Hugging Face's logo

MayBashendy
/
ArabicNewSplits7_OSS_usingWellWrittenEssays_FineTuningAraBERT_run2_AugV5_k3_task2_organization

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
Community
MayBashendy commited on
Commit
c3754e6
·
verified ·
1 Parent(s): 1e1bf7b

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: ArabicNewSplits7_OSS_usingWellWrittenEssays_FineTuningAraBERT_run2_AugV5_k2_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
+ # ArabicNewSplits7_OSS_usingWellWrittenEssays_FineTuningAraBERT_run2_AugV5_k2_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: 0.7687
19
+ - Qwk: 0.5259
20
+ - Mse: 0.7687
21
+ - Rmse: 0.8768
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 | 0.1667 | 2 | 4.5530 | 0.0018 | 4.5530 | 2.1338 |
53
+ | No log | 0.3333 | 4 | 2.4686 | 0.0672 | 2.4686 | 1.5712 |
54
+ | No log | 0.5 | 6 | 1.7900 | 0.0844 | 1.7900 | 1.3379 |
55
+ | No log | 0.6667 | 8 | 1.1826 | 0.1370 | 1.1826 | 1.0875 |
56
+ | No log | 0.8333 | 10 | 1.1721 | 0.1585 | 1.1721 | 1.0826 |
57
+ | No log | 1.0 | 12 | 1.1640 | 0.2448 | 1.1640 | 1.0789 |
58
+ | No log | 1.1667 | 14 | 1.1071 | 0.2843 | 1.1071 | 1.0522 |
59
+ | No log | 1.3333 | 16 | 1.0810 | 0.2694 | 1.0810 | 1.0397 |
60
+ | No log | 1.5 | 18 | 1.5072 | 0.1603 | 1.5072 | 1.2277 |
61
+ | No log | 1.6667 | 20 | 1.6234 | 0.2243 | 1.6234 | 1.2741 |
62
+ | No log | 1.8333 | 22 | 1.2415 | 0.2304 | 1.2415 | 1.1142 |
63
+ | No log | 2.0 | 24 | 1.1132 | 0.1811 | 1.1132 | 1.0551 |
64
+ | No log | 2.1667 | 26 | 1.1144 | 0.1992 | 1.1144 | 1.0557 |
65
+ | No log | 2.3333 | 28 | 1.1988 | 0.2247 | 1.1988 | 1.0949 |
66
+ | No log | 2.5 | 30 | 1.2442 | 0.2821 | 1.2442 | 1.1154 |
67
+ | No log | 2.6667 | 32 | 1.2466 | 0.4140 | 1.2466 | 1.1165 |
68
+ | No log | 2.8333 | 34 | 1.0866 | 0.4094 | 1.0866 | 1.0424 |
69
+ | No log | 3.0 | 36 | 1.2535 | 0.3734 | 1.2535 | 1.1196 |
70
+ | No log | 3.1667 | 38 | 1.2222 | 0.3912 | 1.2222 | 1.1055 |
71
+ | No log | 3.3333 | 40 | 0.9568 | 0.5200 | 0.9568 | 0.9781 |
72
+ | No log | 3.5 | 42 | 0.9871 | 0.5034 | 0.9871 | 0.9935 |
73
+ | No log | 3.6667 | 44 | 1.4433 | 0.3271 | 1.4433 | 1.2014 |
74
+ | No log | 3.8333 | 46 | 1.1213 | 0.3992 | 1.1213 | 1.0589 |
75
+ | No log | 4.0 | 48 | 0.8256 | 0.5397 | 0.8256 | 0.9086 |
76
+ | No log | 4.1667 | 50 | 0.8937 | 0.5357 | 0.8937 | 0.9453 |
77
+ | No log | 4.3333 | 52 | 0.8723 | 0.5634 | 0.8723 | 0.9340 |
78
+ | No log | 4.5 | 54 | 1.0182 | 0.3763 | 1.0182 | 1.0091 |
79
+ | No log | 4.6667 | 56 | 1.1373 | 0.3449 | 1.1373 | 1.0665 |
80
+ | No log | 4.8333 | 58 | 0.9808 | 0.3714 | 0.9808 | 0.9904 |
81
+ | No log | 5.0 | 60 | 0.8560 | 0.5320 | 0.8560 | 0.9252 |
82
+ | No log | 5.1667 | 62 | 0.8289 | 0.5895 | 0.8289 | 0.9104 |
83
+ | No log | 5.3333 | 64 | 0.8378 | 0.6204 | 0.8378 | 0.9153 |
84
+ | No log | 5.5 | 66 | 0.8303 | 0.6168 | 0.8303 | 0.9112 |
85
+ | No log | 5.6667 | 68 | 0.9138 | 0.5471 | 0.9138 | 0.9559 |
86
+ | No log | 5.8333 | 70 | 0.9608 | 0.5296 | 0.9608 | 0.9802 |
87
+ | No log | 6.0 | 72 | 0.9785 | 0.5471 | 0.9785 | 0.9892 |
88
+ | No log | 6.1667 | 74 | 0.8401 | 0.5794 | 0.8401 | 0.9166 |
89
+ | No log | 6.3333 | 76 | 0.8317 | 0.6001 | 0.8317 | 0.9120 |
90
+ | No log | 6.5 | 78 | 0.8318 | 0.5827 | 0.8318 | 0.9120 |
91
+ | No log | 6.6667 | 80 | 0.8195 | 0.5546 | 0.8195 | 0.9053 |
92
+ | No log | 6.8333 | 82 | 0.9137 | 0.5486 | 0.9137 | 0.9559 |
93
+ | No log | 7.0 | 84 | 0.7836 | 0.5497 | 0.7836 | 0.8852 |
94
+ | No log | 7.1667 | 86 | 0.7776 | 0.6097 | 0.7776 | 0.8818 |
95
+ | No log | 7.3333 | 88 | 0.7555 | 0.6239 | 0.7555 | 0.8692 |
96
+ | No log | 7.5 | 90 | 0.7408 | 0.6239 | 0.7408 | 0.8607 |
97
+ | No log | 7.6667 | 92 | 0.7284 | 0.6239 | 0.7284 | 0.8535 |
98
+ | No log | 7.8333 | 94 | 0.7638 | 0.5825 | 0.7638 | 0.8740 |
99
+ | No log | 8.0 | 96 | 0.7645 | 0.5546 | 0.7645 | 0.8744 |
100
+ | No log | 8.1667 | 98 | 0.7178 | 0.6525 | 0.7178 | 0.8472 |
101
+ | No log | 8.3333 | 100 | 0.7438 | 0.6141 | 0.7438 | 0.8625 |
102
+ | No log | 8.5 | 102 | 0.7696 | 0.6151 | 0.7696 | 0.8773 |
103
+ | No log | 8.6667 | 104 | 0.8044 | 0.5287 | 0.8044 | 0.8969 |
104
+ | No log | 8.8333 | 106 | 0.7386 | 0.6172 | 0.7386 | 0.8594 |
105
+ | No log | 9.0 | 108 | 0.7624 | 0.5766 | 0.7624 | 0.8732 |
106
+ | No log | 9.1667 | 110 | 0.9735 | 0.5777 | 0.9735 | 0.9867 |
107
+ | No log | 9.3333 | 112 | 0.8539 | 0.5860 | 0.8539 | 0.9241 |
108
+ | No log | 9.5 | 114 | 0.7642 | 0.6605 | 0.7642 | 0.8742 |
109
+ | No log | 9.6667 | 116 | 0.9461 | 0.5258 | 0.9461 | 0.9727 |
110
+ | No log | 9.8333 | 118 | 0.8830 | 0.5977 | 0.8830 | 0.9397 |
111
+ | No log | 10.0 | 120 | 0.7944 | 0.6026 | 0.7944 | 0.8913 |
112
+ | No log | 10.1667 | 122 | 0.7684 | 0.6029 | 0.7684 | 0.8766 |
113
+ | No log | 10.3333 | 124 | 0.7961 | 0.5339 | 0.7961 | 0.8922 |
114
+ | No log | 10.5 | 126 | 0.7620 | 0.6177 | 0.7620 | 0.8729 |
115
+ | No log | 10.6667 | 128 | 0.7413 | 0.5102 | 0.7413 | 0.8610 |
116
+ | No log | 10.8333 | 130 | 0.7858 | 0.5248 | 0.7858 | 0.8864 |
117
+ | No log | 11.0 | 132 | 0.7757 | 0.5886 | 0.7757 | 0.8807 |
118
+ | No log | 11.1667 | 134 | 0.7246 | 0.5530 | 0.7246 | 0.8512 |
119
+ | No log | 11.3333 | 136 | 0.7330 | 0.5832 | 0.7330 | 0.8561 |
120
+ | No log | 11.5 | 138 | 0.8812 | 0.5139 | 0.8812 | 0.9387 |
121
+ | No log | 11.6667 | 140 | 0.7552 | 0.5788 | 0.7552 | 0.8690 |
122
+ | No log | 11.8333 | 142 | 0.6629 | 0.5821 | 0.6629 | 0.8142 |
123
+ | No log | 12.0 | 144 | 0.6833 | 0.6029 | 0.6833 | 0.8266 |
124
+ | No log | 12.1667 | 146 | 0.6789 | 0.5961 | 0.6789 | 0.8239 |
125
+ | No log | 12.3333 | 148 | 0.6851 | 0.5582 | 0.6851 | 0.8277 |
126
+ | No log | 12.5 | 150 | 0.7015 | 0.6517 | 0.7015 | 0.8375 |
127
+ | No log | 12.6667 | 152 | 0.6944 | 0.6417 | 0.6944 | 0.8333 |
128
+ | No log | 12.8333 | 154 | 0.8199 | 0.4962 | 0.8199 | 0.9055 |
129
+ | No log | 13.0 | 156 | 0.9404 | 0.4894 | 0.9404 | 0.9698 |
130
+ | No log | 13.1667 | 158 | 0.8200 | 0.4949 | 0.8200 | 0.9055 |
131
+ | No log | 13.3333 | 160 | 0.7067 | 0.6237 | 0.7067 | 0.8406 |
132
+ | No log | 13.5 | 162 | 0.7963 | 0.6101 | 0.7963 | 0.8923 |
133
+ | No log | 13.6667 | 164 | 0.8762 | 0.5297 | 0.8762 | 0.9360 |
134
+ | No log | 13.8333 | 166 | 0.7821 | 0.6049 | 0.7821 | 0.8844 |
135
+ | No log | 14.0 | 168 | 0.7056 | 0.6151 | 0.7056 | 0.8400 |
136
+ | No log | 14.1667 | 170 | 0.7170 | 0.5546 | 0.7170 | 0.8468 |
137
+ | No log | 14.3333 | 172 | 0.7225 | 0.5358 | 0.7225 | 0.8500 |
138
+ | No log | 14.5 | 174 | 0.6911 | 0.6117 | 0.6911 | 0.8313 |
139
+ | No log | 14.6667 | 176 | 0.6990 | 0.6117 | 0.6990 | 0.8361 |
140
+ | No log | 14.8333 | 178 | 0.7018 | 0.6487 | 0.7018 | 0.8377 |
141
+ | No log | 15.0 | 180 | 0.7465 | 0.6109 | 0.7465 | 0.8640 |
142
+ | No log | 15.1667 | 182 | 0.7944 | 0.5736 | 0.7944 | 0.8913 |
143
+ | No log | 15.3333 | 184 | 0.7707 | 0.5833 | 0.7707 | 0.8779 |
144
+ | No log | 15.5 | 186 | 0.7427 | 0.5988 | 0.7427 | 0.8618 |
145
+ | No log | 15.6667 | 188 | 0.7504 | 0.5750 | 0.7504 | 0.8663 |
146
+ | No log | 15.8333 | 190 | 0.7613 | 0.6211 | 0.7613 | 0.8725 |
147
+ | No log | 16.0 | 192 | 0.7926 | 0.6047 | 0.7926 | 0.8903 |
148
+ | No log | 16.1667 | 194 | 0.8033 | 0.6069 | 0.8033 | 0.8963 |
149
+ | No log | 16.3333 | 196 | 0.7559 | 0.6131 | 0.7559 | 0.8694 |
150
+ | No log | 16.5 | 198 | 0.7296 | 0.6051 | 0.7296 | 0.8542 |
151
+ | No log | 16.6667 | 200 | 0.7601 | 0.5712 | 0.7601 | 0.8718 |
152
+ | No log | 16.8333 | 202 | 0.7463 | 0.5327 | 0.7463 | 0.8639 |
153
+ | No log | 17.0 | 204 | 0.7327 | 0.5607 | 0.7327 | 0.8560 |
154
+ | No log | 17.1667 | 206 | 0.7258 | 0.5856 | 0.7258 | 0.8520 |
155
+ | No log | 17.3333 | 208 | 0.7106 | 0.6183 | 0.7106 | 0.8430 |
156
+ | No log | 17.5 | 210 | 0.7071 | 0.5776 | 0.7071 | 0.8409 |
157
+ | No log | 17.6667 | 212 | 0.7517 | 0.5117 | 0.7517 | 0.8670 |
158
+ | No log | 17.8333 | 214 | 0.7589 | 0.5228 | 0.7589 | 0.8712 |
159
+ | No log | 18.0 | 216 | 0.7202 | 0.6139 | 0.7202 | 0.8486 |
160
+ | No log | 18.1667 | 218 | 0.7395 | 0.5862 | 0.7395 | 0.8599 |
161
+ | No log | 18.3333 | 220 | 0.7525 | 0.6196 | 0.7525 | 0.8674 |
162
+ | No log | 18.5 | 222 | 0.7450 | 0.5443 | 0.7450 | 0.8632 |
163
+ | No log | 18.6667 | 224 | 0.7489 | 0.5443 | 0.7489 | 0.8654 |
164
+ | No log | 18.8333 | 226 | 0.7503 | 0.6554 | 0.7503 | 0.8662 |
165
+ | No log | 19.0 | 228 | 0.7678 | 0.6489 | 0.7678 | 0.8763 |
166
+ | No log | 19.1667 | 230 | 0.8037 | 0.6091 | 0.8037 | 0.8965 |
167
+ | No log | 19.3333 | 232 | 0.7613 | 0.6334 | 0.7613 | 0.8725 |
168
+ | No log | 19.5 | 234 | 0.7209 | 0.6404 | 0.7209 | 0.8490 |
169
+ | No log | 19.6667 | 236 | 0.6930 | 0.6380 | 0.6930 | 0.8325 |
170
+ | No log | 19.8333 | 238 | 0.7697 | 0.5919 | 0.7697 | 0.8773 |
171
+ | No log | 20.0 | 240 | 0.7874 | 0.5912 | 0.7874 | 0.8874 |
172
+ | No log | 20.1667 | 242 | 0.7183 | 0.6239 | 0.7183 | 0.8475 |
173
+ | No log | 20.3333 | 244 | 0.7092 | 0.6664 | 0.7092 | 0.8421 |
174
+ | No log | 20.5 | 246 | 0.7052 | 0.6773 | 0.7052 | 0.8398 |
175
+ | No log | 20.6667 | 248 | 0.7073 | 0.6664 | 0.7073 | 0.8410 |
176
+ | No log | 20.8333 | 250 | 0.7265 | 0.6141 | 0.7265 | 0.8524 |
177
+ | No log | 21.0 | 252 | 0.7390 | 0.5513 | 0.7390 | 0.8596 |
178
+ | No log | 21.1667 | 254 | 0.7567 | 0.4736 | 0.7567 | 0.8699 |
179
+ | No log | 21.3333 | 256 | 0.7614 | 0.4736 | 0.7614 | 0.8726 |
180
+ | No log | 21.5 | 258 | 0.7373 | 0.5884 | 0.7373 | 0.8586 |
181
+ | No log | 21.6667 | 260 | 0.7354 | 0.5063 | 0.7354 | 0.8576 |
182
+ | No log | 21.8333 | 262 | 0.8066 | 0.4838 | 0.8066 | 0.8981 |
183
+ | No log | 22.0 | 264 | 0.8051 | 0.5117 | 0.8051 | 0.8972 |
184
+ | No log | 22.1667 | 266 | 0.7209 | 0.5437 | 0.7209 | 0.8490 |
185
+ | No log | 22.3333 | 268 | 0.7394 | 0.6758 | 0.7394 | 0.8599 |
186
+ | No log | 22.5 | 270 | 0.7855 | 0.6382 | 0.7855 | 0.8863 |
187
+ | No log | 22.6667 | 272 | 0.7716 | 0.6417 | 0.7716 | 0.8784 |
188
+ | No log | 22.8333 | 274 | 0.7212 | 0.5937 | 0.7212 | 0.8493 |
189
+ | No log | 23.0 | 276 | 0.7165 | 0.6010 | 0.7165 | 0.8465 |
190
+ | No log | 23.1667 | 278 | 0.7282 | 0.5439 | 0.7282 | 0.8533 |
191
+ | No log | 23.3333 | 280 | 0.7683 | 0.5622 | 0.7683 | 0.8765 |
192
+ | No log | 23.5 | 282 | 0.7652 | 0.5336 | 0.7652 | 0.8747 |
193
+ | No log | 23.6667 | 284 | 0.7557 | 0.5307 | 0.7557 | 0.8693 |
194
+ | No log | 23.8333 | 286 | 0.7943 | 0.5393 | 0.7943 | 0.8912 |
195
+ | No log | 24.0 | 288 | 0.8430 | 0.5352 | 0.8430 | 0.9182 |
196
+ | No log | 24.1667 | 290 | 0.7886 | 0.5477 | 0.7886 | 0.8881 |
197
+ | No log | 24.3333 | 292 | 0.7604 | 0.5645 | 0.7604 | 0.8720 |
198
+ | No log | 24.5 | 294 | 0.8809 | 0.5534 | 0.8809 | 0.9386 |
199
+ | No log | 24.6667 | 296 | 0.9962 | 0.5094 | 0.9962 | 0.9981 |
200
+ | No log | 24.8333 | 298 | 0.9803 | 0.4894 | 0.9803 | 0.9901 |
201
+ | No log | 25.0 | 300 | 0.8844 | 0.4345 | 0.8844 | 0.9404 |
202
+ | No log | 25.1667 | 302 | 0.8108 | 0.4937 | 0.8108 | 0.9004 |
203
+ | No log | 25.3333 | 304 | 0.7632 | 0.5528 | 0.7632 | 0.8736 |
204
+ | No log | 25.5 | 306 | 0.7490 | 0.5895 | 0.7490 | 0.8654 |
205
+ | No log | 25.6667 | 308 | 0.7729 | 0.6001 | 0.7729 | 0.8792 |
206
+ | No log | 25.8333 | 310 | 0.8051 | 0.5934 | 0.8051 | 0.8973 |
207
+ | No log | 26.0 | 312 | 0.7764 | 0.6100 | 0.7764 | 0.8812 |
208
+ | No log | 26.1667 | 314 | 0.7587 | 0.5971 | 0.7587 | 0.8710 |
209
+ | No log | 26.3333 | 316 | 0.7665 | 0.5131 | 0.7665 | 0.8755 |
210
+ | No log | 26.5 | 318 | 0.7895 | 0.5841 | 0.7895 | 0.8886 |
211
+ | No log | 26.6667 | 320 | 0.7959 | 0.6005 | 0.7959 | 0.8921 |
212
+ | No log | 26.8333 | 322 | 0.7504 | 0.6269 | 0.7504 | 0.8663 |
213
+ | No log | 27.0 | 324 | 0.7251 | 0.6461 | 0.7251 | 0.8516 |
214
+ | No log | 27.1667 | 326 | 0.7105 | 0.6461 | 0.7105 | 0.8429 |
215
+ | No log | 27.3333 | 328 | 0.6992 | 0.6393 | 0.6992 | 0.8362 |
216
+ | No log | 27.5 | 330 | 0.6966 | 0.5946 | 0.6966 | 0.8347 |
217
+ | No log | 27.6667 | 332 | 0.6952 | 0.6086 | 0.6952 | 0.8338 |
218
+ | No log | 27.8333 | 334 | 0.6999 | 0.6664 | 0.6999 | 0.8366 |
219
+ | No log | 28.0 | 336 | 0.7080 | 0.6377 | 0.7080 | 0.8414 |
220
+ | No log | 28.1667 | 338 | 0.7058 | 0.6340 | 0.7058 | 0.8401 |
221
+ | No log | 28.3333 | 340 | 0.7045 | 0.6448 | 0.7045 | 0.8394 |
222
+ | No log | 28.5 | 342 | 0.7101 | 0.6806 | 0.7101 | 0.8427 |
223
+ | No log | 28.6667 | 344 | 0.6902 | 0.6526 | 0.6902 | 0.8308 |
224
+ | No log | 28.8333 | 346 | 0.6895 | 0.6657 | 0.6895 | 0.8304 |
225
+ | No log | 29.0 | 348 | 0.6936 | 0.6657 | 0.6936 | 0.8328 |
226
+ | No log | 29.1667 | 350 | 0.6701 | 0.6860 | 0.6701 | 0.8186 |
227
+ | No log | 29.3333 | 352 | 0.7473 | 0.5763 | 0.7473 | 0.8645 |
228
+ | No log | 29.5 | 354 | 0.8170 | 0.5048 | 0.8170 | 0.9039 |
229
+ | No log | 29.6667 | 356 | 0.7749 | 0.5618 | 0.7749 | 0.8803 |
230
+ | No log | 29.8333 | 358 | 0.6829 | 0.6232 | 0.6829 | 0.8264 |
231
+ | No log | 30.0 | 360 | 0.6769 | 0.6902 | 0.6769 | 0.8228 |
232
+ | No log | 30.1667 | 362 | 0.7518 | 0.6446 | 0.7518 | 0.8670 |
233
+ | No log | 30.3333 | 364 | 0.7965 | 0.6140 | 0.7965 | 0.8925 |
234
+ | No log | 30.5 | 366 | 0.7824 | 0.6162 | 0.7824 | 0.8845 |
235
+ | No log | 30.6667 | 368 | 0.7443 | 0.6120 | 0.7443 | 0.8627 |
236
+ | No log | 30.8333 | 370 | 0.7392 | 0.5466 | 0.7392 | 0.8598 |
237
+ | No log | 31.0 | 372 | 0.7538 | 0.5361 | 0.7538 | 0.8682 |
238
+ | No log | 31.1667 | 374 | 0.7470 | 0.5451 | 0.7470 | 0.8643 |
239
+ | No log | 31.3333 | 376 | 0.7462 | 0.6044 | 0.7462 | 0.8638 |
240
+ | No log | 31.5 | 378 | 0.8341 | 0.5658 | 0.8340 | 0.9133 |
241
+ | No log | 31.6667 | 380 | 0.9309 | 0.5750 | 0.9309 | 0.9648 |
242
+ | No log | 31.8333 | 382 | 0.9555 | 0.5649 | 0.9555 | 0.9775 |
243
+ | No log | 32.0 | 384 | 0.9000 | 0.5532 | 0.9000 | 0.9487 |
244
+ | No log | 32.1667 | 386 | 0.8215 | 0.5539 | 0.8215 | 0.9063 |
245
+ | No log | 32.3333 | 388 | 0.7628 | 0.5530 | 0.7628 | 0.8734 |
246
+ | No log | 32.5 | 390 | 0.7672 | 0.6012 | 0.7672 | 0.8759 |
247
+ | No log | 32.6667 | 392 | 0.7882 | 0.5734 | 0.7882 | 0.8878 |
248
+ | No log | 32.8333 | 394 | 0.7611 | 0.6035 | 0.7611 | 0.8724 |
249
+ | No log | 33.0 | 396 | 0.7267 | 0.5299 | 0.7267 | 0.8525 |
250
+ | No log | 33.1667 | 398 | 0.7276 | 0.5483 | 0.7276 | 0.8530 |
251
+ | No log | 33.3333 | 400 | 0.7484 | 0.4948 | 0.7484 | 0.8651 |
252
+ | No log | 33.5 | 402 | 0.7725 | 0.5029 | 0.7725 | 0.8789 |
253
+ | No log | 33.6667 | 404 | 0.7787 | 0.5107 | 0.7787 | 0.8824 |
254
+ | No log | 33.8333 | 406 | 0.7857 | 0.5869 | 0.7857 | 0.8864 |
255
+ | No log | 34.0 | 408 | 0.7719 | 0.5779 | 0.7719 | 0.8786 |
256
+ | No log | 34.1667 | 410 | 0.7398 | 0.6230 | 0.7398 | 0.8601 |
257
+ | No log | 34.3333 | 412 | 0.7388 | 0.6067 | 0.7388 | 0.8595 |
258
+ | No log | 34.5 | 414 | 0.7347 | 0.6148 | 0.7347 | 0.8572 |
259
+ | No log | 34.6667 | 416 | 0.7273 | 0.6211 | 0.7273 | 0.8528 |
260
+ | No log | 34.8333 | 418 | 0.7342 | 0.5886 | 0.7342 | 0.8569 |
261
+ | No log | 35.0 | 420 | 0.7443 | 0.5720 | 0.7443 | 0.8627 |
262
+ | No log | 35.1667 | 422 | 0.7729 | 0.5927 | 0.7729 | 0.8791 |
263
+ | No log | 35.3333 | 424 | 0.7785 | 0.5380 | 0.7785 | 0.8823 |
264
+ | No log | 35.5 | 426 | 0.7534 | 0.5057 | 0.7534 | 0.8680 |
265
+ | No log | 35.6667 | 428 | 0.7428 | 0.5749 | 0.7428 | 0.8619 |
266
+ | No log | 35.8333 | 430 | 0.7289 | 0.5886 | 0.7289 | 0.8538 |
267
+ | No log | 36.0 | 432 | 0.7396 | 0.6088 | 0.7396 | 0.8600 |
268
+ | No log | 36.1667 | 434 | 0.7383 | 0.6098 | 0.7383 | 0.8593 |
269
+ | No log | 36.3333 | 436 | 0.7283 | 0.5543 | 0.7283 | 0.8534 |
270
+ | No log | 36.5 | 438 | 0.7212 | 0.5543 | 0.7212 | 0.8492 |
271
+ | No log | 36.6667 | 440 | 0.7201 | 0.6189 | 0.7201 | 0.8486 |
272
+ | No log | 36.8333 | 442 | 0.7256 | 0.6189 | 0.7256 | 0.8518 |
273
+ | No log | 37.0 | 444 | 0.7304 | 0.6189 | 0.7304 | 0.8546 |
274
+ | No log | 37.1667 | 446 | 0.7377 | 0.6098 | 0.7377 | 0.8589 |
275
+ | No log | 37.3333 | 448 | 0.7406 | 0.6066 | 0.7406 | 0.8606 |
276
+ | No log | 37.5 | 450 | 0.7318 | 0.6202 | 0.7318 | 0.8555 |
277
+ | No log | 37.6667 | 452 | 0.7204 | 0.6590 | 0.7204 | 0.8488 |
278
+ | No log | 37.8333 | 454 | 0.7034 | 0.6078 | 0.7034 | 0.8387 |
279
+ | No log | 38.0 | 456 | 0.7102 | 0.6279 | 0.7102 | 0.8427 |
280
+ | No log | 38.1667 | 458 | 0.7290 | 0.6088 | 0.7290 | 0.8538 |
281
+ | No log | 38.3333 | 460 | 0.7444 | 0.5886 | 0.7444 | 0.8628 |
282
+ | No log | 38.5 | 462 | 0.7489 | 0.5720 | 0.7489 | 0.8654 |
283
+ | No log | 38.6667 | 464 | 0.7435 | 0.5659 | 0.7435 | 0.8622 |
284
+ | No log | 38.8333 | 466 | 0.7370 | 0.5797 | 0.7370 | 0.8585 |
285
+ | No log | 39.0 | 468 | 0.7270 | 0.5621 | 0.7270 | 0.8526 |
286
+ | No log | 39.1667 | 470 | 0.7200 | 0.5621 | 0.7200 | 0.8485 |
287
+ | No log | 39.3333 | 472 | 0.7088 | 0.5966 | 0.7088 | 0.8419 |
288
+ | No log | 39.5 | 474 | 0.7042 | 0.6130 | 0.7042 | 0.8392 |
289
+ | No log | 39.6667 | 476 | 0.7067 | 0.5797 | 0.7067 | 0.8406 |
290
+ | No log | 39.8333 | 478 | 0.7105 | 0.6189 | 0.7105 | 0.8429 |
291
+ | No log | 40.0 | 480 | 0.7101 | 0.6015 | 0.7101 | 0.8427 |
292
+ | No log | 40.1667 | 482 | 0.7216 | 0.5835 | 0.7216 | 0.8495 |
293
+ | No log | 40.3333 | 484 | 0.7336 | 0.6015 | 0.7336 | 0.8565 |
294
+ | No log | 40.5 | 486 | 0.7558 | 0.5424 | 0.7558 | 0.8693 |
295
+ | No log | 40.6667 | 488 | 0.7691 | 0.5401 | 0.7691 | 0.8770 |
296
+ | No log | 40.8333 | 490 | 0.7712 | 0.5725 | 0.7712 | 0.8782 |
297
+ | No log | 41.0 | 492 | 0.7719 | 0.5860 | 0.7719 | 0.8786 |
298
+ | No log | 41.1667 | 494 | 0.7800 | 0.5646 | 0.7800 | 0.8832 |
299
+ | No log | 41.3333 | 496 | 0.7790 | 0.6184 | 0.7790 | 0.8826 |
300
+ | No log | 41.5 | 498 | 0.7717 | 0.6184 | 0.7717 | 0.8785 |
301
+ | 0.2373 | 41.6667 | 500 | 0.7587 | 0.5660 | 0.7587 | 0.8710 |
302
+ | 0.2373 | 41.8333 | 502 | 0.7509 | 0.5660 | 0.7509 | 0.8665 |
303
+ | 0.2373 | 42.0 | 504 | 0.7512 | 0.5547 | 0.7512 | 0.8667 |
304
+ | 0.2373 | 42.1667 | 506 | 0.7518 | 0.5451 | 0.7518 | 0.8671 |
305
+ | 0.2373 | 42.3333 | 508 | 0.7527 | 0.5380 | 0.7527 | 0.8676 |
306
+ | 0.2373 | 42.5 | 510 | 0.7687 | 0.5259 | 0.7687 | 0.8768 |
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 sha256:3bd3c528a805135d0f44d8a65e26de37e22b6d5c022fb658e0eee5b725ee5ce3
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:432f65607d65ab75983bbef56d6315b2a5a76b068bb86f1792c6e88297766128
3
+ size 5304