Hugging Face's logo

MayBashendy
/
ArabicNewSplits7_B_usingALLEssays_FineTuningAraBERT_run1_AugV5_k19_task2_organization

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
 Community
MayBashendy commited on
Commit
6205f7f
·
verified ·
1 Parent(s): 5af1fec

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_B_usingALLEssays_FineTuningAraBERT_run1_AugV5_k18_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_B_usingALLEssays_FineTuningAraBERT_run1_AugV5_k18_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: 0.7778
19
+ - Qwk: 0.4738
20
+ - Mse: 0.7778
21
+ - Rmse: 0.8819
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 | 0.0217 | 2 | 4.6652 | 0.0010 | 4.6652 | 2.1599 |
53
+ | No log | 0.0435 | 4 | 2.6487 | 0.0274 | 2.6487 | 1.6275 |
54
+ | No log | 0.0652 | 6 | 2.0063 | -0.0102 | 2.0063 | 1.4164 |
55
+ | No log | 0.0870 | 8 | 1.5790 | -0.0080 | 1.5790 | 1.2566 |
56
+ | No log | 0.1087 | 10 | 1.2454 | 0.2439 | 1.2454 | 1.1160 |
57
+ | No log | 0.1304 | 12 | 1.1039 | 0.2825 | 1.1039 | 1.0507 |
58
+ | No log | 0.1522 | 14 | 2.0706 | 0.1798 | 2.0706 | 1.4390 |
59
+ | No log | 0.1739 | 16 | 2.5444 | 0.0911 | 2.5444 | 1.5951 |
60
+ | No log | 0.1957 | 18 | 1.9002 | 0.1745 | 1.9002 | 1.3785 |
61
+ | No log | 0.2174 | 20 | 1.4771 | 0.2369 | 1.4771 | 1.2154 |
62
+ | No log | 0.2391 | 22 | 1.0236 | 0.3009 | 1.0236 | 1.0117 |
63
+ | No log | 0.2609 | 24 | 1.0399 | 0.3250 | 1.0399 | 1.0198 |
64
+ | No log | 0.2826 | 26 | 1.1164 | 0.2769 | 1.1164 | 1.0566 |
65
+ | No log | 0.3043 | 28 | 1.2926 | 0.1722 | 1.2926 | 1.1369 |
66
+ | No log | 0.3261 | 30 | 1.3592 | 0.1279 | 1.3592 | 1.1659 |
67
+ | No log | 0.3478 | 32 | 1.2460 | 0.0878 | 1.2460 | 1.1162 |
68
+ | No log | 0.3696 | 34 | 1.1727 | 0.1438 | 1.1727 | 1.0829 |
69
+ | No log | 0.3913 | 36 | 1.0689 | 0.2191 | 1.0689 | 1.0339 |
70
+ | No log | 0.4130 | 38 | 1.0357 | 0.3338 | 1.0357 | 1.0177 |
71
+ | No log | 0.4348 | 40 | 1.0294 | 0.3635 | 1.0294 | 1.0146 |
72
+ | No log | 0.4565 | 42 | 1.0333 | 0.2589 | 1.0333 | 1.0165 |
73
+ | No log | 0.4783 | 44 | 1.2002 | 0.2384 | 1.2002 | 1.0956 |
74
+ | No log | 0.5 | 46 | 1.3254 | 0.2653 | 1.3254 | 1.1513 |
75
+ | No log | 0.5217 | 48 | 1.5583 | 0.2682 | 1.5583 | 1.2483 |
76
+ | No log | 0.5435 | 50 | 1.4867 | 0.2619 | 1.4867 | 1.2193 |
77
+ | No log | 0.5652 | 52 | 1.1870 | 0.2456 | 1.1870 | 1.0895 |
78
+ | No log | 0.5870 | 54 | 1.0339 | 0.3380 | 1.0339 | 1.0168 |
79
+ | No log | 0.6087 | 56 | 0.9934 | 0.3601 | 0.9934 | 0.9967 |
80
+ | No log | 0.6304 | 58 | 0.8708 | 0.4507 | 0.8708 | 0.9331 |
81
+ | No log | 0.6522 | 60 | 0.9380 | 0.3981 | 0.9380 | 0.9685 |
82
+ | No log | 0.6739 | 62 | 1.0752 | 0.3660 | 1.0752 | 1.0369 |
83
+ | No log | 0.6957 | 64 | 0.9696 | 0.3959 | 0.9696 | 0.9847 |
84
+ | No log | 0.7174 | 66 | 0.7320 | 0.4927 | 0.7320 | 0.8555 |
85
+ | No log | 0.7391 | 68 | 0.7735 | 0.5065 | 0.7735 | 0.8795 |
86
+ | No log | 0.7609 | 70 | 0.7351 | 0.5028 | 0.7351 | 0.8574 |
87
+ | No log | 0.7826 | 72 | 1.0433 | 0.3739 | 1.0433 | 1.0214 |
88
+ | No log | 0.8043 | 74 | 1.3174 | 0.4 | 1.3174 | 1.1478 |
89
+ | No log | 0.8261 | 76 | 1.1638 | 0.3453 | 1.1638 | 1.0788 |
90
+ | No log | 0.8478 | 78 | 1.1532 | 0.3067 | 1.1532 | 1.0739 |
91
+ | No log | 0.8696 | 80 | 1.4792 | 0.3295 | 1.4792 | 1.2162 |
92
+ | No log | 0.8913 | 82 | 1.5024 | 0.3101 | 1.5024 | 1.2257 |
93
+ | No log | 0.9130 | 84 | 1.1909 | 0.3831 | 1.1909 | 1.0913 |
94
+ | No log | 0.9348 | 86 | 0.8498 | 0.4211 | 0.8498 | 0.9219 |
95
+ | No log | 0.9565 | 88 | 0.8427 | 0.4598 | 0.8427 | 0.9180 |
96
+ | No log | 0.9783 | 90 | 0.8937 | 0.4578 | 0.8937 | 0.9454 |
97
+ | No log | 1.0 | 92 | 1.0178 | 0.3835 | 1.0178 | 1.0089 |
98
+ | No log | 1.0217 | 94 | 0.8968 | 0.4690 | 0.8968 | 0.9470 |
99
+ | No log | 1.0435 | 96 | 0.7624 | 0.6107 | 0.7624 | 0.8732 |
100
+ | No log | 1.0652 | 98 | 0.7573 | 0.5963 | 0.7573 | 0.8702 |
101
+ | No log | 1.0870 | 100 | 0.7558 | 0.6272 | 0.7558 | 0.8694 |
102
+ | No log | 1.1087 | 102 | 0.8521 | 0.5427 | 0.8521 | 0.9231 |
103
+ | No log | 1.1304 | 104 | 1.0819 | 0.4857 | 1.0819 | 1.0401 |
104
+ | No log | 1.1522 | 106 | 1.0864 | 0.4616 | 1.0864 | 1.0423 |
105
+ | No log | 1.1739 | 108 | 0.8190 | 0.5006 | 0.8190 | 0.9050 |
106
+ | No log | 1.1957 | 110 | 0.7597 | 0.5415 | 0.7597 | 0.8716 |
107
+ | No log | 1.2174 | 112 | 0.7572 | 0.5483 | 0.7572 | 0.8702 |
108
+ | No log | 1.2391 | 114 | 0.7820 | 0.5565 | 0.7820 | 0.8843 |
109
+ | No log | 1.2609 | 116 | 1.0355 | 0.5262 | 1.0355 | 1.0176 |
110
+ | No log | 1.2826 | 118 | 1.1753 | 0.4667 | 1.1753 | 1.0841 |
111
+ | No log | 1.3043 | 120 | 0.9746 | 0.5237 | 0.9746 | 0.9872 |
112
+ | No log | 1.3261 | 122 | 0.7961 | 0.5895 | 0.7961 | 0.8923 |
113
+ | No log | 1.3478 | 124 | 0.7961 | 0.6163 | 0.7961 | 0.8922 |
114
+ | No log | 1.3696 | 126 | 0.8479 | 0.5253 | 0.8479 | 0.9208 |
115
+ | No log | 1.3913 | 128 | 0.8341 | 0.5334 | 0.8341 | 0.9133 |
116
+ | No log | 1.4130 | 130 | 0.8273 | 0.4937 | 0.8273 | 0.9095 |
117
+ | No log | 1.4348 | 132 | 0.8456 | 0.4816 | 0.8456 | 0.9196 |
118
+ | No log | 1.4565 | 134 | 0.8800 | 0.4565 | 0.8800 | 0.9381 |
119
+ | No log | 1.4783 | 136 | 0.8543 | 0.4736 | 0.8543 | 0.9243 |
120
+ | No log | 1.5 | 138 | 0.8315 | 0.4746 | 0.8315 | 0.9118 |
121
+ | No log | 1.5217 | 140 | 0.8342 | 0.5407 | 0.8342 | 0.9134 |
122
+ | No log | 1.5435 | 142 | 0.8225 | 0.5469 | 0.8225 | 0.9069 |
123
+ | No log | 1.5652 | 144 | 0.8150 | 0.5439 | 0.8150 | 0.9028 |
124
+ | No log | 1.5870 | 146 | 0.8253 | 0.4668 | 0.8253 | 0.9084 |
125
+ | No log | 1.6087 | 148 | 0.8191 | 0.5160 | 0.8191 | 0.9051 |
126
+ | No log | 1.6304 | 150 | 0.8491 | 0.4624 | 0.8491 | 0.9214 |
127
+ | No log | 1.6522 | 152 | 0.8550 | 0.5075 | 0.8550 | 0.9246 |
128
+ | No log | 1.6739 | 154 | 0.8309 | 0.5861 | 0.8309 | 0.9115 |
129
+ | No log | 1.6957 | 156 | 0.8569 | 0.5946 | 0.8569 | 0.9257 |
130
+ | No log | 1.7174 | 158 | 0.9412 | 0.5133 | 0.9412 | 0.9701 |
131
+ | No log | 1.7391 | 160 | 0.9338 | 0.5178 | 0.9338 | 0.9663 |
132
+ | No log | 1.7609 | 162 | 0.8996 | 0.5607 | 0.8996 | 0.9485 |
133
+ | No log | 1.7826 | 164 | 0.9857 | 0.4992 | 0.9857 | 0.9928 |
134
+ | No log | 1.8043 | 166 | 0.9663 | 0.5075 | 0.9663 | 0.9830 |
135
+ | No log | 1.8261 | 168 | 0.9243 | 0.4853 | 0.9243 | 0.9614 |
136
+ | No log | 1.8478 | 170 | 0.9597 | 0.5305 | 0.9597 | 0.9796 |
137
+ | No log | 1.8696 | 172 | 0.9427 | 0.3617 | 0.9427 | 0.9709 |
138
+ | No log | 1.8913 | 174 | 1.1722 | 0.4329 | 1.1722 | 1.0827 |
139
+ | No log | 1.9130 | 176 | 1.2712 | 0.3930 | 1.2712 | 1.1275 |
140
+ | No log | 1.9348 | 178 | 1.0472 | 0.3557 | 1.0472 | 1.0233 |
141
+ | No log | 1.9565 | 180 | 0.9393 | 0.3985 | 0.9393 | 0.9692 |
142
+ | No log | 1.9783 | 182 | 0.9544 | 0.4017 | 0.9544 | 0.9769 |
143
+ | No log | 2.0 | 184 | 1.0193 | 0.3568 | 1.0193 | 1.0096 |
144
+ | No log | 2.0217 | 186 | 1.1475 | 0.4217 | 1.1475 | 1.0712 |
145
+ | No log | 2.0435 | 188 | 1.1448 | 0.3798 | 1.1448 | 1.0699 |
146
+ | No log | 2.0652 | 190 | 1.1581 | 0.4073 | 1.1581 | 1.0762 |
147
+ | No log | 2.0870 | 192 | 1.0058 | 0.4032 | 1.0058 | 1.0029 |
148
+ | No log | 2.1087 | 194 | 0.9201 | 0.4306 | 0.9201 | 0.9592 |
149
+ | No log | 2.1304 | 196 | 0.9356 | 0.4181 | 0.9356 | 0.9673 |
150
+ | No log | 2.1522 | 198 | 0.9502 | 0.4548 | 0.9502 | 0.9748 |
151
+ | No log | 2.1739 | 200 | 1.2493 | 0.4492 | 1.2493 | 1.1177 |
152
+ | No log | 2.1957 | 202 | 1.3142 | 0.4279 | 1.3142 | 1.1464 |
153
+ | No log | 2.2174 | 204 | 1.0528 | 0.3847 | 1.0528 | 1.0261 |
154
+ | No log | 2.2391 | 206 | 0.9452 | 0.4291 | 0.9452 | 0.9722 |
155
+ | No log | 2.2609 | 208 | 1.0371 | 0.4468 | 1.0371 | 1.0184 |
156
+ | No log | 2.2826 | 210 | 0.9865 | 0.4378 | 0.9865 | 0.9932 |
157
+ | No log | 2.3043 | 212 | 0.9079 | 0.4277 | 0.9079 | 0.9528 |
158
+ | No log | 2.3261 | 214 | 1.0335 | 0.3869 | 1.0335 | 1.0166 |
159
+ | No log | 2.3478 | 216 | 1.4235 | 0.3462 | 1.4235 | 1.1931 |
160
+ | No log | 2.3696 | 218 | 1.5530 | 0.3139 | 1.5530 | 1.2462 |
161
+ | No log | 2.3913 | 220 | 1.3082 | 0.3494 | 1.3082 | 1.1438 |
162
+ | No log | 2.4130 | 222 | 0.9710 | 0.3665 | 0.9710 | 0.9854 |
163
+ | No log | 2.4348 | 224 | 0.8840 | 0.4653 | 0.8840 | 0.9402 |
164
+ | No log | 2.4565 | 226 | 0.8818 | 0.4593 | 0.8818 | 0.9390 |
165
+ | No log | 2.4783 | 228 | 0.9441 | 0.3944 | 0.9441 | 0.9716 |
166
+ | No log | 2.5 | 230 | 0.9825 | 0.4224 | 0.9825 | 0.9912 |
167
+ | No log | 2.5217 | 232 | 0.9054 | 0.4384 | 0.9054 | 0.9515 |
168
+ | No log | 2.5435 | 234 | 0.8736 | 0.5098 | 0.8736 | 0.9346 |
169
+ | No log | 2.5652 | 236 | 0.8610 | 0.5359 | 0.8610 | 0.9279 |
170
+ | No log | 2.5870 | 238 | 0.8626 | 0.4808 | 0.8626 | 0.9288 |
171
+ | No log | 2.6087 | 240 | 0.8512 | 0.4219 | 0.8512 | 0.9226 |
172
+ | No log | 2.6304 | 242 | 0.8776 | 0.4396 | 0.8776 | 0.9368 |
173
+ | No log | 2.6522 | 244 | 0.8812 | 0.4464 | 0.8812 | 0.9387 |
174
+ | No log | 2.6739 | 246 | 0.8407 | 0.4709 | 0.8407 | 0.9169 |
175
+ | No log | 2.6957 | 248 | 0.8720 | 0.4369 | 0.8720 | 0.9338 |
176
+ | No log | 2.7174 | 250 | 0.9620 | 0.5291 | 0.9620 | 0.9808 |
177
+ | No log | 2.7391 | 252 | 1.0071 | 0.5118 | 1.0071 | 1.0035 |
178
+ | No log | 2.7609 | 254 | 0.9522 | 0.5160 | 0.9522 | 0.9758 |
179
+ | No log | 2.7826 | 256 | 0.8533 | 0.4976 | 0.8533 | 0.9237 |
180
+ | No log | 2.8043 | 258 | 0.8258 | 0.4256 | 0.8258 | 0.9087 |
181
+ | No log | 2.8261 | 260 | 0.8434 | 0.5230 | 0.8434 | 0.9184 |
182
+ | No log | 2.8478 | 262 | 0.8248 | 0.5259 | 0.8248 | 0.9082 |
183
+ | No log | 2.8696 | 264 | 0.8387 | 0.4929 | 0.8387 | 0.9158 |
184
+ | No log | 2.8913 | 266 | 0.8615 | 0.4368 | 0.8615 | 0.9282 |
185
+ | No log | 2.9130 | 268 | 0.8630 | 0.4368 | 0.8630 | 0.9290 |
186
+ | No log | 2.9348 | 270 | 0.8394 | 0.4749 | 0.8394 | 0.9162 |
187
+ | No log | 2.9565 | 272 | 0.8437 | 0.4483 | 0.8437 | 0.9185 |
188
+ | No log | 2.9783 | 274 | 0.9025 | 0.4694 | 0.9025 | 0.9500 |
189
+ | No log | 3.0 | 276 | 0.9115 | 0.4694 | 0.9115 | 0.9547 |
190
+ | No log | 3.0217 | 278 | 0.8880 | 0.4672 | 0.8880 | 0.9424 |
191
+ | No log | 3.0435 | 280 | 0.9069 | 0.4369 | 0.9069 | 0.9523 |
192
+ | No log | 3.0652 | 282 | 0.8998 | 0.4430 | 0.8998 | 0.9486 |
193
+ | No log | 3.0870 | 284 | 0.8836 | 0.4396 | 0.8836 | 0.9400 |
194
+ | No log | 3.1087 | 286 | 0.8719 | 0.4460 | 0.8719 | 0.9338 |
195
+ | No log | 3.1304 | 288 | 0.8611 | 0.5216 | 0.8611 | 0.9280 |
196
+ | No log | 3.1522 | 290 | 0.8768 | 0.4782 | 0.8768 | 0.9364 |
197
+ | No log | 3.1739 | 292 | 0.8938 | 0.4686 | 0.8938 | 0.9454 |
198
+ | No log | 3.1957 | 294 | 0.9108 | 0.4815 | 0.9108 | 0.9544 |
199
+ | No log | 3.2174 | 296 | 0.9324 | 0.4212 | 0.9324 | 0.9656 |
200
+ | No log | 3.2391 | 298 | 0.9466 | 0.3805 | 0.9466 | 0.9729 |
201
+ | No log | 3.2609 | 300 | 0.8596 | 0.4234 | 0.8596 | 0.9271 |
202
+ | No log | 3.2826 | 302 | 0.8229 | 0.5916 | 0.8229 | 0.9071 |
203
+ | No log | 3.3043 | 304 | 0.8069 | 0.5854 | 0.8069 | 0.8983 |
204
+ | No log | 3.3261 | 306 | 0.8264 | 0.5504 | 0.8264 | 0.9091 |
205
+ | No log | 3.3478 | 308 | 0.8170 | 0.5530 | 0.8170 | 0.9039 |
206
+ | No log | 3.3696 | 310 | 0.8286 | 0.4792 | 0.8286 | 0.9103 |
207
+ | No log | 3.3913 | 312 | 0.8343 | 0.4535 | 0.8343 | 0.9134 |
208
+ | No log | 3.4130 | 314 | 0.8515 | 0.5194 | 0.8515 | 0.9228 |
209
+ | No log | 3.4348 | 316 | 0.8486 | 0.4902 | 0.8486 | 0.9212 |
210
+ | No log | 3.4565 | 318 | 0.8990 | 0.4138 | 0.8990 | 0.9482 |
211
+ | No log | 3.4783 | 320 | 0.9259 | 0.3625 | 0.9259 | 0.9622 |
212
+ | No log | 3.5 | 322 | 0.8727 | 0.4066 | 0.8727 | 0.9342 |
213
+ | No log | 3.5217 | 324 | 0.8814 | 0.4440 | 0.8814 | 0.9388 |
214
+ | No log | 3.5435 | 326 | 0.8784 | 0.4029 | 0.8784 | 0.9372 |
215
+ | No log | 3.5652 | 328 | 0.8784 | 0.4778 | 0.8784 | 0.9372 |
216
+ | No log | 3.5870 | 330 | 0.8843 | 0.4029 | 0.8843 | 0.9404 |
217
+ | No log | 3.6087 | 332 | 0.9498 | 0.3584 | 0.9498 | 0.9746 |
218
+ | No log | 3.6304 | 334 | 1.0136 | 0.3298 | 1.0136 | 1.0068 |
219
+ | No log | 3.6522 | 336 | 0.9349 | 0.4294 | 0.9349 | 0.9669 |
220
+ | No log | 3.6739 | 338 | 0.9438 | 0.3533 | 0.9438 | 0.9715 |
221
+ | No log | 3.6957 | 340 | 1.0083 | 0.4166 | 1.0083 | 1.0042 |
222
+ | No log | 3.7174 | 342 | 0.9373 | 0.3533 | 0.9373 | 0.9682 |
223
+ | No log | 3.7391 | 344 | 0.9850 | 0.3865 | 0.9850 | 0.9925 |
224
+ | No log | 3.7609 | 346 | 1.2369 | 0.3974 | 1.2369 | 1.1122 |
225
+ | No log | 3.7826 | 348 | 1.2721 | 0.3974 | 1.2721 | 1.1279 |
226
+ | No log | 3.8043 | 350 | 1.1010 | 0.3537 | 1.1010 | 1.0493 |
227
+ | No log | 3.8261 | 352 | 0.9416 | 0.4045 | 0.9416 | 0.9704 |
228
+ | No log | 3.8478 | 354 | 0.8856 | 0.4349 | 0.8856 | 0.9411 |
229
+ | No log | 3.8696 | 356 | 0.8706 | 0.4349 | 0.8706 | 0.9331 |
230
+ | No log | 3.8913 | 358 | 0.9272 | 0.3255 | 0.9272 | 0.9629 |
231
+ | No log | 3.9130 | 360 | 1.1541 | 0.4186 | 1.1541 | 1.0743 |
232
+ | No log | 3.9348 | 362 | 1.1100 | 0.4272 | 1.1100 | 1.0536 |
233
+ | No log | 3.9565 | 364 | 0.8960 | 0.3714 | 0.8960 | 0.9466 |
234
+ | No log | 3.9783 | 366 | 0.8432 | 0.3991 | 0.8432 | 0.9183 |
235
+ | No log | 4.0 | 368 | 0.8547 | 0.4311 | 0.8547 | 0.9245 |
236
+ | No log | 4.0217 | 370 | 0.8498 | 0.3943 | 0.8498 | 0.9219 |
237
+ | No log | 4.0435 | 372 | 0.8758 | 0.4334 | 0.8758 | 0.9358 |
238
+ | No log | 4.0652 | 374 | 0.9423 | 0.3165 | 0.9423 | 0.9707 |
239
+ | No log | 4.0870 | 376 | 0.9528 | 0.3298 | 0.9528 | 0.9761 |
240
+ | No log | 4.1087 | 378 | 0.9629 | 0.3207 | 0.9629 | 0.9813 |
241
+ | No log | 4.1304 | 380 | 0.9435 | 0.3074 | 0.9435 | 0.9714 |
242
+ | No log | 4.1522 | 382 | 0.9988 | 0.3828 | 0.9988 | 0.9994 |
243
+ | No log | 4.1739 | 384 | 1.1433 | 0.4101 | 1.1433 | 1.0693 |
244
+ | No log | 4.1957 | 386 | 1.3750 | 0.3879 | 1.3750 | 1.1726 |
245
+ | No log | 4.2174 | 388 | 1.3741 | 0.3688 | 1.3741 | 1.1722 |
246
+ | No log | 4.2391 | 390 | 1.0747 | 0.4476 | 1.0747 | 1.0367 |
247
+ | No log | 4.2609 | 392 | 0.8681 | 0.4007 | 0.8681 | 0.9317 |
248
+ | No log | 4.2826 | 394 | 0.8841 | 0.4240 | 0.8841 | 0.9403 |
249
+ | No log | 4.3043 | 396 | 0.9003 | 0.4381 | 0.9003 | 0.9488 |
250
+ | No log | 4.3261 | 398 | 0.9119 | 0.4158 | 0.9119 | 0.9550 |
251
+ | No log | 4.3478 | 400 | 0.9693 | 0.3381 | 0.9693 | 0.9845 |
252
+ | No log | 4.3696 | 402 | 0.9695 | 0.3439 | 0.9695 | 0.9846 |
253
+ | No log | 4.3913 | 404 | 0.9285 | 0.3709 | 0.9285 | 0.9636 |
254
+ | No log | 4.4130 | 406 | 0.8949 | 0.4007 | 0.8949 | 0.9460 |
255
+ | No log | 4.4348 | 408 | 0.8841 | 0.4050 | 0.8841 | 0.9403 |
256
+ | No log | 4.4565 | 410 | 0.9050 | 0.4239 | 0.9050 | 0.9513 |
257
+ | No log | 4.4783 | 412 | 0.8858 | 0.3671 | 0.8858 | 0.9412 |
258
+ | No log | 4.5 | 414 | 0.9131 | 0.4823 | 0.9131 | 0.9556 |
259
+ | No log | 4.5217 | 416 | 1.1168 | 0.4664 | 1.1168 | 1.0568 |
260
+ | No log | 4.5435 | 418 | 1.2743 | 0.3498 | 1.2743 | 1.1288 |
261
+ | No log | 4.5652 | 420 | 1.1583 | 0.4387 | 1.1583 | 1.0762 |
262
+ | No log | 4.5870 | 422 | 0.9711 | 0.4654 | 0.9711 | 0.9854 |
263
+ | No log | 4.6087 | 424 | 0.8733 | 0.4066 | 0.8733 | 0.9345 |
264
+ | No log | 4.6304 | 426 | 0.8899 | 0.4620 | 0.8899 | 0.9434 |
265
+ | No log | 4.6522 | 428 | 0.8843 | 0.5636 | 0.8843 | 0.9404 |
266
+ | No log | 4.6739 | 430 | 0.8416 | 0.5205 | 0.8416 | 0.9174 |
267
+ | No log | 4.6957 | 432 | 0.8693 | 0.5462 | 0.8693 | 0.9324 |
268
+ | No log | 4.7174 | 434 | 0.9323 | 0.4838 | 0.9323 | 0.9655 |
269
+ | No log | 4.7391 | 436 | 0.9294 | 0.4644 | 0.9294 | 0.9641 |
270
+ | No log | 4.7609 | 438 | 0.9964 | 0.4344 | 0.9964 | 0.9982 |
271
+ | No log | 4.7826 | 440 | 1.0942 | 0.4743 | 1.0942 | 1.0460 |
272
+ | No log | 4.8043 | 442 | 1.0588 | 0.4492 | 1.0588 | 1.0290 |
273
+ | No log | 4.8261 | 444 | 0.9596 | 0.3928 | 0.9596 | 0.9796 |
274
+ | No log | 4.8478 | 446 | 0.8970 | 0.4454 | 0.8970 | 0.9471 |
275
+ | No log | 4.8696 | 448 | 0.8593 | 0.4712 | 0.8593 | 0.9270 |
276
+ | No log | 4.8913 | 450 | 0.8615 | 0.4510 | 0.8615 | 0.9281 |
277
+ | No log | 4.9130 | 452 | 0.9477 | 0.3573 | 0.9477 | 0.9735 |
278
+ | No log | 4.9348 | 454 | 1.0406 | 0.3777 | 1.0406 | 1.0201 |
279
+ | No log | 4.9565 | 456 | 0.9390 | 0.3913 | 0.9390 | 0.9690 |
280
+ | No log | 4.9783 | 458 | 0.8113 | 0.5089 | 0.8113 | 0.9007 |
281
+ | No log | 5.0 | 460 | 0.8695 | 0.4553 | 0.8695 | 0.9325 |
282
+ | No log | 5.0217 | 462 | 0.9058 | 0.4606 | 0.9058 | 0.9517 |
283
+ | No log | 5.0435 | 464 | 0.8726 | 0.4164 | 0.8726 | 0.9342 |
284
+ | No log | 5.0652 | 466 | 0.8236 | 0.4280 | 0.8236 | 0.9075 |
285
+ | No log | 5.0870 | 468 | 0.8619 | 0.4507 | 0.8619 | 0.9284 |
286
+ | No log | 5.1087 | 470 | 0.9089 | 0.4285 | 0.9089 | 0.9534 |
287
+ | No log | 5.1304 | 472 | 0.8967 | 0.4213 | 0.8967 | 0.9470 |
288
+ | No log | 5.1522 | 474 | 0.8522 | 0.3965 | 0.8522 | 0.9231 |
289
+ | No log | 5.1739 | 476 | 0.8304 | 0.4780 | 0.8304 | 0.9113 |
290
+ | No log | 5.1957 | 478 | 0.8304 | 0.4780 | 0.8304 | 0.9113 |
291
+ | No log | 5.2174 | 480 | 0.8303 | 0.4813 | 0.8303 | 0.9112 |
292
+ | No log | 5.2391 | 482 | 0.8373 | 0.4485 | 0.8373 | 0.9150 |
293
+ | No log | 5.2609 | 484 | 0.8487 | 0.4064 | 0.8487 | 0.9213 |
294
+ | No log | 5.2826 | 486 | 0.8715 | 0.3806 | 0.8715 | 0.9336 |
295
+ | No log | 5.3043 | 488 | 0.8852 | 0.3806 | 0.8852 | 0.9408 |
296
+ | No log | 5.3261 | 490 | 0.8828 | 0.3852 | 0.8828 | 0.9396 |
297
+ | No log | 5.3478 | 492 | 0.8526 | 0.4234 | 0.8526 | 0.9234 |
298
+ | No log | 5.3696 | 494 | 0.8317 | 0.3829 | 0.8317 | 0.9120 |
299
+ | No log | 5.3913 | 496 | 0.8229 | 0.3829 | 0.8229 | 0.9071 |
300
+ | No log | 5.4130 | 498 | 0.8137 | 0.4159 | 0.8137 | 0.9021 |
301
+ | 0.3719 | 5.4348 | 500 | 0.7940 | 0.5089 | 0.7940 | 0.8910 |
302
+ | 0.3719 | 5.4565 | 502 | 0.7865 | 0.5053 | 0.7865 | 0.8868 |
303
+ | 0.3719 | 5.4783 | 504 | 0.8154 | 0.4672 | 0.8154 | 0.9030 |
304
+ | 0.3719 | 5.5 | 506 | 0.8582 | 0.4959 | 0.8582 | 0.9264 |
305
+ | 0.3719 | 5.5217 | 508 | 0.8110 | 0.4469 | 0.8110 | 0.9006 |
306
+ | 0.3719 | 5.5435 | 510 | 0.7778 | 0.4738 | 0.7778 | 0.8819 |
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:dec7a9a8189375bf842f02ef3cdfaea010d23d112344744547d9d04922a7b2ae
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a6b584bd0b256b7831a683c0ecee537402a0a07a7c6e63483557e6d631f509af
3
+ size 5304