Hugging Face's logo

MayBashendy
/
Arabic_CrossPrompt_FineTuningAraBERT_noAug_TestTask5_holistic

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
Community
MayBashendy commited on
Commit
da4a470
·
verified ·
1 Parent(s): f7176ec

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: Arabic_CrossPrompt_FineTuningAraBERT_noAug_TestTask5_grammar
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
+ # Arabic_CrossPrompt_FineTuningAraBERT_noAug_TestTask5_grammar
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: 0.4960
19
+ - Qwk: 0.5793
20
+ - Mse: 0.4960
21
+ - Rmse: 0.7043
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 | 0.0198 | 2 | 4.3214 | -0.0067 | 4.3214 | 2.0788 |
53
+ | No log | 0.0396 | 4 | 2.9172 | 0.0635 | 2.9172 | 1.7080 |
54
+ | No log | 0.0594 | 6 | 1.5062 | 0.0019 | 1.5062 | 1.2273 |
55
+ | No log | 0.0792 | 8 | 1.0377 | 0.0355 | 1.0377 | 1.0187 |
56
+ | No log | 0.0990 | 10 | 0.8053 | 0.1398 | 0.8053 | 0.8974 |
57
+ | No log | 0.1188 | 12 | 0.8507 | 0.0841 | 0.8507 | 0.9223 |
58
+ | No log | 0.1386 | 14 | 0.8907 | 0.1137 | 0.8907 | 0.9438 |
59
+ | No log | 0.1584 | 16 | 0.7907 | 0.1793 | 0.7907 | 0.8892 |
60
+ | No log | 0.1782 | 18 | 0.6838 | 0.3145 | 0.6838 | 0.8269 |
61
+ | No log | 0.1980 | 20 | 0.6677 | 0.3585 | 0.6677 | 0.8171 |
62
+ | No log | 0.2178 | 22 | 0.6864 | 0.2340 | 0.6864 | 0.8285 |
63
+ | No log | 0.2376 | 24 | 0.7205 | 0.1808 | 0.7205 | 0.8488 |
64
+ | No log | 0.2574 | 26 | 0.7187 | 0.1657 | 0.7187 | 0.8478 |
65
+ | No log | 0.2772 | 28 | 0.6942 | 0.1957 | 0.6942 | 0.8332 |
66
+ | No log | 0.2970 | 30 | 0.6759 | 0.3128 | 0.6759 | 0.8221 |
67
+ | No log | 0.3168 | 32 | 0.6765 | 0.3796 | 0.6765 | 0.8225 |
68
+ | No log | 0.3366 | 34 | 0.6537 | 0.4037 | 0.6537 | 0.8085 |
69
+ | No log | 0.3564 | 36 | 0.5938 | 0.3794 | 0.5938 | 0.7706 |
70
+ | No log | 0.3762 | 38 | 0.6528 | 0.3029 | 0.6528 | 0.8079 |
71
+ | No log | 0.3960 | 40 | 0.7335 | 0.3070 | 0.7335 | 0.8564 |
72
+ | No log | 0.4158 | 42 | 0.7371 | 0.3172 | 0.7371 | 0.8585 |
73
+ | No log | 0.4356 | 44 | 0.6799 | 0.3768 | 0.6799 | 0.8245 |
74
+ | No log | 0.4554 | 46 | 0.5893 | 0.4440 | 0.5893 | 0.7676 |
75
+ | No log | 0.4752 | 48 | 0.5767 | 0.4800 | 0.5767 | 0.7594 |
76
+ | No log | 0.4950 | 50 | 0.5700 | 0.4605 | 0.5700 | 0.7550 |
77
+ | No log | 0.5149 | 52 | 0.5702 | 0.4662 | 0.5702 | 0.7551 |
78
+ | No log | 0.5347 | 54 | 0.5748 | 0.4026 | 0.5748 | 0.7582 |
79
+ | No log | 0.5545 | 56 | 0.6172 | 0.3677 | 0.6172 | 0.7856 |
80
+ | No log | 0.5743 | 58 | 0.6126 | 0.3491 | 0.6126 | 0.7827 |
81
+ | No log | 0.5941 | 60 | 0.6277 | 0.3003 | 0.6277 | 0.7923 |
82
+ | No log | 0.6139 | 62 | 0.5893 | 0.4110 | 0.5893 | 0.7677 |
83
+ | No log | 0.6337 | 64 | 0.5511 | 0.4678 | 0.5511 | 0.7423 |
84
+ | No log | 0.6535 | 66 | 0.5633 | 0.5058 | 0.5633 | 0.7505 |
85
+ | No log | 0.6733 | 68 | 0.6189 | 0.5007 | 0.6189 | 0.7867 |
86
+ | No log | 0.6931 | 70 | 0.6102 | 0.5174 | 0.6102 | 0.7811 |
87
+ | No log | 0.7129 | 72 | 0.5809 | 0.5142 | 0.5809 | 0.7622 |
88
+ | No log | 0.7327 | 74 | 0.6551 | 0.4910 | 0.6551 | 0.8094 |
89
+ | No log | 0.7525 | 76 | 0.5688 | 0.5201 | 0.5688 | 0.7542 |
90
+ | No log | 0.7723 | 78 | 0.5110 | 0.5429 | 0.5110 | 0.7149 |
91
+ | No log | 0.7921 | 80 | 0.5380 | 0.3901 | 0.5380 | 0.7335 |
92
+ | No log | 0.8119 | 82 | 0.6138 | 0.3229 | 0.6138 | 0.7834 |
93
+ | No log | 0.8317 | 84 | 0.6101 | 0.3231 | 0.6101 | 0.7811 |
94
+ | No log | 0.8515 | 86 | 0.5300 | 0.4620 | 0.5300 | 0.7280 |
95
+ | No log | 0.8713 | 88 | 0.5203 | 0.5345 | 0.5203 | 0.7213 |
96
+ | No log | 0.8911 | 90 | 0.5034 | 0.5517 | 0.5034 | 0.7095 |
97
+ | No log | 0.9109 | 92 | 0.6162 | 0.3668 | 0.6162 | 0.7850 |
98
+ | No log | 0.9307 | 94 | 0.7100 | 0.3035 | 0.7100 | 0.8426 |
99
+ | No log | 0.9505 | 96 | 0.6769 | 0.3292 | 0.6769 | 0.8227 |
100
+ | No log | 0.9703 | 98 | 0.5643 | 0.4905 | 0.5643 | 0.7512 |
101
+ | No log | 0.9901 | 100 | 0.5014 | 0.5022 | 0.5014 | 0.7081 |
102
+ | No log | 1.0099 | 102 | 0.4908 | 0.5146 | 0.4908 | 0.7006 |
103
+ | No log | 1.0297 | 104 | 0.4970 | 0.4629 | 0.4970 | 0.7050 |
104
+ | No log | 1.0495 | 106 | 0.5592 | 0.3769 | 0.5592 | 0.7478 |
105
+ | No log | 1.0693 | 108 | 0.6568 | 0.4085 | 0.6568 | 0.8104 |
106
+ | No log | 1.0891 | 110 | 0.7098 | 0.4463 | 0.7098 | 0.8425 |
107
+ | No log | 1.1089 | 112 | 0.6890 | 0.4918 | 0.6890 | 0.8301 |
108
+ | No log | 1.1287 | 114 | 0.5379 | 0.5552 | 0.5379 | 0.7334 |
109
+ | No log | 1.1485 | 116 | 0.5224 | 0.5883 | 0.5224 | 0.7228 |
110
+ | No log | 1.1683 | 118 | 0.5042 | 0.6115 | 0.5042 | 0.7101 |
111
+ | No log | 1.1881 | 120 | 0.6940 | 0.5222 | 0.6940 | 0.8331 |
112
+ | No log | 1.2079 | 122 | 0.8069 | 0.3473 | 0.8069 | 0.8982 |
113
+ | No log | 1.2277 | 124 | 0.8173 | 0.2675 | 0.8173 | 0.9040 |
114
+ | No log | 1.2475 | 126 | 0.7003 | 0.4258 | 0.7003 | 0.8369 |
115
+ | No log | 1.2673 | 128 | 0.5189 | 0.5775 | 0.5189 | 0.7203 |
116
+ | No log | 1.2871 | 130 | 0.5131 | 0.5372 | 0.5131 | 0.7163 |
117
+ | No log | 1.3069 | 132 | 0.5103 | 0.5715 | 0.5103 | 0.7143 |
118
+ | No log | 1.3267 | 134 | 0.5104 | 0.4942 | 0.5104 | 0.7144 |
119
+ | No log | 1.3465 | 136 | 0.6700 | 0.4089 | 0.6700 | 0.8185 |
120
+ | No log | 1.3663 | 138 | 0.7466 | 0.3296 | 0.7466 | 0.8640 |
121
+ | No log | 1.3861 | 140 | 0.6965 | 0.4316 | 0.6965 | 0.8346 |
122
+ | No log | 1.4059 | 142 | 0.6386 | 0.5585 | 0.6386 | 0.7991 |
123
+ | No log | 1.4257 | 144 | 0.6027 | 0.5620 | 0.6027 | 0.7763 |
124
+ | No log | 1.4455 | 146 | 0.4979 | 0.5620 | 0.4979 | 0.7056 |
125
+ | No log | 1.4653 | 148 | 0.4714 | 0.5272 | 0.4714 | 0.6866 |
126
+ | No log | 1.4851 | 150 | 0.4736 | 0.5024 | 0.4736 | 0.6882 |
127
+ | No log | 1.5050 | 152 | 0.4853 | 0.4033 | 0.4853 | 0.6966 |
128
+ | No log | 1.5248 | 154 | 0.5246 | 0.3924 | 0.5246 | 0.7243 |
129
+ | No log | 1.5446 | 156 | 0.6435 | 0.4319 | 0.6435 | 0.8022 |
130
+ | No log | 1.5644 | 158 | 0.6377 | 0.4026 | 0.6377 | 0.7986 |
131
+ | No log | 1.5842 | 160 | 0.5481 | 0.4043 | 0.5481 | 0.7403 |
132
+ | No log | 1.6040 | 162 | 0.5065 | 0.3718 | 0.5065 | 0.7117 |
133
+ | No log | 1.6238 | 164 | 0.5086 | 0.4257 | 0.5086 | 0.7132 |
134
+ | No log | 1.6436 | 166 | 0.5509 | 0.4817 | 0.5509 | 0.7422 |
135
+ | No log | 1.6634 | 168 | 0.6131 | 0.5389 | 0.6131 | 0.7830 |
136
+ | No log | 1.6832 | 170 | 0.8426 | 0.4808 | 0.8426 | 0.9179 |
137
+ | No log | 1.7030 | 172 | 0.9014 | 0.4595 | 0.9014 | 0.9494 |
138
+ | No log | 1.7228 | 174 | 0.7099 | 0.4987 | 0.7099 | 0.8426 |
139
+ | No log | 1.7426 | 176 | 0.6223 | 0.5521 | 0.6223 | 0.7889 |
140
+ | No log | 1.7624 | 178 | 0.6193 | 0.5599 | 0.6193 | 0.7869 |
141
+ | No log | 1.7822 | 180 | 0.7295 | 0.4730 | 0.7295 | 0.8541 |
142
+ | No log | 1.8020 | 182 | 0.7522 | 0.4658 | 0.7522 | 0.8673 |
143
+ | No log | 1.8218 | 184 | 0.6219 | 0.5308 | 0.6219 | 0.7886 |
144
+ | No log | 1.8416 | 186 | 0.5378 | 0.5054 | 0.5378 | 0.7334 |
145
+ | No log | 1.8614 | 188 | 0.5580 | 0.5189 | 0.5580 | 0.7470 |
146
+ | No log | 1.8812 | 190 | 0.5325 | 0.4654 | 0.5325 | 0.7297 |
147
+ | No log | 1.9010 | 192 | 0.5347 | 0.4226 | 0.5347 | 0.7312 |
148
+ | No log | 1.9208 | 194 | 0.5795 | 0.4631 | 0.5795 | 0.7612 |
149
+ | No log | 1.9406 | 196 | 0.6169 | 0.4687 | 0.6169 | 0.7854 |
150
+ | No log | 1.9604 | 198 | 0.6850 | 0.4003 | 0.6850 | 0.8276 |
151
+ | No log | 1.9802 | 200 | 0.7071 | 0.4247 | 0.7071 | 0.8409 |
152
+ | No log | 2.0 | 202 | 0.8268 | 0.4295 | 0.8268 | 0.9093 |
153
+ | No log | 2.0198 | 204 | 0.8451 | 0.4717 | 0.8451 | 0.9193 |
154
+ | No log | 2.0396 | 206 | 0.7749 | 0.5098 | 0.7749 | 0.8803 |
155
+ | No log | 2.0594 | 208 | 0.6879 | 0.5427 | 0.6879 | 0.8294 |
156
+ | No log | 2.0792 | 210 | 0.6468 | 0.5373 | 0.6468 | 0.8042 |
157
+ | No log | 2.0990 | 212 | 0.5832 | 0.5442 | 0.5832 | 0.7637 |
158
+ | No log | 2.1188 | 214 | 0.5547 | 0.5476 | 0.5547 | 0.7448 |
159
+ | No log | 2.1386 | 216 | 0.6863 | 0.4893 | 0.6863 | 0.8284 |
160
+ | No log | 2.1584 | 218 | 0.7505 | 0.4176 | 0.7505 | 0.8663 |
161
+ | No log | 2.1782 | 220 | 0.6569 | 0.4044 | 0.6569 | 0.8105 |
162
+ | No log | 2.1980 | 222 | 0.5207 | 0.5029 | 0.5207 | 0.7216 |
163
+ | No log | 2.2178 | 224 | 0.4929 | 0.5183 | 0.4929 | 0.7020 |
164
+ | No log | 2.2376 | 226 | 0.5028 | 0.5392 | 0.5028 | 0.7091 |
165
+ | No log | 2.2574 | 228 | 0.6333 | 0.4736 | 0.6333 | 0.7958 |
166
+ | No log | 2.2772 | 230 | 0.7494 | 0.4754 | 0.7494 | 0.8657 |
167
+ | No log | 2.2970 | 232 | 0.6745 | 0.5546 | 0.6745 | 0.8213 |
168
+ | No log | 2.3168 | 234 | 0.5450 | 0.5775 | 0.5450 | 0.7382 |
169
+ | No log | 2.3366 | 236 | 0.5198 | 0.5461 | 0.5198 | 0.7210 |
170
+ | No log | 2.3564 | 238 | 0.4995 | 0.5423 | 0.4995 | 0.7068 |
171
+ | No log | 2.3762 | 240 | 0.4971 | 0.5364 | 0.4971 | 0.7051 |
172
+ | No log | 2.3960 | 242 | 0.4948 | 0.5284 | 0.4948 | 0.7034 |
173
+ | No log | 2.4158 | 244 | 0.5098 | 0.4742 | 0.5098 | 0.7140 |
174
+ | No log | 2.4356 | 246 | 0.4943 | 0.4964 | 0.4943 | 0.7031 |
175
+ | No log | 2.4554 | 248 | 0.4904 | 0.4865 | 0.4904 | 0.7003 |
176
+ | No log | 2.4752 | 250 | 0.5070 | 0.4865 | 0.5070 | 0.7120 |
177
+ | No log | 2.4950 | 252 | 0.6019 | 0.5224 | 0.6019 | 0.7758 |
178
+ | No log | 2.5149 | 254 | 0.7326 | 0.4532 | 0.7326 | 0.8559 |
179
+ | No log | 2.5347 | 256 | 0.8288 | 0.4272 | 0.8288 | 0.9104 |
180
+ | No log | 2.5545 | 258 | 0.7715 | 0.4900 | 0.7715 | 0.8783 |
181
+ | No log | 2.5743 | 260 | 0.6758 | 0.5568 | 0.6758 | 0.8221 |
182
+ | No log | 2.5941 | 262 | 0.6080 | 0.6088 | 0.6080 | 0.7798 |
183
+ | No log | 2.6139 | 264 | 0.6009 | 0.6006 | 0.6009 | 0.7752 |
184
+ | No log | 2.6337 | 266 | 0.5798 | 0.6130 | 0.5798 | 0.7615 |
185
+ | No log | 2.6535 | 268 | 0.5962 | 0.5761 | 0.5962 | 0.7721 |
186
+ | No log | 2.6733 | 270 | 0.6342 | 0.4635 | 0.6342 | 0.7964 |
187
+ | No log | 2.6931 | 272 | 0.6725 | 0.2463 | 0.6725 | 0.8201 |
188
+ | No log | 2.7129 | 274 | 0.6584 | 0.2377 | 0.6584 | 0.8114 |
189
+ | No log | 2.7327 | 276 | 0.6294 | 0.2870 | 0.6294 | 0.7934 |
190
+ | No log | 2.7525 | 278 | 0.6059 | 0.2870 | 0.6059 | 0.7784 |
191
+ | No log | 2.7723 | 280 | 0.5971 | 0.2957 | 0.5971 | 0.7727 |
192
+ | No log | 2.7921 | 282 | 0.6375 | 0.2710 | 0.6375 | 0.7984 |
193
+ | No log | 2.8119 | 284 | 0.6941 | 0.3000 | 0.6941 | 0.8331 |
194
+ | No log | 2.8317 | 286 | 0.6663 | 0.4606 | 0.6663 | 0.8162 |
195
+ | No log | 2.8515 | 288 | 0.5939 | 0.5822 | 0.5939 | 0.7707 |
196
+ | No log | 2.8713 | 290 | 0.6253 | 0.5926 | 0.6253 | 0.7907 |
197
+ | No log | 2.8911 | 292 | 0.5989 | 0.5948 | 0.5989 | 0.7739 |
198
+ | No log | 2.9109 | 294 | 0.5802 | 0.6007 | 0.5802 | 0.7617 |
199
+ | No log | 2.9307 | 296 | 0.6774 | 0.5268 | 0.6774 | 0.8231 |
200
+ | No log | 2.9505 | 298 | 0.6966 | 0.4976 | 0.6966 | 0.8346 |
201
+ | No log | 2.9703 | 300 | 0.6021 | 0.5328 | 0.6021 | 0.7760 |
202
+ | No log | 2.9901 | 302 | 0.4904 | 0.5367 | 0.4904 | 0.7003 |
203
+ | No log | 3.0099 | 304 | 0.4959 | 0.4902 | 0.4959 | 0.7042 |
204
+ | No log | 3.0297 | 306 | 0.4931 | 0.4585 | 0.4931 | 0.7022 |
205
+ | No log | 3.0495 | 308 | 0.4977 | 0.4957 | 0.4977 | 0.7055 |
206
+ | No log | 3.0693 | 310 | 0.5766 | 0.5460 | 0.5766 | 0.7593 |
207
+ | No log | 3.0891 | 312 | 0.6484 | 0.5177 | 0.6484 | 0.8052 |
208
+ | No log | 3.1089 | 314 | 0.6354 | 0.5230 | 0.6354 | 0.7971 |
209
+ | No log | 3.1287 | 316 | 0.5811 | 0.5177 | 0.5811 | 0.7623 |
210
+ | No log | 3.1485 | 318 | 0.5403 | 0.5195 | 0.5403 | 0.7351 |
211
+ | No log | 3.1683 | 320 | 0.5572 | 0.5243 | 0.5572 | 0.7465 |
212
+ | No log | 3.1881 | 322 | 0.5900 | 0.5432 | 0.5900 | 0.7681 |
213
+ | No log | 3.2079 | 324 | 0.6617 | 0.4795 | 0.6617 | 0.8134 |
214
+ | No log | 3.2277 | 326 | 0.6676 | 0.4534 | 0.6676 | 0.8171 |
215
+ | No log | 3.2475 | 328 | 0.6030 | 0.4787 | 0.6030 | 0.7766 |
216
+ | No log | 3.2673 | 330 | 0.5358 | 0.5630 | 0.5358 | 0.7320 |
217
+ | No log | 3.2871 | 332 | 0.4971 | 0.5573 | 0.4971 | 0.7050 |
218
+ | No log | 3.3069 | 334 | 0.5111 | 0.5993 | 0.5111 | 0.7149 |
219
+ | No log | 3.3267 | 336 | 0.5067 | 0.6072 | 0.5067 | 0.7118 |
220
+ | No log | 3.3465 | 338 | 0.5683 | 0.5969 | 0.5683 | 0.7539 |
221
+ | No log | 3.3663 | 340 | 0.7460 | 0.4823 | 0.7460 | 0.8637 |
222
+ | No log | 3.3861 | 342 | 0.8828 | 0.4145 | 0.8828 | 0.9396 |
223
+ | No log | 3.4059 | 344 | 0.8907 | 0.4153 | 0.8907 | 0.9438 |
224
+ | No log | 3.4257 | 346 | 0.7572 | 0.4594 | 0.7572 | 0.8702 |
225
+ | No log | 3.4455 | 348 | 0.6012 | 0.5598 | 0.6012 | 0.7754 |
226
+ | No log | 3.4653 | 350 | 0.5851 | 0.5488 | 0.5851 | 0.7649 |
227
+ | No log | 3.4851 | 352 | 0.5497 | 0.5623 | 0.5497 | 0.7414 |
228
+ | No log | 3.5050 | 354 | 0.5605 | 0.5773 | 0.5605 | 0.7487 |
229
+ | No log | 3.5248 | 356 | 0.6674 | 0.5558 | 0.6674 | 0.8170 |
230
+ | No log | 3.5446 | 358 | 0.7834 | 0.4663 | 0.7834 | 0.8851 |
231
+ | No log | 3.5644 | 360 | 0.7836 | 0.4805 | 0.7836 | 0.8852 |
232
+ | No log | 3.5842 | 362 | 0.7023 | 0.5345 | 0.7023 | 0.8380 |
233
+ | No log | 3.6040 | 364 | 0.5420 | 0.6189 | 0.5420 | 0.7362 |
234
+ | No log | 3.6238 | 366 | 0.5198 | 0.6031 | 0.5198 | 0.7210 |
235
+ | No log | 3.6436 | 368 | 0.5154 | 0.5974 | 0.5154 | 0.7179 |
236
+ | No log | 3.6634 | 370 | 0.5909 | 0.5966 | 0.5909 | 0.7687 |
237
+ | No log | 3.6832 | 372 | 0.7003 | 0.4983 | 0.7003 | 0.8368 |
238
+ | No log | 3.7030 | 374 | 0.6886 | 0.4809 | 0.6886 | 0.8298 |
239
+ | No log | 3.7228 | 376 | 0.5734 | 0.5264 | 0.5734 | 0.7572 |
240
+ | No log | 3.7426 | 378 | 0.5256 | 0.5879 | 0.5256 | 0.7250 |
241
+ | No log | 3.7624 | 380 | 0.5182 | 0.5897 | 0.5182 | 0.7198 |
242
+ | No log | 3.7822 | 382 | 0.5598 | 0.5668 | 0.5598 | 0.7482 |
243
+ | No log | 3.8020 | 384 | 0.6724 | 0.5205 | 0.6724 | 0.8200 |
244
+ | No log | 3.8218 | 386 | 0.7733 | 0.4747 | 0.7733 | 0.8794 |
245
+ | No log | 3.8416 | 388 | 0.8037 | 0.4765 | 0.8037 | 0.8965 |
246
+ | No log | 3.8614 | 390 | 0.7182 | 0.4836 | 0.7182 | 0.8475 |
247
+ | No log | 3.8812 | 392 | 0.6395 | 0.5337 | 0.6395 | 0.7997 |
248
+ | No log | 3.9010 | 394 | 0.5742 | 0.5832 | 0.5742 | 0.7577 |
249
+ | No log | 3.9208 | 396 | 0.5875 | 0.5721 | 0.5875 | 0.7665 |
250
+ | No log | 3.9406 | 398 | 0.6721 | 0.5291 | 0.6721 | 0.8198 |
251
+ | No log | 3.9604 | 400 | 0.7364 | 0.5316 | 0.7364 | 0.8581 |
252
+ | No log | 3.9802 | 402 | 0.7351 | 0.5588 | 0.7351 | 0.8574 |
253
+ | No log | 4.0 | 404 | 0.7558 | 0.5398 | 0.7558 | 0.8694 |
254
+ | No log | 4.0198 | 406 | 0.7428 | 0.5288 | 0.7428 | 0.8619 |
255
+ | No log | 4.0396 | 408 | 0.6595 | 0.5607 | 0.6595 | 0.8121 |
256
+ | No log | 4.0594 | 410 | 0.5730 | 0.5518 | 0.5730 | 0.7570 |
257
+ | No log | 4.0792 | 412 | 0.5627 | 0.5360 | 0.5627 | 0.7501 |
258
+ | No log | 4.0990 | 414 | 0.6115 | 0.5104 | 0.6115 | 0.7820 |
259
+ | No log | 4.1188 | 416 | 0.7058 | 0.4445 | 0.7058 | 0.8401 |
260
+ | No log | 4.1386 | 418 | 0.7630 | 0.4347 | 0.7630 | 0.8735 |
261
+ | No log | 4.1584 | 420 | 0.7869 | 0.4618 | 0.7869 | 0.8871 |
262
+ | No log | 4.1782 | 422 | 0.6505 | 0.5437 | 0.6505 | 0.8065 |
263
+ | No log | 4.1980 | 424 | 0.5968 | 0.5814 | 0.5968 | 0.7726 |
264
+ | No log | 4.2178 | 426 | 0.6656 | 0.5341 | 0.6656 | 0.8158 |
265
+ | No log | 4.2376 | 428 | 0.8111 | 0.4616 | 0.8111 | 0.9006 |
266
+ | No log | 4.2574 | 430 | 0.7756 | 0.4812 | 0.7756 | 0.8807 |
267
+ | No log | 4.2772 | 432 | 0.5990 | 0.5883 | 0.5990 | 0.7739 |
268
+ | No log | 4.2970 | 434 | 0.5469 | 0.5647 | 0.5469 | 0.7395 |
269
+ | No log | 4.3168 | 436 | 0.5644 | 0.5139 | 0.5644 | 0.7513 |
270
+ | No log | 4.3366 | 438 | 0.5172 | 0.5424 | 0.5172 | 0.7192 |
271
+ | No log | 4.3564 | 440 | 0.5641 | 0.5331 | 0.5641 | 0.7510 |
272
+ | No log | 4.3762 | 442 | 0.8089 | 0.4315 | 0.8089 | 0.8994 |
273
+ | No log | 4.3960 | 444 | 0.8810 | 0.4041 | 0.8810 | 0.9386 |
274
+ | No log | 4.4158 | 446 | 0.7841 | 0.4075 | 0.7841 | 0.8855 |
275
+ | No log | 4.4356 | 448 | 0.6109 | 0.5324 | 0.6109 | 0.7816 |
276
+ | No log | 4.4554 | 450 | 0.5221 | 0.5467 | 0.5221 | 0.7226 |
277
+ | No log | 4.4752 | 452 | 0.5011 | 0.5492 | 0.5011 | 0.7079 |
278
+ | No log | 4.4950 | 454 | 0.4949 | 0.5866 | 0.4949 | 0.7035 |
279
+ | No log | 4.5149 | 456 | 0.5824 | 0.5652 | 0.5824 | 0.7632 |
280
+ | No log | 4.5347 | 458 | 0.7049 | 0.5323 | 0.7049 | 0.8396 |
281
+ | No log | 4.5545 | 460 | 0.7355 | 0.5253 | 0.7355 | 0.8576 |
282
+ | No log | 4.5743 | 462 | 0.6313 | 0.5639 | 0.6313 | 0.7945 |
283
+ | No log | 4.5941 | 464 | 0.5840 | 0.5911 | 0.5840 | 0.7642 |
284
+ | No log | 4.6139 | 466 | 0.5819 | 0.5897 | 0.5819 | 0.7628 |
285
+ | No log | 4.6337 | 468 | 0.6567 | 0.5372 | 0.6567 | 0.8104 |
286
+ | No log | 4.6535 | 470 | 0.7490 | 0.4798 | 0.7490 | 0.8654 |
287
+ | No log | 4.6733 | 472 | 0.8477 | 0.4355 | 0.8477 | 0.9207 |
288
+ | No log | 4.6931 | 474 | 0.7648 | 0.4907 | 0.7648 | 0.8745 |
289
+ | No log | 4.7129 | 476 | 0.6175 | 0.5585 | 0.6175 | 0.7858 |
290
+ | No log | 4.7327 | 478 | 0.5142 | 0.6049 | 0.5142 | 0.7171 |
291
+ | No log | 4.7525 | 480 | 0.5148 | 0.5908 | 0.5148 | 0.7175 |
292
+ | No log | 4.7723 | 482 | 0.5935 | 0.5651 | 0.5935 | 0.7704 |
293
+ | No log | 4.7921 | 484 | 0.5711 | 0.5678 | 0.5711 | 0.7557 |
294
+ | No log | 4.8119 | 486 | 0.6065 | 0.5353 | 0.6065 | 0.7788 |
295
+ | No log | 4.8317 | 488 | 0.5842 | 0.5413 | 0.5842 | 0.7643 |
296
+ | No log | 4.8515 | 490 | 0.6870 | 0.5324 | 0.6870 | 0.8289 |
297
+ | No log | 4.8713 | 492 | 0.6627 | 0.5408 | 0.6627 | 0.8140 |
298
+ | No log | 4.8911 | 494 | 0.6137 | 0.5463 | 0.6137 | 0.7834 |
299
+ | No log | 4.9109 | 496 | 0.5093 | 0.5944 | 0.5093 | 0.7137 |
300
+ | No log | 4.9307 | 498 | 0.4654 | 0.6641 | 0.4654 | 0.6822 |
301
+ | 0.5253 | 4.9505 | 500 | 0.4715 | 0.6245 | 0.4715 | 0.6867 |
302
+ | 0.5253 | 4.9703 | 502 | 0.5949 | 0.5558 | 0.5949 | 0.7713 |
303
+ | 0.5253 | 4.9901 | 504 | 0.7813 | 0.4918 | 0.7813 | 0.8839 |
304
+ | 0.5253 | 5.0099 | 506 | 0.7283 | 0.4850 | 0.7283 | 0.8534 |
305
+ | 0.5253 | 5.0297 | 508 | 0.5528 | 0.5614 | 0.5528 | 0.7435 |
306
+ | 0.5253 | 5.0495 | 510 | 0.4960 | 0.5793 | 0.4960 | 0.7043 |
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:a3a28f2d23b24b615fb5c8000897dd58476e31a2d8d1e90bd542e9c5ca4be642
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:491ad072f1d563c23bfda816a21bd54b8b305775dfc5106422f5462225b7ee43
3
+ size 5240