Hugging Face's logo

MayBashendy
/
ArabicNewSplits7_B_usingWellWrittenEssays_FineTuningAraBERT_run3_AugV5_k8_task2_organization

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
 Community
MayBashendy commited on
Commit
96d7e18
·
verified ·
1 Parent(s): c6c7ccf

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_usingWellWrittenEssays_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
+ # ArabicNewSplits7_B_usingWellWrittenEssays_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: 0.9276
19
+ - Qwk: 0.3609
20
+ - Mse: 0.9276
21
+ - Rmse: 0.9631
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 | 0.0526 | 2 | 4.5488 | 0.0010 | 4.5488 | 2.1328 |
53
+ | No log | 0.1053 | 4 | 2.5395 | 0.0039 | 2.5395 | 1.5936 |
54
+ | No log | 0.1579 | 6 | 1.9919 | -0.0233 | 1.9919 | 1.4113 |
55
+ | No log | 0.2105 | 8 | 1.5066 | 0.0101 | 1.5066 | 1.2274 |
56
+ | No log | 0.2632 | 10 | 1.3092 | 0.0731 | 1.3092 | 1.1442 |
57
+ | No log | 0.3158 | 12 | 1.3200 | 0.0472 | 1.3200 | 1.1489 |
58
+ | No log | 0.3684 | 14 | 1.3044 | 0.0841 | 1.3044 | 1.1421 |
59
+ | No log | 0.4211 | 16 | 1.2121 | 0.0941 | 1.2121 | 1.1010 |
60
+ | No log | 0.4737 | 18 | 1.1821 | 0.1407 | 1.1821 | 1.0873 |
61
+ | No log | 0.5263 | 20 | 1.3218 | 0.0575 | 1.3218 | 1.1497 |
62
+ | No log | 0.5789 | 22 | 1.5035 | 0.0 | 1.5035 | 1.2262 |
63
+ | No log | 0.6316 | 24 | 1.6095 | 0.0 | 1.6095 | 1.2687 |
64
+ | No log | 0.6842 | 26 | 1.5280 | 0.0 | 1.5280 | 1.2361 |
65
+ | No log | 0.7368 | 28 | 1.3824 | 0.0254 | 1.3824 | 1.1758 |
66
+ | No log | 0.7895 | 30 | 1.2671 | 0.0865 | 1.2671 | 1.1257 |
67
+ | No log | 0.8421 | 32 | 1.1720 | 0.1793 | 1.1720 | 1.0826 |
68
+ | No log | 0.8947 | 34 | 1.1836 | 0.2323 | 1.1836 | 1.0879 |
69
+ | No log | 0.9474 | 36 | 1.2076 | 0.2180 | 1.2076 | 1.0989 |
70
+ | No log | 1.0 | 38 | 1.1870 | 0.1968 | 1.1870 | 1.0895 |
71
+ | No log | 1.0526 | 40 | 1.1672 | 0.1773 | 1.1672 | 1.0804 |
72
+ | No log | 1.1053 | 42 | 1.1971 | 0.1076 | 1.1971 | 1.0941 |
73
+ | No log | 1.1579 | 44 | 1.2066 | 0.1417 | 1.2066 | 1.0985 |
74
+ | No log | 1.2105 | 46 | 1.2317 | 0.0979 | 1.2317 | 1.1098 |
75
+ | No log | 1.2632 | 48 | 1.3459 | 0.0955 | 1.3459 | 1.1601 |
76
+ | No log | 1.3158 | 50 | 1.3393 | 0.1136 | 1.3393 | 1.1573 |
77
+ | No log | 1.3684 | 52 | 1.2494 | 0.2040 | 1.2494 | 1.1178 |
78
+ | No log | 1.4211 | 54 | 1.2080 | 0.2025 | 1.2080 | 1.0991 |
79
+ | No log | 1.4737 | 56 | 1.1788 | 0.1772 | 1.1788 | 1.0857 |
80
+ | No log | 1.5263 | 58 | 1.1270 | 0.1977 | 1.1270 | 1.0616 |
81
+ | No log | 1.5789 | 60 | 1.1276 | 0.1602 | 1.1276 | 1.0619 |
82
+ | No log | 1.6316 | 62 | 1.1267 | 0.1442 | 1.1267 | 1.0614 |
83
+ | No log | 1.6842 | 64 | 1.0864 | 0.1406 | 1.0864 | 1.0423 |
84
+ | No log | 1.7368 | 66 | 1.1186 | 0.2665 | 1.1186 | 1.0577 |
85
+ | No log | 1.7895 | 68 | 1.2390 | 0.1772 | 1.2390 | 1.1131 |
86
+ | No log | 1.8421 | 70 | 1.1890 | 0.2058 | 1.1890 | 1.0904 |
87
+ | No log | 1.8947 | 72 | 1.0508 | 0.3230 | 1.0508 | 1.0251 |
88
+ | No log | 1.9474 | 74 | 0.9894 | 0.3596 | 0.9894 | 0.9947 |
89
+ | No log | 2.0 | 76 | 0.9848 | 0.3596 | 0.9848 | 0.9924 |
90
+ | No log | 2.0526 | 78 | 0.9835 | 0.4157 | 0.9835 | 0.9917 |
91
+ | No log | 2.1053 | 80 | 1.0601 | 0.3106 | 1.0601 | 1.0296 |
92
+ | No log | 2.1579 | 82 | 1.3051 | 0.2465 | 1.3051 | 1.1424 |
93
+ | No log | 2.2105 | 84 | 1.3157 | 0.2465 | 1.3157 | 1.1470 |
94
+ | No log | 2.2632 | 86 | 1.1385 | 0.2961 | 1.1385 | 1.0670 |
95
+ | No log | 2.3158 | 88 | 0.9746 | 0.3042 | 0.9746 | 0.9872 |
96
+ | No log | 2.3684 | 90 | 1.1242 | 0.125 | 1.1242 | 1.0603 |
97
+ | No log | 2.4211 | 92 | 1.2865 | 0.0920 | 1.2865 | 1.1342 |
98
+ | No log | 2.4737 | 94 | 1.2922 | 0.1516 | 1.2922 | 1.1368 |
99
+ | No log | 2.5263 | 96 | 1.1589 | 0.0930 | 1.1589 | 1.0765 |
100
+ | No log | 2.5789 | 98 | 1.0201 | 0.2871 | 1.0201 | 1.0100 |
101
+ | No log | 2.6316 | 100 | 0.9628 | 0.4002 | 0.9628 | 0.9812 |
102
+ | No log | 2.6842 | 102 | 0.9691 | 0.3650 | 0.9691 | 0.9844 |
103
+ | No log | 2.7368 | 104 | 0.9869 | 0.3723 | 0.9869 | 0.9935 |
104
+ | No log | 2.7895 | 106 | 0.9965 | 0.3855 | 0.9965 | 0.9982 |
105
+ | No log | 2.8421 | 108 | 1.0050 | 0.4527 | 1.0050 | 1.0025 |
106
+ | No log | 2.8947 | 110 | 1.0141 | 0.4455 | 1.0141 | 1.0070 |
107
+ | No log | 2.9474 | 112 | 1.0241 | 0.4120 | 1.0241 | 1.0120 |
108
+ | No log | 3.0 | 114 | 1.1377 | 0.2030 | 1.1377 | 1.0666 |
109
+ | No log | 3.0526 | 116 | 1.2001 | 0.2233 | 1.2001 | 1.0955 |
110
+ | No log | 3.1053 | 118 | 1.1021 | 0.1919 | 1.1021 | 1.0498 |
111
+ | No log | 3.1579 | 120 | 1.0399 | 0.3677 | 1.0399 | 1.0198 |
112
+ | No log | 3.2105 | 122 | 1.0420 | 0.3263 | 1.0420 | 1.0208 |
113
+ | No log | 3.2632 | 124 | 1.0441 | 0.3493 | 1.0441 | 1.0218 |
114
+ | No log | 3.3158 | 126 | 1.0549 | 0.3275 | 1.0549 | 1.0271 |
115
+ | No log | 3.3684 | 128 | 1.0611 | 0.2857 | 1.0611 | 1.0301 |
116
+ | No log | 3.4211 | 130 | 1.1801 | 0.3094 | 1.1801 | 1.0863 |
117
+ | No log | 3.4737 | 132 | 1.1667 | 0.1516 | 1.1667 | 1.0802 |
118
+ | No log | 3.5263 | 134 | 1.0546 | 0.2927 | 1.0546 | 1.0270 |
119
+ | No log | 3.5789 | 136 | 1.0015 | 0.3270 | 1.0015 | 1.0008 |
120
+ | No log | 3.6316 | 138 | 1.0141 | 0.2893 | 1.0141 | 1.0070 |
121
+ | No log | 3.6842 | 140 | 1.0188 | 0.2647 | 1.0188 | 1.0093 |
122
+ | No log | 3.7368 | 142 | 0.9972 | 0.2823 | 0.9972 | 0.9986 |
123
+ | No log | 3.7895 | 144 | 0.9728 | 0.3263 | 0.9728 | 0.9863 |
124
+ | No log | 3.8421 | 146 | 0.9704 | 0.3983 | 0.9704 | 0.9851 |
125
+ | No log | 3.8947 | 148 | 0.9707 | 0.3478 | 0.9707 | 0.9852 |
126
+ | No log | 3.9474 | 150 | 1.0144 | 0.3517 | 1.0144 | 1.0072 |
127
+ | No log | 4.0 | 152 | 1.0006 | 0.3389 | 1.0006 | 1.0003 |
128
+ | No log | 4.0526 | 154 | 0.9850 | 0.3045 | 0.9850 | 0.9925 |
129
+ | No log | 4.1053 | 156 | 0.9925 | 0.2871 | 0.9925 | 0.9963 |
130
+ | No log | 4.1579 | 158 | 0.9906 | 0.2871 | 0.9906 | 0.9953 |
131
+ | No log | 4.2105 | 160 | 0.9998 | 0.4105 | 0.9998 | 0.9999 |
132
+ | No log | 4.2632 | 162 | 1.0137 | 0.4369 | 1.0137 | 1.0068 |
133
+ | No log | 4.3158 | 164 | 1.0514 | 0.2918 | 1.0514 | 1.0254 |
134
+ | No log | 4.3684 | 166 | 1.0650 | 0.3013 | 1.0650 | 1.0320 |
135
+ | No log | 4.4211 | 168 | 1.0144 | 0.3344 | 1.0144 | 1.0072 |
136
+ | No log | 4.4737 | 170 | 1.0532 | 0.3186 | 1.0532 | 1.0262 |
137
+ | No log | 4.5263 | 172 | 1.0879 | 0.3424 | 1.0879 | 1.0430 |
138
+ | No log | 4.5789 | 174 | 1.0212 | 0.4006 | 1.0212 | 1.0105 |
139
+ | No log | 4.6316 | 176 | 1.0370 | 0.3399 | 1.0370 | 1.0184 |
140
+ | No log | 4.6842 | 178 | 1.0217 | 0.3134 | 1.0217 | 1.0108 |
141
+ | No log | 4.7368 | 180 | 1.0742 | 0.3299 | 1.0742 | 1.0364 |
142
+ | No log | 4.7895 | 182 | 1.2070 | 0.3513 | 1.2070 | 1.0986 |
143
+ | No log | 4.8421 | 184 | 1.1768 | 0.2725 | 1.1768 | 1.0848 |
144
+ | No log | 4.8947 | 186 | 1.2029 | 0.2696 | 1.2029 | 1.0968 |
145
+ | No log | 4.9474 | 188 | 1.2002 | 0.2037 | 1.2002 | 1.0955 |
146
+ | No log | 5.0 | 190 | 1.3104 | 0.2676 | 1.3104 | 1.1447 |
147
+ | No log | 5.0526 | 192 | 1.4079 | 0.2676 | 1.4079 | 1.1865 |
148
+ | No log | 5.1053 | 194 | 1.2947 | 0.2122 | 1.2947 | 1.1378 |
149
+ | No log | 5.1579 | 196 | 1.0865 | 0.1406 | 1.0865 | 1.0424 |
150
+ | No log | 5.2105 | 198 | 1.0712 | 0.2349 | 1.0712 | 1.0350 |
151
+ | No log | 5.2632 | 200 | 1.0650 | 0.2242 | 1.0650 | 1.0320 |
152
+ | No log | 5.3158 | 202 | 1.1746 | 0.2905 | 1.1746 | 1.0838 |
153
+ | No log | 5.3684 | 204 | 1.4991 | 0.2436 | 1.4991 | 1.2244 |
154
+ | No log | 5.4211 | 206 | 1.7056 | 0.2222 | 1.7056 | 1.3060 |
155
+ | No log | 5.4737 | 208 | 1.6040 | 0.1953 | 1.6040 | 1.2665 |
156
+ | No log | 5.5263 | 210 | 1.2969 | 0.2457 | 1.2969 | 1.1388 |
157
+ | No log | 5.5789 | 212 | 1.0895 | 0.2535 | 1.0895 | 1.0438 |
158
+ | No log | 5.6316 | 214 | 1.0073 | 0.2721 | 1.0073 | 1.0037 |
159
+ | No log | 5.6842 | 216 | 0.9891 | 0.3317 | 0.9891 | 0.9945 |
160
+ | No log | 5.7368 | 218 | 0.9974 | 0.3217 | 0.9974 | 0.9987 |
161
+ | No log | 5.7895 | 220 | 0.9982 | 0.2995 | 0.9982 | 0.9991 |
162
+ | No log | 5.8421 | 222 | 0.9905 | 0.2587 | 0.9905 | 0.9953 |
163
+ | No log | 5.8947 | 224 | 0.9683 | 0.3147 | 0.9683 | 0.9840 |
164
+ | No log | 5.9474 | 226 | 0.9544 | 0.3382 | 0.9544 | 0.9769 |
165
+ | No log | 6.0 | 228 | 0.9682 | 0.3418 | 0.9682 | 0.9840 |
166
+ | No log | 6.0526 | 230 | 0.9263 | 0.3780 | 0.9263 | 0.9625 |
167
+ | No log | 6.1053 | 232 | 0.8848 | 0.3508 | 0.8848 | 0.9406 |
168
+ | No log | 6.1579 | 234 | 1.0110 | 0.4508 | 1.0110 | 1.0055 |
169
+ | No log | 6.2105 | 236 | 1.0953 | 0.3992 | 1.0953 | 1.0466 |
170
+ | No log | 6.2632 | 238 | 0.9904 | 0.4345 | 0.9904 | 0.9952 |
171
+ | No log | 6.3158 | 240 | 0.8663 | 0.4450 | 0.8663 | 0.9308 |
172
+ | No log | 6.3684 | 242 | 1.0251 | 0.4100 | 1.0251 | 1.0125 |
173
+ | No log | 6.4211 | 244 | 1.1169 | 0.3692 | 1.1169 | 1.0568 |
174
+ | No log | 6.4737 | 246 | 0.9938 | 0.3644 | 0.9938 | 0.9969 |
175
+ | No log | 6.5263 | 248 | 0.9229 | 0.3271 | 0.9229 | 0.9607 |
176
+ | No log | 6.5789 | 250 | 1.0281 | 0.2263 | 1.0281 | 1.0140 |
177
+ | No log | 6.6316 | 252 | 1.1905 | 0.2634 | 1.1905 | 1.0911 |
178
+ | No log | 6.6842 | 254 | 1.2132 | 0.2914 | 1.2132 | 1.1015 |
179
+ | No log | 6.7368 | 256 | 1.1279 | 0.2184 | 1.1279 | 1.0620 |
180
+ | No log | 6.7895 | 258 | 1.0070 | 0.2899 | 1.0070 | 1.0035 |
181
+ | No log | 6.8421 | 260 | 0.9787 | 0.3733 | 0.9787 | 0.9893 |
182
+ | No log | 6.8947 | 262 | 0.9834 | 0.3542 | 0.9834 | 0.9917 |
183
+ | No log | 6.9474 | 264 | 0.9716 | 0.3542 | 0.9716 | 0.9857 |
184
+ | No log | 7.0 | 266 | 0.9531 | 0.3399 | 0.9531 | 0.9763 |
185
+ | No log | 7.0526 | 268 | 0.9494 | 0.3497 | 0.9494 | 0.9744 |
186
+ | No log | 7.1053 | 270 | 0.9307 | 0.3354 | 0.9307 | 0.9647 |
187
+ | No log | 7.1579 | 272 | 0.9050 | 0.3838 | 0.9050 | 0.9513 |
188
+ | No log | 7.2105 | 274 | 0.8801 | 0.3552 | 0.8801 | 0.9381 |
189
+ | No log | 7.2632 | 276 | 0.8741 | 0.3463 | 0.8741 | 0.9350 |
190
+ | No log | 7.3158 | 278 | 0.9637 | 0.3438 | 0.9637 | 0.9817 |
191
+ | No log | 7.3684 | 280 | 1.1225 | 0.3959 | 1.1225 | 1.0595 |
192
+ | No log | 7.4211 | 282 | 1.1380 | 0.4155 | 1.1380 | 1.0668 |
193
+ | No log | 7.4737 | 284 | 0.9543 | 0.4219 | 0.9543 | 0.9769 |
194
+ | No log | 7.5263 | 286 | 0.8283 | 0.4662 | 0.8283 | 0.9101 |
195
+ | No log | 7.5789 | 288 | 0.8442 | 0.4626 | 0.8442 | 0.9188 |
196
+ | No log | 7.6316 | 290 | 0.8633 | 0.4491 | 0.8633 | 0.9291 |
197
+ | No log | 7.6842 | 292 | 0.8866 | 0.3902 | 0.8866 | 0.9416 |
198
+ | No log | 7.7368 | 294 | 0.9190 | 0.4105 | 0.9190 | 0.9586 |
199
+ | No log | 7.7895 | 296 | 0.9219 | 0.3804 | 0.9219 | 0.9601 |
200
+ | No log | 7.8421 | 298 | 0.9284 | 0.3902 | 0.9284 | 0.9635 |
201
+ | No log | 7.8947 | 300 | 0.9338 | 0.3609 | 0.9338 | 0.9663 |
202
+ | No log | 7.9474 | 302 | 0.9340 | 0.2775 | 0.9340 | 0.9664 |
203
+ | No log | 8.0 | 304 | 0.9377 | 0.2584 | 0.9377 | 0.9684 |
204
+ | No log | 8.0526 | 306 | 0.9292 | 0.3753 | 0.9292 | 0.9639 |
205
+ | No log | 8.1053 | 308 | 0.9450 | 0.4067 | 0.9450 | 0.9721 |
206
+ | No log | 8.1579 | 310 | 0.9312 | 0.3720 | 0.9312 | 0.9650 |
207
+ | No log | 8.2105 | 312 | 0.9207 | 0.2823 | 0.9207 | 0.9596 |
208
+ | No log | 8.2632 | 314 | 0.9476 | 0.2966 | 0.9476 | 0.9734 |
209
+ | No log | 8.3158 | 316 | 0.9325 | 0.3309 | 0.9325 | 0.9657 |
210
+ | No log | 8.3684 | 318 | 0.9576 | 0.3738 | 0.9576 | 0.9786 |
211
+ | No log | 8.4211 | 320 | 0.9860 | 0.3478 | 0.9860 | 0.9930 |
212
+ | No log | 8.4737 | 322 | 1.0146 | 0.2525 | 1.0146 | 1.0073 |
213
+ | No log | 8.5263 | 324 | 1.0357 | 0.2683 | 1.0357 | 1.0177 |
214
+ | No log | 8.5789 | 326 | 1.0381 | 0.25 | 1.0381 | 1.0189 |
215
+ | No log | 8.6316 | 328 | 1.0327 | 0.2475 | 1.0327 | 1.0162 |
216
+ | No log | 8.6842 | 330 | 0.9772 | 0.3547 | 0.9772 | 0.9886 |
217
+ | No log | 8.7368 | 332 | 0.9313 | 0.3564 | 0.9313 | 0.9650 |
218
+ | No log | 8.7895 | 334 | 0.9397 | 0.4838 | 0.9397 | 0.9694 |
219
+ | No log | 8.8421 | 336 | 0.9646 | 0.4814 | 0.9646 | 0.9821 |
220
+ | No log | 8.8947 | 338 | 1.0922 | 0.3706 | 1.0922 | 1.0451 |
221
+ | No log | 8.9474 | 340 | 1.2406 | 0.4123 | 1.2406 | 1.1138 |
222
+ | No log | 9.0 | 342 | 1.1852 | 0.3833 | 1.1852 | 1.0887 |
223
+ | No log | 9.0526 | 344 | 1.0421 | 0.3256 | 1.0421 | 1.0208 |
224
+ | No log | 9.1053 | 346 | 0.9801 | 0.2702 | 0.9801 | 0.9900 |
225
+ | No log | 9.1579 | 348 | 0.9829 | 0.1662 | 0.9829 | 0.9914 |
226
+ | No log | 9.2105 | 350 | 1.0148 | 0.2702 | 1.0148 | 1.0074 |
227
+ | No log | 9.2632 | 352 | 1.1307 | 0.3578 | 1.1307 | 1.0634 |
228
+ | No log | 9.3158 | 354 | 1.3383 | 0.3935 | 1.3383 | 1.1569 |
229
+ | No log | 9.3684 | 356 | 1.3581 | 0.4126 | 1.3581 | 1.1654 |
230
+ | No log | 9.4211 | 358 | 1.2027 | 0.3688 | 1.2027 | 1.0967 |
231
+ | No log | 9.4737 | 360 | 1.0220 | 0.2110 | 1.0220 | 1.0110 |
232
+ | No log | 9.5263 | 362 | 0.9857 | 0.2432 | 0.9857 | 0.9928 |
233
+ | No log | 9.5789 | 364 | 0.9758 | 0.2773 | 0.9758 | 0.9878 |
234
+ | No log | 9.6316 | 366 | 0.9724 | 0.2773 | 0.9724 | 0.9861 |
235
+ | No log | 9.6842 | 368 | 1.0292 | 0.2837 | 1.0292 | 1.0145 |
236
+ | No log | 9.7368 | 370 | 1.2165 | 0.3992 | 1.2165 | 1.1030 |
237
+ | No log | 9.7895 | 372 | 1.2367 | 0.3959 | 1.2367 | 1.1121 |
238
+ | No log | 9.8421 | 374 | 1.0920 | 0.3237 | 1.0920 | 1.0450 |
239
+ | No log | 9.8947 | 376 | 0.9694 | 0.3602 | 0.9694 | 0.9846 |
240
+ | No log | 9.9474 | 378 | 0.9333 | 0.3200 | 0.9333 | 0.9661 |
241
+ | No log | 10.0 | 380 | 0.9192 | 0.3756 | 0.9192 | 0.9587 |
242
+ | No log | 10.0526 | 382 | 0.9304 | 0.3697 | 0.9304 | 0.9646 |
243
+ | No log | 10.1053 | 384 | 1.0100 | 0.3424 | 1.0100 | 1.0050 |
244
+ | No log | 10.1579 | 386 | 1.0628 | 0.3384 | 1.0628 | 1.0309 |
245
+ | No log | 10.2105 | 388 | 1.0128 | 0.3465 | 1.0128 | 1.0064 |
246
+ | No log | 10.2632 | 390 | 0.9150 | 0.3707 | 0.9150 | 0.9566 |
247
+ | No log | 10.3158 | 392 | 0.8948 | 0.3424 | 0.8948 | 0.9460 |
248
+ | No log | 10.3684 | 394 | 0.9016 | 0.3335 | 0.9016 | 0.9495 |
249
+ | No log | 10.4211 | 396 | 0.9397 | 0.3656 | 0.9397 | 0.9694 |
250
+ | No log | 10.4737 | 398 | 1.0429 | 0.3590 | 1.0429 | 1.0212 |
251
+ | No log | 10.5263 | 400 | 1.0498 | 0.3365 | 1.0498 | 1.0246 |
252
+ | No log | 10.5789 | 402 | 0.9696 | 0.3022 | 0.9696 | 0.9847 |
253
+ | No log | 10.6316 | 404 | 0.9242 | 0.3164 | 0.9242 | 0.9613 |
254
+ | No log | 10.6842 | 406 | 0.9753 | 0.3516 | 0.9753 | 0.9876 |
255
+ | No log | 10.7368 | 408 | 0.9928 | 0.3502 | 0.9928 | 0.9964 |
256
+ | No log | 10.7895 | 410 | 0.9508 | 0.4258 | 0.9508 | 0.9751 |
257
+ | No log | 10.8421 | 412 | 0.9546 | 0.2625 | 0.9546 | 0.9770 |
258
+ | No log | 10.8947 | 414 | 0.9954 | 0.3022 | 0.9954 | 0.9977 |
259
+ | No log | 10.9474 | 416 | 1.0189 | 0.3299 | 1.0189 | 1.0094 |
260
+ | No log | 11.0 | 418 | 0.9817 | 0.2857 | 0.9817 | 0.9908 |
261
+ | No log | 11.0526 | 420 | 0.9480 | 0.3596 | 0.9480 | 0.9737 |
262
+ | No log | 11.1053 | 422 | 0.9509 | 0.3210 | 0.9509 | 0.9752 |
263
+ | No log | 11.1579 | 424 | 0.9757 | 0.2801 | 0.9757 | 0.9878 |
264
+ | No log | 11.2105 | 426 | 1.0211 | 0.2481 | 1.0211 | 1.0105 |
265
+ | No log | 11.2632 | 428 | 1.0187 | 0.2998 | 1.0187 | 1.0093 |
266
+ | No log | 11.3158 | 430 | 0.9857 | 0.2822 | 0.9857 | 0.9928 |
267
+ | No log | 11.3684 | 432 | 0.9711 | 0.3639 | 0.9711 | 0.9855 |
268
+ | No log | 11.4211 | 434 | 0.9732 | 0.3780 | 0.9732 | 0.9865 |
269
+ | No log | 11.4737 | 436 | 0.9842 | 0.3389 | 0.9842 | 0.9921 |
270
+ | No log | 11.5263 | 438 | 1.0335 | 0.2343 | 1.0335 | 1.0166 |
271
+ | No log | 11.5789 | 440 | 1.1381 | 0.2864 | 1.1381 | 1.0668 |
272
+ | No log | 11.6316 | 442 | 1.1443 | 0.2864 | 1.1443 | 1.0697 |
273
+ | No log | 11.6842 | 444 | 1.1013 | 0.2884 | 1.1013 | 1.0494 |
274
+ | No log | 11.7368 | 446 | 1.1113 | 0.3182 | 1.1113 | 1.0542 |
275
+ | No log | 11.7895 | 448 | 1.0470 | 0.3162 | 1.0470 | 1.0232 |
276
+ | No log | 11.8421 | 450 | 1.0042 | 0.2782 | 1.0042 | 1.0021 |
277
+ | No log | 11.8947 | 452 | 0.9899 | 0.2506 | 0.9899 | 0.9950 |
278
+ | No log | 11.9474 | 454 | 0.9565 | 0.2621 | 0.9565 | 0.9780 |
279
+ | No log | 12.0 | 456 | 0.9448 | 0.2921 | 0.9448 | 0.9720 |
280
+ | No log | 12.0526 | 458 | 0.9420 | 0.3069 | 0.9420 | 0.9706 |
281
+ | No log | 12.1053 | 460 | 0.9162 | 0.3753 | 0.9162 | 0.9572 |
282
+ | No log | 12.1579 | 462 | 0.8941 | 0.3896 | 0.8941 | 0.9456 |
283
+ | No log | 12.2105 | 464 | 0.8938 | 0.4142 | 0.8938 | 0.9454 |
284
+ | No log | 12.2632 | 466 | 0.9201 | 0.3643 | 0.9201 | 0.9592 |
285
+ | No log | 12.3158 | 468 | 0.9513 | 0.3734 | 0.9513 | 0.9754 |
286
+ | No log | 12.3684 | 470 | 0.9940 | 0.3923 | 0.9940 | 0.9970 |
287
+ | No log | 12.4211 | 472 | 0.9743 | 0.3923 | 0.9743 | 0.9870 |
288
+ | No log | 12.4737 | 474 | 0.9143 | 0.4166 | 0.9143 | 0.9562 |
289
+ | No log | 12.5263 | 476 | 0.9137 | 0.4166 | 0.9137 | 0.9559 |
290
+ | No log | 12.5789 | 478 | 0.9338 | 0.4345 | 0.9338 | 0.9664 |
291
+ | No log | 12.6316 | 480 | 0.9121 | 0.3809 | 0.9121 | 0.9550 |
292
+ | No log | 12.6842 | 482 | 0.8928 | 0.3590 | 0.8928 | 0.9449 |
293
+ | No log | 12.7368 | 484 | 0.9031 | 0.3590 | 0.9031 | 0.9503 |
294
+ | No log | 12.7895 | 486 | 0.9554 | 0.3405 | 0.9554 | 0.9774 |
295
+ | No log | 12.8421 | 488 | 0.9954 | 0.2326 | 0.9954 | 0.9977 |
296
+ | No log | 12.8947 | 490 | 0.9649 | 0.3263 | 0.9649 | 0.9823 |
297
+ | No log | 12.9474 | 492 | 0.9195 | 0.3289 | 0.9195 | 0.9589 |
298
+ | No log | 13.0 | 494 | 0.8898 | 0.3609 | 0.8898 | 0.9433 |
299
+ | No log | 13.0526 | 496 | 0.8857 | 0.4104 | 0.8857 | 0.9411 |
300
+ | No log | 13.1053 | 498 | 0.9020 | 0.4104 | 0.9020 | 0.9498 |
301
+ | 0.4127 | 13.1579 | 500 | 0.9000 | 0.4104 | 0.9000 | 0.9487 |
302
+ | 0.4127 | 13.2105 | 502 | 0.9069 | 0.3522 | 0.9069 | 0.9523 |
303
+ | 0.4127 | 13.2632 | 504 | 0.9146 | 0.3263 | 0.9146 | 0.9563 |
304
+ | 0.4127 | 13.3158 | 506 | 0.9300 | 0.3609 | 0.9300 | 0.9644 |
305
+ | 0.4127 | 13.3684 | 508 | 0.9338 | 0.3609 | 0.9338 | 0.9663 |
306
+ | 0.4127 | 13.4211 | 510 | 0.9276 | 0.3609 | 0.9276 | 0.9631 |
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 sha256:0fd07e1e1c4efcc32e510ac2e176db5abc6d69900ffd2496388b609e1c29a30a
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0860e9920ee7ee60baee611d00219f42ce6cd09b7f89c939d9a00bf448d90061
3
+ size 5368