Hugging Face's logo

MayBashendy
/
ArabicNewSplits7_B_usingWellWrittenEssays_FineTuningAraBERT_run2_AugV5_k14_task5_organization

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
 Community
MayBashendy commited on
Commit
d70b7bd
·
verified ·
1 Parent(s): 0597fae

Training in progress, step 500

Browse files
Files changed (4) hide show
  1. README.md +342 -0
  2. config.json +32 -0
  3. model.safetensors +3 -0
  4. training_args.bin +3 -0
README.md ADDED
@@ -0,0 +1,342 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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_usingWellWrittenEssays_FineTuningAraBERT_run2_AugV5_k13_task5_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_usingWellWrittenEssays_FineTuningAraBERT_run2_AugV5_k13_task5_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: 1.1021
19
+ - Qwk: 0.0894
20
+ - Mse: 1.1021
21
+ - Rmse: 1.0498
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 | 0.0317 | 2 | 3.7836 | -0.0294 | 3.7836 | 1.9452 |
53
+ | No log | 0.0635 | 4 | 2.0931 | 0.0260 | 2.0931 | 1.4468 |
54
+ | No log | 0.0952 | 6 | 1.4445 | 0.0 | 1.4445 | 1.2019 |
55
+ | No log | 0.1270 | 8 | 1.2618 | 0.1454 | 1.2618 | 1.1233 |
56
+ | No log | 0.1587 | 10 | 1.0890 | 0.2042 | 1.0890 | 1.0435 |
57
+ | No log | 0.1905 | 12 | 1.6222 | -0.0597 | 1.6222 | 1.2736 |
58
+ | No log | 0.2222 | 14 | 1.4163 | 0.0542 | 1.4163 | 1.1901 |
59
+ | No log | 0.2540 | 16 | 1.0606 | 0.2441 | 1.0606 | 1.0299 |
60
+ | No log | 0.2857 | 18 | 1.0387 | 0.1722 | 1.0387 | 1.0192 |
61
+ | No log | 0.3175 | 20 | 1.1429 | 0.1500 | 1.1429 | 1.0691 |
62
+ | No log | 0.3492 | 22 | 1.0082 | 0.3021 | 1.0082 | 1.0041 |
63
+ | No log | 0.3810 | 24 | 0.9963 | 0.2887 | 0.9963 | 0.9981 |
64
+ | No log | 0.4127 | 26 | 1.0772 | 0.3344 | 1.0772 | 1.0379 |
65
+ | No log | 0.4444 | 28 | 1.0221 | 0.3367 | 1.0221 | 1.0110 |
66
+ | No log | 0.4762 | 30 | 1.0046 | 0.3263 | 1.0046 | 1.0023 |
67
+ | No log | 0.5079 | 32 | 1.0078 | 0.3029 | 1.0078 | 1.0039 |
68
+ | No log | 0.5397 | 34 | 1.2759 | 0.2496 | 1.2759 | 1.1296 |
69
+ | No log | 0.5714 | 36 | 1.3230 | 0.1773 | 1.3230 | 1.1502 |
70
+ | No log | 0.6032 | 38 | 1.2145 | 0.2376 | 1.2145 | 1.1021 |
71
+ | No log | 0.6349 | 40 | 1.0400 | 0.2810 | 1.0400 | 1.0198 |
72
+ | No log | 0.6667 | 42 | 1.0035 | 0.3642 | 1.0035 | 1.0018 |
73
+ | No log | 0.6984 | 44 | 0.9997 | 0.3229 | 0.9997 | 0.9999 |
74
+ | No log | 0.7302 | 46 | 1.0813 | 0.2071 | 1.0813 | 1.0399 |
75
+ | No log | 0.7619 | 48 | 1.2179 | 0.2177 | 1.2179 | 1.1036 |
76
+ | No log | 0.7937 | 50 | 1.4727 | 0.1751 | 1.4727 | 1.2135 |
77
+ | No log | 0.8254 | 52 | 1.8278 | 0.0422 | 1.8278 | 1.3520 |
78
+ | No log | 0.8571 | 54 | 2.0374 | -0.0342 | 2.0374 | 1.4274 |
79
+ | No log | 0.8889 | 56 | 1.9071 | 0.1098 | 1.9071 | 1.3810 |
80
+ | No log | 0.9206 | 58 | 1.3883 | 0.1601 | 1.3883 | 1.1782 |
81
+ | No log | 0.9524 | 60 | 1.0284 | 0.3296 | 1.0284 | 1.0141 |
82
+ | No log | 0.9841 | 62 | 1.0085 | 0.3689 | 1.0085 | 1.0042 |
83
+ | No log | 1.0159 | 64 | 1.0328 | 0.3289 | 1.0328 | 1.0163 |
84
+ | No log | 1.0476 | 66 | 1.1956 | 0.1410 | 1.1956 | 1.0934 |
85
+ | No log | 1.0794 | 68 | 1.5840 | 0.1548 | 1.5840 | 1.2586 |
86
+ | No log | 1.1111 | 70 | 1.7388 | 0.1916 | 1.7388 | 1.3186 |
87
+ | No log | 1.1429 | 72 | 1.5816 | 0.1575 | 1.5816 | 1.2576 |
88
+ | No log | 1.1746 | 74 | 1.2540 | 0.1322 | 1.2540 | 1.1198 |
89
+ | No log | 1.2063 | 76 | 1.0518 | 0.3692 | 1.0518 | 1.0256 |
90
+ | No log | 1.2381 | 78 | 0.9433 | 0.4974 | 0.9433 | 0.9713 |
91
+ | No log | 1.2698 | 80 | 0.9644 | 0.4491 | 0.9644 | 0.9820 |
92
+ | No log | 1.3016 | 82 | 1.0735 | 0.3025 | 1.0735 | 1.0361 |
93
+ | No log | 1.3333 | 84 | 1.3740 | 0.1561 | 1.3740 | 1.1722 |
94
+ | No log | 1.3651 | 86 | 1.4854 | 0.1328 | 1.4854 | 1.2188 |
95
+ | No log | 1.3968 | 88 | 1.3281 | 0.1872 | 1.3281 | 1.1524 |
96
+ | No log | 1.4286 | 90 | 1.1174 | 0.2528 | 1.1174 | 1.0571 |
97
+ | No log | 1.4603 | 92 | 0.9618 | 0.2273 | 0.9618 | 0.9807 |
98
+ | No log | 1.4921 | 94 | 0.8744 | 0.4327 | 0.8744 | 0.9351 |
99
+ | No log | 1.5238 | 96 | 0.8684 | 0.4223 | 0.8684 | 0.9319 |
100
+ | No log | 1.5556 | 98 | 0.8980 | 0.4198 | 0.8980 | 0.9476 |
101
+ | No log | 1.5873 | 100 | 1.0580 | 0.1488 | 1.0580 | 1.0286 |
102
+ | No log | 1.6190 | 102 | 1.4153 | 0.0178 | 1.4153 | 1.1897 |
103
+ | No log | 1.6508 | 104 | 1.7332 | -0.1702 | 1.7332 | 1.3165 |
104
+ | No log | 1.6825 | 106 | 1.5119 | -0.0339 | 1.5119 | 1.2296 |
105
+ | No log | 1.7143 | 108 | 1.2056 | 0.0111 | 1.2056 | 1.0980 |
106
+ | No log | 1.7460 | 110 | 1.1757 | 0.1361 | 1.1757 | 1.0843 |
107
+ | No log | 1.7778 | 112 | 1.3518 | 0.1233 | 1.3518 | 1.1627 |
108
+ | No log | 1.8095 | 114 | 1.1353 | 0.1389 | 1.1353 | 1.0655 |
109
+ | No log | 1.8413 | 116 | 1.0315 | 0.2841 | 1.0315 | 1.0156 |
110
+ | No log | 1.8730 | 118 | 1.0623 | 0.1493 | 1.0623 | 1.0307 |
111
+ | No log | 1.9048 | 120 | 1.0160 | 0.2365 | 1.0160 | 1.0080 |
112
+ | No log | 1.9365 | 122 | 1.0214 | 0.2274 | 1.0214 | 1.0106 |
113
+ | No log | 1.9683 | 124 | 1.0722 | 0.2225 | 1.0722 | 1.0355 |
114
+ | No log | 2.0 | 126 | 1.0556 | 0.2541 | 1.0556 | 1.0274 |
115
+ | No log | 2.0317 | 128 | 1.0433 | 0.2821 | 1.0433 | 1.0214 |
116
+ | No log | 2.0635 | 130 | 1.0952 | 0.2424 | 1.0952 | 1.0465 |
117
+ | No log | 2.0952 | 132 | 1.1222 | 0.2051 | 1.1222 | 1.0593 |
118
+ | No log | 2.1270 | 134 | 1.1462 | 0.1654 | 1.1462 | 1.0706 |
119
+ | No log | 2.1587 | 136 | 1.2428 | 0.1814 | 1.2428 | 1.1148 |
120
+ | No log | 2.1905 | 138 | 1.2785 | 0.1814 | 1.2785 | 1.1307 |
121
+ | No log | 2.2222 | 140 | 1.2874 | 0.2126 | 1.2874 | 1.1346 |
122
+ | No log | 2.2540 | 142 | 1.2224 | 0.1428 | 1.2224 | 1.1056 |
123
+ | No log | 2.2857 | 144 | 1.0797 | 0.1619 | 1.0797 | 1.0391 |
124
+ | No log | 2.3175 | 146 | 1.0169 | 0.2000 | 1.0169 | 1.0084 |
125
+ | No log | 2.3492 | 148 | 1.0161 | 0.2386 | 1.0161 | 1.0080 |
126
+ | No log | 2.3810 | 150 | 0.9615 | 0.2179 | 0.9615 | 0.9806 |
127
+ | No log | 2.4127 | 152 | 1.0077 | 0.2772 | 1.0077 | 1.0038 |
128
+ | No log | 2.4444 | 154 | 1.1146 | 0.1197 | 1.1146 | 1.0557 |
129
+ | No log | 2.4762 | 156 | 1.0976 | 0.1863 | 1.0976 | 1.0477 |
130
+ | No log | 2.5079 | 158 | 1.0558 | 0.2386 | 1.0558 | 1.0275 |
131
+ | No log | 2.5397 | 160 | 1.0530 | 0.2907 | 1.0530 | 1.0262 |
132
+ | No log | 2.5714 | 162 | 1.2006 | 0.1689 | 1.2006 | 1.0957 |
133
+ | No log | 2.6032 | 164 | 1.2620 | 0.2070 | 1.2620 | 1.1234 |
134
+ | No log | 2.6349 | 166 | 1.1525 | 0.2037 | 1.1525 | 1.0736 |
135
+ | No log | 2.6667 | 168 | 1.2274 | 0.2037 | 1.2274 | 1.1079 |
136
+ | No log | 2.6984 | 170 | 1.4748 | 0.2123 | 1.4748 | 1.2144 |
137
+ | No log | 2.7302 | 172 | 1.4999 | 0.2223 | 1.4999 | 1.2247 |
138
+ | No log | 2.7619 | 174 | 1.3165 | 0.0922 | 1.3165 | 1.1474 |
139
+ | No log | 2.7937 | 176 | 1.1712 | 0.1343 | 1.1712 | 1.0822 |
140
+ | No log | 2.8254 | 178 | 1.1712 | 0.1053 | 1.1712 | 1.0822 |
141
+ | No log | 2.8571 | 180 | 1.2586 | 0.1370 | 1.2586 | 1.1219 |
142
+ | No log | 2.8889 | 182 | 1.4104 | 0.1601 | 1.4104 | 1.1876 |
143
+ | No log | 2.9206 | 184 | 1.3963 | 0.1892 | 1.3963 | 1.1817 |
144
+ | No log | 2.9524 | 186 | 1.2030 | 0.1255 | 1.2030 | 1.0968 |
145
+ | No log | 2.9841 | 188 | 0.9876 | 0.2384 | 0.9876 | 0.9938 |
146
+ | No log | 3.0159 | 190 | 0.9438 | 0.3702 | 0.9438 | 0.9715 |
147
+ | No log | 3.0476 | 192 | 0.9866 | 0.3491 | 0.9866 | 0.9933 |
148
+ | No log | 3.0794 | 194 | 1.1438 | 0.2925 | 1.1438 | 1.0695 |
149
+ | No log | 3.1111 | 196 | 1.5081 | 0.1683 | 1.5081 | 1.2281 |
150
+ | No log | 3.1429 | 198 | 1.6705 | 0.1607 | 1.6705 | 1.2925 |
151
+ | No log | 3.1746 | 200 | 1.7458 | 0.1203 | 1.7458 | 1.3213 |
152
+ | No log | 3.2063 | 202 | 1.7119 | 0.1203 | 1.7119 | 1.3084 |
153
+ | No log | 3.2381 | 204 | 1.5450 | 0.1394 | 1.5450 | 1.2430 |
154
+ | No log | 3.2698 | 206 | 1.3182 | 0.1255 | 1.3182 | 1.1481 |
155
+ | No log | 3.3016 | 208 | 1.2300 | 0.1397 | 1.2300 | 1.1090 |
156
+ | No log | 3.3333 | 210 | 1.2363 | 0.1370 | 1.2363 | 1.1119 |
157
+ | No log | 3.3651 | 212 | 1.3195 | 0.1744 | 1.3195 | 1.1487 |
158
+ | No log | 3.3968 | 214 | 1.4061 | 0.2270 | 1.4061 | 1.1858 |
159
+ | No log | 3.4286 | 216 | 1.3898 | 0.2270 | 1.3898 | 1.1789 |
160
+ | No log | 3.4603 | 218 | 1.3073 | 0.2138 | 1.3073 | 1.1434 |
161
+ | No log | 3.4921 | 220 | 1.1297 | 0.1795 | 1.1297 | 1.0629 |
162
+ | No log | 3.5238 | 222 | 1.0705 | 0.0841 | 1.0705 | 1.0346 |
163
+ | No log | 3.5556 | 224 | 1.1323 | 0.2417 | 1.1323 | 1.0641 |
164
+ | No log | 3.5873 | 226 | 1.3555 | 0.2317 | 1.3555 | 1.1642 |
165
+ | No log | 3.6190 | 228 | 1.4655 | 0.2056 | 1.4655 | 1.2106 |
166
+ | No log | 3.6508 | 230 | 1.3748 | 0.2482 | 1.3748 | 1.1725 |
167
+ | No log | 3.6825 | 232 | 1.1482 | 0.1795 | 1.1482 | 1.0715 |
168
+ | No log | 3.7143 | 234 | 1.0270 | 0.3257 | 1.0270 | 1.0134 |
169
+ | No log | 3.7460 | 236 | 0.9578 | 0.4595 | 0.9578 | 0.9787 |
170
+ | No log | 3.7778 | 238 | 0.9719 | 0.4461 | 0.9719 | 0.9859 |
171
+ | No log | 3.8095 | 240 | 1.0338 | 0.2461 | 1.0338 | 1.0167 |
172
+ | No log | 3.8413 | 242 | 1.1492 | 0.2159 | 1.1492 | 1.0720 |
173
+ | No log | 3.8730 | 244 | 1.3336 | 0.2123 | 1.3336 | 1.1548 |
174
+ | No log | 3.9048 | 246 | 1.3915 | 0.2391 | 1.3915 | 1.1796 |
175
+ | No log | 3.9365 | 248 | 1.3731 | 0.2391 | 1.3731 | 1.1718 |
176
+ | No log | 3.9683 | 250 | 1.3089 | 0.2391 | 1.3089 | 1.1441 |
177
+ | No log | 4.0 | 252 | 1.2793 | 0.1769 | 1.2793 | 1.1311 |
178
+ | No log | 4.0317 | 254 | 1.2837 | 0.1744 | 1.2837 | 1.1330 |
179
+ | No log | 4.0635 | 256 | 1.2972 | 0.1744 | 1.2972 | 1.1390 |
180
+ | No log | 4.0952 | 258 | 1.3417 | 0.2522 | 1.3417 | 1.1583 |
181
+ | No log | 4.1270 | 260 | 1.3539 | 0.2482 | 1.3539 | 1.1636 |
182
+ | No log | 4.1587 | 262 | 1.2634 | 0.2522 | 1.2634 | 1.1240 |
183
+ | No log | 4.1905 | 264 | 1.1646 | 0.2372 | 1.1646 | 1.0792 |
184
+ | No log | 4.2222 | 266 | 1.1330 | 0.2227 | 1.1330 | 1.0644 |
185
+ | No log | 4.2540 | 268 | 1.1157 | 0.1838 | 1.1157 | 1.0563 |
186
+ | No log | 4.2857 | 270 | 1.1830 | 0.2372 | 1.1830 | 1.0876 |
187
+ | No log | 4.3175 | 272 | 1.2530 | 0.2709 | 1.2530 | 1.1194 |
188
+ | No log | 4.3492 | 274 | 1.1864 | 0.2065 | 1.1864 | 1.0892 |
189
+ | No log | 4.3810 | 276 | 1.1789 | 0.2372 | 1.1789 | 1.0858 |
190
+ | No log | 4.4127 | 278 | 1.2710 | 0.2437 | 1.2710 | 1.1274 |
191
+ | No log | 4.4444 | 280 | 1.3047 | 0.2363 | 1.3047 | 1.1422 |
192
+ | No log | 4.4762 | 282 | 1.2442 | 0.2457 | 1.2442 | 1.1154 |
193
+ | No log | 4.5079 | 284 | 1.2081 | 0.2363 | 1.2081 | 1.0992 |
194
+ | No log | 4.5397 | 286 | 1.2213 | 0.2149 | 1.2213 | 1.1051 |
195
+ | No log | 4.5714 | 288 | 1.3224 | 0.2015 | 1.3224 | 1.1500 |
196
+ | No log | 4.6032 | 290 | 1.2979 | 0.2065 | 1.2979 | 1.1393 |
197
+ | No log | 4.6349 | 292 | 1.2452 | 0.1700 | 1.2452 | 1.1159 |
198
+ | No log | 4.6667 | 294 | 1.2254 | 0.2149 | 1.2254 | 1.1070 |
199
+ | No log | 4.6984 | 296 | 1.2734 | 0.2363 | 1.2734 | 1.1284 |
200
+ | No log | 4.7302 | 298 | 1.2491 | 0.2363 | 1.2491 | 1.1176 |
201
+ | No log | 4.7619 | 300 | 1.1758 | 0.2389 | 1.1758 | 1.0844 |
202
+ | No log | 4.7937 | 302 | 1.2278 | 0.2187 | 1.2278 | 1.1081 |
203
+ | No log | 4.8254 | 304 | 1.2779 | 0.2568 | 1.2779 | 1.1305 |
204
+ | No log | 4.8571 | 306 | 1.2873 | 0.2070 | 1.2873 | 1.1346 |
205
+ | No log | 4.8889 | 308 | 1.2883 | 0.2070 | 1.2883 | 1.1350 |
206
+ | No log | 4.9206 | 310 | 1.3230 | 0.2070 | 1.3230 | 1.1502 |
207
+ | No log | 4.9524 | 312 | 1.2836 | 0.2568 | 1.2836 | 1.1329 |
208
+ | No log | 4.9841 | 314 | 1.2252 | 0.2752 | 1.2252 | 1.1069 |
209
+ | No log | 5.0159 | 316 | 1.1148 | 0.2149 | 1.1148 | 1.0558 |
210
+ | No log | 5.0476 | 318 | 1.0351 | 0.2636 | 1.0351 | 1.0174 |
211
+ | No log | 5.0794 | 320 | 1.0470 | 0.2192 | 1.0470 | 1.0232 |
212
+ | No log | 5.1111 | 322 | 1.1374 | 0.2315 | 1.1374 | 1.0665 |
213
+ | No log | 5.1429 | 324 | 1.3357 | 0.2391 | 1.3357 | 1.1557 |
214
+ | No log | 5.1746 | 326 | 1.3813 | 0.1751 | 1.3813 | 1.1753 |
215
+ | No log | 5.2063 | 328 | 1.3911 | 0.2058 | 1.3911 | 1.1795 |
216
+ | No log | 5.2381 | 330 | 1.2729 | 0.2221 | 1.2729 | 1.1282 |
217
+ | No log | 5.2698 | 332 | 1.1298 | 0.2089 | 1.1298 | 1.0629 |
218
+ | No log | 5.3016 | 334 | 1.1107 | 0.1770 | 1.1107 | 1.0539 |
219
+ | No log | 5.3333 | 336 | 1.1483 | 0.2089 | 1.1483 | 1.0716 |
220
+ | No log | 5.3651 | 338 | 1.2220 | 0.2065 | 1.2220 | 1.1054 |
221
+ | No log | 5.3968 | 340 | 1.2924 | 0.2437 | 1.2924 | 1.1368 |
222
+ | No log | 5.4286 | 342 | 1.3666 | 0.1751 | 1.3666 | 1.1690 |
223
+ | No log | 5.4603 | 344 | 1.2881 | 0.2079 | 1.2881 | 1.1350 |
224
+ | No log | 5.4921 | 346 | 1.2039 | 0.2531 | 1.2039 | 1.0972 |
225
+ | No log | 5.5238 | 348 | 1.1463 | 0.1793 | 1.1463 | 1.0706 |
226
+ | No log | 5.5556 | 350 | 1.1135 | 0.1976 | 1.1135 | 1.0552 |
227
+ | No log | 5.5873 | 352 | 1.0304 | 0.1322 | 1.0304 | 1.0151 |
228
+ | No log | 5.6190 | 354 | 1.0407 | 0.1322 | 1.0407 | 1.0201 |
229
+ | No log | 5.6508 | 356 | 1.1656 | 0.1904 | 1.1656 | 1.0796 |
230
+ | No log | 5.6825 | 358 | 1.3692 | 0.2482 | 1.3692 | 1.1701 |
231
+ | No log | 5.7143 | 360 | 1.4103 | 0.2110 | 1.4103 | 1.1876 |
232
+ | No log | 5.7460 | 362 | 1.2766 | 0.2123 | 1.2766 | 1.1299 |
233
+ | No log | 5.7778 | 364 | 1.1283 | 0.2729 | 1.1283 | 1.0622 |
234
+ | No log | 5.8095 | 366 | 1.1093 | 0.4014 | 1.1093 | 1.0532 |
235
+ | No log | 5.8413 | 368 | 1.1640 | 0.2925 | 1.1640 | 1.0789 |
236
+ | No log | 5.8730 | 370 | 1.2569 | 0.2359 | 1.2569 | 1.1211 |
237
+ | No log | 5.9048 | 372 | 1.2380 | 0.2261 | 1.2380 | 1.1127 |
238
+ | No log | 5.9365 | 374 | 1.2956 | 0.2315 | 1.2956 | 1.1383 |
239
+ | No log | 5.9683 | 376 | 1.2482 | 0.1370 | 1.2482 | 1.1172 |
240
+ | No log | 6.0 | 378 | 1.1948 | 0.0421 | 1.1948 | 1.0931 |
241
+ | No log | 6.0317 | 380 | 1.2096 | 0.1904 | 1.2096 | 1.0998 |
242
+ | No log | 6.0635 | 382 | 1.2630 | 0.2026 | 1.2630 | 1.1238 |
243
+ | No log | 6.0952 | 384 | 1.3273 | 0.2123 | 1.3273 | 1.1521 |
244
+ | No log | 6.1270 | 386 | 1.2108 | 0.2495 | 1.2108 | 1.1004 |
245
+ | No log | 6.1587 | 388 | 1.0720 | 0.1397 | 1.0720 | 1.0354 |
246
+ | No log | 6.1905 | 390 | 1.0734 | 0.0310 | 1.0734 | 1.0361 |
247
+ | No log | 6.2222 | 392 | 1.1378 | 0.1512 | 1.1378 | 1.0667 |
248
+ | No log | 6.2540 | 394 | 1.1882 | 0.1700 | 1.1882 | 1.0901 |
249
+ | No log | 6.2857 | 396 | 1.1619 | 0.0390 | 1.1619 | 1.0779 |
250
+ | No log | 6.3175 | 398 | 1.1756 | 0.1370 | 1.1756 | 1.0843 |
251
+ | No log | 6.3492 | 400 | 1.3104 | 0.2015 | 1.3104 | 1.1447 |
252
+ | No log | 6.3810 | 402 | 1.5121 | 0.2292 | 1.5121 | 1.2297 |
253
+ | No log | 6.4127 | 404 | 1.6091 | 0.2270 | 1.6091 | 1.2685 |
254
+ | No log | 6.4444 | 406 | 1.6418 | 0.2270 | 1.6418 | 1.2813 |
255
+ | No log | 6.4762 | 408 | 1.6159 | 0.2221 | 1.6159 | 1.2712 |
256
+ | No log | 6.5079 | 410 | 1.5312 | 0.2342 | 1.5312 | 1.2374 |
257
+ | No log | 6.5397 | 412 | 1.3822 | 0.2184 | 1.3822 | 1.1757 |
258
+ | No log | 6.5714 | 414 | 1.2950 | 0.2474 | 1.2950 | 1.1380 |
259
+ | No log | 6.6032 | 416 | 1.1656 | 0.2015 | 1.1656 | 1.0796 |
260
+ | No log | 6.6349 | 418 | 1.1724 | 0.2187 | 1.1724 | 1.0828 |
261
+ | No log | 6.6667 | 420 | 1.3506 | 0.2110 | 1.3506 | 1.1622 |
262
+ | No log | 6.6984 | 422 | 1.4516 | 0.2110 | 1.4516 | 1.2048 |
263
+ | No log | 6.7302 | 424 | 1.3696 | 0.2342 | 1.3696 | 1.1703 |
264
+ | No log | 6.7619 | 426 | 1.2759 | 0.2372 | 1.2759 | 1.1296 |
265
+ | No log | 6.7937 | 428 | 1.1777 | 0.0760 | 1.1777 | 1.0852 |
266
+ | No log | 6.8254 | 430 | 1.1569 | 0.0907 | 1.1569 | 1.0756 |
267
+ | No log | 6.8571 | 432 | 1.2380 | 0.2015 | 1.2380 | 1.1127 |
268
+ | No log | 6.8889 | 434 | 1.3816 | 0.2391 | 1.3816 | 1.1754 |
269
+ | No log | 6.9206 | 436 | 1.3856 | 0.2070 | 1.3856 | 1.1771 |
270
+ | No log | 6.9524 | 438 | 1.3158 | 0.2070 | 1.3158 | 1.1471 |
271
+ | No log | 6.9841 | 440 | 1.2496 | 0.2240 | 1.2496 | 1.1178 |
272
+ | No log | 7.0159 | 442 | 1.3116 | 0.2391 | 1.3116 | 1.1452 |
273
+ | No log | 7.0476 | 444 | 1.2774 | 0.2611 | 1.2774 | 1.1302 |
274
+ | No log | 7.0794 | 446 | 1.2192 | 0.2240 | 1.2192 | 1.1042 |
275
+ | No log | 7.1111 | 448 | 1.3183 | 0.2568 | 1.3183 | 1.1482 |
276
+ | No log | 7.1429 | 450 | 1.4253 | 0.2482 | 1.4253 | 1.1939 |
277
+ | No log | 7.1746 | 452 | 1.3744 | 0.2568 | 1.3744 | 1.1724 |
278
+ | No log | 7.2063 | 454 | 1.2861 | 0.2367 | 1.2861 | 1.1340 |
279
+ | No log | 7.2381 | 456 | 1.2743 | 0.2665 | 1.2743 | 1.1288 |
280
+ | No log | 7.2698 | 458 | 1.2702 | 0.2372 | 1.2702 | 1.1270 |
281
+ | No log | 7.3016 | 460 | 1.2393 | 0.2065 | 1.2393 | 1.1133 |
282
+ | No log | 7.3333 | 462 | 1.2301 | 0.1744 | 1.2301 | 1.1091 |
283
+ | No log | 7.3651 | 464 | 1.2362 | 0.2065 | 1.2362 | 1.1118 |
284
+ | No log | 7.3968 | 466 | 1.2915 | 0.2367 | 1.2915 | 1.1364 |
285
+ | No log | 7.4286 | 468 | 1.2742 | 0.2187 | 1.2742 | 1.1288 |
286
+ | No log | 7.4603 | 470 | 1.2634 | 0.2187 | 1.2634 | 1.1240 |
287
+ | No log | 7.4921 | 472 | 1.2463 | 0.2240 | 1.2463 | 1.1164 |
288
+ | No log | 7.5238 | 474 | 1.2015 | 0.2187 | 1.2015 | 1.0961 |
289
+ | No log | 7.5556 | 476 | 1.2214 | 0.2240 | 1.2214 | 1.1052 |
290
+ | No log | 7.5873 | 478 | 1.3597 | 0.1808 | 1.3597 | 1.1661 |
291
+ | No log | 7.6190 | 480 | 1.4012 | 0.1863 | 1.4012 | 1.1837 |
292
+ | No log | 7.6508 | 482 | 1.3582 | 0.2123 | 1.3582 | 1.1654 |
293
+ | No log | 7.6825 | 484 | 1.3630 | 0.2123 | 1.3630 | 1.1675 |
294
+ | No log | 7.7143 | 486 | 1.3255 | 0.2123 | 1.3255 | 1.1513 |
295
+ | No log | 7.7460 | 488 | 1.2378 | 0.2367 | 1.2378 | 1.1126 |
296
+ | No log | 7.7778 | 490 | 1.1866 | 0.2709 | 1.1866 | 1.0893 |
297
+ | No log | 7.8095 | 492 | 1.1961 | 0.2709 | 1.1961 | 1.0937 |
298
+ | No log | 7.8413 | 494 | 1.2208 | 0.2752 | 1.2208 | 1.1049 |
299
+ | No log | 7.8730 | 496 | 1.1444 | 0.2709 | 1.1444 | 1.0697 |
300
+ | No log | 7.9048 | 498 | 0.9855 | 0.2076 | 0.9855 | 0.9927 |
301
+ | 0.3001 | 7.9365 | 500 | 0.9231 | 0.2135 | 0.9231 | 0.9608 |
302
+ | 0.3001 | 7.9683 | 502 | 0.9045 | 0.3454 | 0.9045 | 0.9511 |
303
+ | 0.3001 | 8.0 | 504 | 0.9417 | 0.2325 | 0.9417 | 0.9704 |
304
+ | 0.3001 | 8.0317 | 506 | 1.0862 | 0.2750 | 1.0862 | 1.0422 |
305
+ | 0.3001 | 8.0635 | 508 | 1.3196 | 0.2606 | 1.3196 | 1.1487 |
306
+ | 0.3001 | 8.0952 | 510 | 1.3392 | 0.2606 | 1.3392 | 1.1573 |
307
+ | 0.3001 | 8.1270 | 512 | 1.2328 | 0.2240 | 1.2328 | 1.1103 |
308
+ | 0.3001 | 8.1587 | 514 | 1.1414 | 0.2417 | 1.1414 | 1.0684 |
309
+ | 0.3001 | 8.1905 | 516 | 1.1980 | 0.2417 | 1.1980 | 1.0945 |
310
+ | 0.3001 | 8.2222 | 518 | 1.3318 | 0.2187 | 1.3318 | 1.1540 |
311
+ | 0.3001 | 8.2540 | 520 | 1.3306 | 0.2187 | 1.3306 | 1.1535 |
312
+ | 0.3001 | 8.2857 | 522 | 1.1892 | 0.2283 | 1.1892 | 1.0905 |
313
+ | 0.3001 | 8.3175 | 524 | 1.1279 | 0.0691 | 1.1279 | 1.0620 |
314
+ | 0.3001 | 8.3492 | 526 | 1.0969 | 0.0310 | 1.0969 | 1.0473 |
315
+ | 0.3001 | 8.3810 | 528 | 1.0991 | 0.0310 | 1.0991 | 1.0484 |
316
+ | 0.3001 | 8.4127 | 530 | 1.1667 | 0.1053 | 1.1667 | 1.0801 |
317
+ | 0.3001 | 8.4444 | 532 | 1.2987 | 0.2315 | 1.2987 | 1.1396 |
318
+ | 0.3001 | 8.4762 | 534 | 1.3873 | 0.2221 | 1.3873 | 1.1778 |
319
+ | 0.3001 | 8.5079 | 536 | 1.4293 | 0.2221 | 1.4293 | 1.1955 |
320
+ | 0.3001 | 8.5397 | 538 | 1.4085 | 0.2424 | 1.4085 | 1.1868 |
321
+ | 0.3001 | 8.5714 | 540 | 1.3256 | 0.1744 | 1.3256 | 1.1513 |
322
+ | 0.3001 | 8.6032 | 542 | 1.2643 | 0.1407 | 1.2643 | 1.1244 |
323
+ | 0.3001 | 8.6349 | 544 | 1.1425 | 0.1434 | 1.1425 | 1.0689 |
324
+ | 0.3001 | 8.6667 | 546 | 1.0557 | 0.1528 | 1.0557 | 1.0275 |
325
+ | 0.3001 | 8.6984 | 548 | 1.0762 | 0.2192 | 1.0762 | 1.0374 |
326
+ | 0.3001 | 8.7302 | 550 | 1.2400 | 0.2313 | 1.2400 | 1.1135 |
327
+ | 0.3001 | 8.7619 | 552 | 1.4435 | 0.2317 | 1.4435 | 1.2015 |
328
+ | 0.3001 | 8.7937 | 554 | 1.5042 | 0.2317 | 1.5042 | 1.2265 |
329
+ | 0.3001 | 8.8254 | 556 | 1.4107 | 0.2317 | 1.4107 | 1.1877 |
330
+ | 0.3001 | 8.8571 | 558 | 1.2652 | 0.2026 | 1.2652 | 1.1248 |
331
+ | 0.3001 | 8.8889 | 560 | 1.2288 | 0.1434 | 1.2288 | 1.1085 |
332
+ | 0.3001 | 8.9206 | 562 | 1.1708 | 0.0841 | 1.1708 | 1.0820 |
333
+ | 0.3001 | 8.9524 | 564 | 1.1086 | 0.0894 | 1.1086 | 1.0529 |
334
+ | 0.3001 | 8.9841 | 566 | 1.1021 | 0.0894 | 1.1021 | 1.0498 |
335
+
336
+
337
+ ### Framework versions
338
+
339
+ - Transformers 4.44.2
340
+ - Pytorch 2.4.0+cu118
341
+ - Datasets 2.21.0
342
+ - 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 sha256:17eba234f0ac1d17f0d200e2506de3366f7fc04736cca289950c30eb597dd268
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:758ea4d8523f3c49570a6ba2085f4c9238ba21f19eab5d850887bbcace700af9
3
+ size 5368