | No log | 9.4054 | 348 | 0.6002 | 0.5606 | 0.6002 | 0.7747 |
226
+ | No log | 9.4595 | 350 | 0.5953 | 0.5606 | 0.5953 | 0.7715 |
227
+ | No log | 9.5135 | 352 | 0.5821 | 0.5166 | 0.5821 | 0.7630 |
228
+ | No log | 9.5676 | 354 | 0.5869 | 0.4926 | 0.5869 | 0.7661 |
229
+ | No log | 9.6216 | 356 | 0.5712 | 0.5371 | 0.5712 | 0.7558 |
230
+ | No log | 9.6757 | 358 | 0.5736 | 0.5339 | 0.5736 | 0.7574 |
231
+ | No log | 9.7297 | 360 | 0.6025 | 0.5079 | 0.6025 | 0.7762 |
232
+ | No log | 9.7838 | 362 | 0.6342 | 0.5095 | 0.6342 | 0.7964 |
233
+ | No log | 9.8378 | 364 | 0.6399 | 0.5244 | 0.6399 | 0.7999 |
234
+ | No log | 9.8919 | 366 | 0.6686 | 0.52 | 0.6686 | 0.8177 |
235
+ | No log | 9.9459 | 368 | 0.6342 | 0.5107 | 0.6342 | 0.7964 |
236
+ | No log | 10.0 | 370 | 0.6417 | 0.5277 | 0.6417 | 0.8011 |
237
+ | No log | 10.0541 | 372 | 0.6358 | 0.5348 | 0.6358 | 0.7974 |
238
+ | No log | 10.1081 | 374 | 0.6602 | 0.5124 | 0.6602 | 0.8126 |
239
+ | No log | 10.1622 | 376 | 0.6560 | 0.5090 | 0.6560 | 0.8099 |
240
+ | No log | 10.2162 | 378 | 0.5962 | 0.5655 | 0.5962 | 0.7721 |
241
+ | No log | 10.2703 | 380 | 0.6176 | 0.5352 | 0.6176 | 0.7859 |
242
+ | No log | 10.3243 | 382 | 0.6060 | 0.4943 | 0.6060 | 0.7784 |
243
+ | No log | 10.3784 | 384 | 0.5946 | 0.5279 | 0.5946 | 0.7711 |
244
+ | No log | 10.4324 | 386 | 0.5943 | 0.5314 | 0.5943 | 0.7709 |
245
+ | No log | 10.4865 | 388 | 0.5885 | 0.5210 | 0.5885 | 0.7671 |
246
+ | No log | 10.5405 | 390 | 0.5979 | 0.5367 | 0.5979 | 0.7732 |
247
+ | No log | 10.5946 | 392 | 0.5968 | 0.5549 | 0.5968 | 0.7725 |
248
+ | No log | 10.6486 | 394 | 0.5978 | 0.5188 | 0.5978 | 0.7732 |
249
+ | No log | 10.7027 | 396 | 0.7272 | 0.4636 | 0.7272 | 0.8528 |
250
+ | No log | 10.7568 | 398 | 0.8091 | 0.4061 | 0.8091 | 0.8995 |
251
+ | No log | 10.8108 | 400 | 0.6564 | 0.5082 | 0.6564 | 0.8102 |
252
+ | No log | 10.8649 | 402 | 0.5671 | 0.5320 | 0.5671 | 0.7531 |
253
+ | No log | 10.9189 | 404 | 0.5551 | 0.5384 | 0.5551 | 0.7450 |
254
+ | No log | 10.9730 | 406 | 0.5612 | 0.5133 | 0.5612 | 0.7492 |
255
+ | No log | 11.0270 | 408 | 0.6126 | 0.4854 | 0.6126 | 0.7827 |
256
+ | No log | 11.0811 | 410 | 0.5895 | 0.5170 | 0.5895 | 0.7678 |
257
+ | No log | 11.1351 | 412 | 0.5591 | 0.4963 | 0.5591 | 0.7477 |
258
+ | No log | 11.1892 | 414 | 0.5565 | 0.5348 | 0.5565 | 0.7460 |
259
+ | No log | 11.2432 | 416 | 0.5508 | 0.5545 | 0.5508 | 0.7422 |
260
+ | No log | 11.2973 | 418 | 0.5464 | 0.4935 | 0.5464 | 0.7392 |
261
+ | No log | 11.3514 | 420 | 0.5421 | 0.5106 | 0.5421 | 0.7363 |
262
+ | No log | 11.4054 | 422 | 0.5603 | 0.5495 | 0.5603 | 0.7485 |
263
+ | No log | 11.4595 | 424 | 0.6168 | 0.5070 | 0.6168 | 0.7853 |
264
+ | No log | 11.5135 | 426 | 0.6191 | 0.5070 | 0.6191 | 0.7868 |
265
+ | No log | 11.5676 | 428 | 0.5635 | 0.5644 | 0.5635 | 0.7507 |
266
+ | No log | 11.6216 | 430 | 0.5850 | 0.5257 | 0.5850 | 0.7649 |
267
+ | No log | 11.6757 | 432 | 0.6209 | 0.5310 | 0.6209 | 0.7880 |
268
+ | No log | 11.7297 | 434 | 0.5834 | 0.5522 | 0.5834 | 0.7638 |
269
+ | No log | 11.7838 | 436 | 0.5786 | 0.5011 | 0.5786 | 0.7607 |
270
+ | No log | 11.8378 | 438 | 0.5735 | 0.4759 | 0.5735 | 0.7573 |
271
+ | No log | 11.8919 | 440 | 0.5780 | 0.4559 | 0.5780 | 0.7603 |
272
+ | No log | 11.9459 | 442 | 0.5837 | 0.4785 | 0.5837 | 0.7640 |
273
+ | No log | 12.0 | 444 | 0.5865 | 0.4646 | 0.5865 | 0.7659 |
274
+ | No log | 12.0541 | 446 | 0.5800 | 0.4622 | 0.5800 | 0.7616 |
275
+ | No log | 12.1081 | 448 | 0.5693 | 0.4217 | 0.5693 | 0.7545 |
276
+ | No log | 12.1622 | 450 | 0.5773 | 0.4455 | 0.5773 | 0.7598 |
277
+ | No log | 12.2162 | 452 | 0.5810 | 0.4739 | 0.5810 | 0.7622 |
278
+ | No log | 12.2703 | 454 | 0.5633 | 0.4375 | 0.5633 | 0.7505 |
279
+ | No log | 12.3243 | 456 | 0.5595 | 0.4620 | 0.5595 | 0.7480 |
280
+ | No log | 12.3784 | 458 | 0.5746 | 0.4899 | 0.5746 | 0.7580 |
281
+ | No log | 12.4324 | 460 | 0.5599 | 0.4749 | 0.5599 | 0.7483 |
282
+ | No log | 12.4865 | 462 | 0.5670 | 0.5158 | 0.5670 | 0.7530 |
283
+ | No log | 12.5405 | 464 | 0.6073 | 0.4813 | 0.6073 | 0.7793 |
284
+ | No log | 12.5946 | 466 | 0.5839 | 0.53 | 0.5839 | 0.7641 |
285
+ | No log | 12.6486 | 468 | 0.5507 | 0.5228 | 0.5507 | 0.7421 |
286
+ | No log | 12.7027 | 470 | 0.5473 | 0.5167 | 0.5473 | 0.7398 |
287
+ | No log | 12.7568 | 472 | 0.5654 | 0.4917 | 0.5654 | 0.7519 |
288
+ | No log | 12.8108 | 474 | 0.6727 | 0.4377 | 0.6727 | 0.8202 |
289
+ | No log | 12.8649 | 476 | 0.7439 | 0.4201 | 0.7439 | 0.8625 |
290
+ | No log | 12.9189 | 478 | 0.8146 | 0.4461 | 0.8146 | 0.9025 |
291
+ | No log | 12.9730 | 480 | 0.7090 | 0.4734 | 0.7090 | 0.8420 |
292
+ | No log | 13.0270 | 482 | 0.5676 | 0.4958 | 0.5676 | 0.7534 |
293
+ | No log | 13.0811 | 484 | 0.5565 | 0.5539 | 0.5565 | 0.7460 |
294
+ | No log | 13.1351 | 486 | 0.5554 | 0.5602 | 0.5554 | 0.7452 |
295
+ | No log | 13.1892 | 488 | 0.5381 | 0.5273 | 0.5381 | 0.7335 |
296
+ | No log | 13.2432 | 490 | 0.5425 | 0.4355 | 0.5425 | 0.7365 |
297
+ | No log | 13.2973 | 492 | 0.5617 | 0.4691 | 0.5617 | 0.7495 |
298
+ | No log | 13.3514 | 494 | 0.5621 | 0.4818 | 0.5621 | 0.7497 |
299
+ | No log | 13.4054 | 496 | 0.5441 | 0.4863 | 0.5441 | 0.7377 |
300
+ | No log | 13.4595 | 498 | 0.5421 | 0.5415 | 0.5421 | 0.7362 |
301
+ | 0.3405 | 13.5135 | 500 | 0.5457 | 0.5421 | 0.5457 | 0.7387 |
302
+ | 0.3405 | 13.5676 | 502 | 0.5920 | 0.5264 | 0.5920 | 0.7694 |
303
+ | 0.3405 | 13.6216 | 504 | 0.7262 | 0.4760 | 0.7262 | 0.8521 |
304
+ | 0.3405 | 13.6757 | 506 | 0.7651 | 0.4501 | 0.7651 | 0.8747 |
305
+ | 0.3405 | 13.7297 | 508 | 0.6930 | 0.4661 | 0.6930 | 0.8324 |
306
+ | 0.3405 | 13.7838 | 510 | 0.6328 | 0.5380 | 0.6328 | 0.7955 |
307
+
308
+
309
+ ### Framework versions
310
+
311
+ - Transformers 4.44.2
312
+ - Pytorch 2.4.0+cu118
313
+ - Datasets 2.21.0
314
+ - Tokenizers 0.19.1
config.json ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "aubmindlab/bert-base-arabertv02",
3
+ "architectures": [
4
+ "BertForSequenceClassification"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "hidden_act": "gelu",
9
+ "hidden_dropout_prob": 0.1,
10
+ "hidden_size": 768,
11
+ "id2label": {
12
+ "0": "LABEL_0"
13
+ },
14
+ "initializer_range": 0.02,
15
+ "intermediate_size": 3072,
16
+ "label2id": {
17
+ "LABEL_0": 0
18
+ },
19
+ "layer_norm_eps": 1e-12,
20
+ "max_position_embeddings": 512,
21
+ "model_type": "bert",
22
+ "num_attention_heads": 12,
23
+ "num_hidden_layers": 12,
24
+ "pad_token_id": 0,
25
+ "position_embedding_type": "absolute",
26
+ "problem_type": "regression",
27
+ "torch_dtype": "float32",
28
+ "transformers_version": "4.44.2",
29
+ "type_vocab_size": 2,
30
+ "use_cache": true,
31
+ "vocab_size": 64000
32
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:af58565af5731c3b70704e2f32498219e4e0fe5a680d284ba6f6109a57d26fcd
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0cf679f27d1b5a0a52b221e2eafb66c0ecfdbfac3b402472af401e55c6bd2ec8
3
+ size 5304