Hugging Face's logo

MayBashendy
/
ArabicNewSplits7_OSS_usingWellWrittenEssays_FineTuningAraBERT_run3_AugV5_k14_task2_organization

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
 Community
MayBashendy commited on
Commit
7a42e1d
·
verified ·
1 Parent(s): 0c9c4b8

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_run3_AugV5_k13_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_run3_AugV5_k13_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: 0.8480
19
+ - Qwk: 0.4413
20
+ - Mse: 0.8480
21
+ - Rmse: 0.9209
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 | 0.0270 | 2 | 4.6072 | -0.0132 | 4.6072 | 2.1464 |
53
+ | No log | 0.0541 | 4 | 2.8525 | -0.0189 | 2.8525 | 1.6889 |
54
+ | No log | 0.0811 | 6 | 1.5590 | 0.0585 | 1.5590 | 1.2486 |
55
+ | No log | 0.1081 | 8 | 1.2592 | 0.1144 | 1.2592 | 1.1221 |
56
+ | No log | 0.1351 | 10 | 1.3213 | 0.0955 | 1.3213 | 1.1495 |
57
+ | No log | 0.1622 | 12 | 1.1815 | 0.1247 | 1.1815 | 1.0870 |
58
+ | No log | 0.1892 | 14 | 1.1845 | 0.0628 | 1.1845 | 1.0883 |
59
+ | No log | 0.2162 | 16 | 1.1350 | 0.1417 | 1.1350 | 1.0654 |
60
+ | No log | 0.2432 | 18 | 1.1129 | 0.2939 | 1.1129 | 1.0549 |
61
+ | No log | 0.2703 | 20 | 1.2344 | 0.2161 | 1.2344 | 1.1110 |
62
+ | No log | 0.2973 | 22 | 1.2167 | 0.2214 | 1.2167 | 1.1031 |
63
+ | No log | 0.3243 | 24 | 1.1097 | 0.3679 | 1.1097 | 1.0534 |
64
+ | No log | 0.3514 | 26 | 1.0080 | 0.3985 | 1.0080 | 1.0040 |
65
+ | No log | 0.3784 | 28 | 1.0328 | 0.2128 | 1.0328 | 1.0163 |
66
+ | No log | 0.4054 | 30 | 1.1395 | 0.2293 | 1.1395 | 1.0675 |
67
+ | No log | 0.4324 | 32 | 1.1539 | 0.2293 | 1.1539 | 1.0742 |
68
+ | No log | 0.4595 | 34 | 1.1204 | 0.1585 | 1.1204 | 1.0585 |
69
+ | No log | 0.4865 | 36 | 1.1114 | 0.2220 | 1.1114 | 1.0542 |
70
+ | No log | 0.5135 | 38 | 1.1536 | 0.1919 | 1.1536 | 1.0741 |
71
+ | No log | 0.5405 | 40 | 1.4140 | 0.1284 | 1.4140 | 1.1891 |
72
+ | No log | 0.5676 | 42 | 1.4022 | 0.1171 | 1.4022 | 1.1841 |
73
+ | No log | 0.5946 | 44 | 1.3857 | 0.1256 | 1.3857 | 1.1772 |
74
+ | No log | 0.6216 | 46 | 1.2614 | 0.1459 | 1.2614 | 1.1231 |
75
+ | No log | 0.6486 | 48 | 1.0296 | 0.3086 | 1.0296 | 1.0147 |
76
+ | No log | 0.6757 | 50 | 0.9912 | 0.3231 | 0.9912 | 0.9956 |
77
+ | No log | 0.7027 | 52 | 1.0928 | 0.375 | 1.0928 | 1.0454 |
78
+ | No log | 0.7297 | 54 | 1.1020 | 0.4075 | 1.1020 | 1.0498 |
79
+ | No log | 0.7568 | 56 | 0.9537 | 0.4275 | 0.9537 | 0.9766 |
80
+ | No log | 0.7838 | 58 | 0.9262 | 0.4871 | 0.9262 | 0.9624 |
81
+ | No log | 0.8108 | 60 | 0.9017 | 0.5119 | 0.9017 | 0.9496 |
82
+ | No log | 0.8378 | 62 | 0.9272 | 0.4633 | 0.9272 | 0.9629 |
83
+ | No log | 0.8649 | 64 | 0.8483 | 0.6067 | 0.8483 | 0.9210 |
84
+ | No log | 0.8919 | 66 | 0.9990 | 0.4457 | 0.9990 | 0.9995 |
85
+ | No log | 0.9189 | 68 | 1.2149 | 0.3482 | 1.2149 | 1.1022 |
86
+ | No log | 0.9459 | 70 | 1.0839 | 0.3977 | 1.0839 | 1.0411 |
87
+ | No log | 0.9730 | 72 | 1.0551 | 0.5418 | 1.0551 | 1.0272 |
88
+ | No log | 1.0 | 74 | 1.0217 | 0.4923 | 1.0217 | 1.0108 |
89
+ | No log | 1.0270 | 76 | 0.9609 | 0.4668 | 0.9609 | 0.9802 |
90
+ | No log | 1.0541 | 78 | 1.0360 | 0.4625 | 1.0360 | 1.0178 |
91
+ | No log | 1.0811 | 80 | 1.2860 | 0.4330 | 1.2860 | 1.1340 |
92
+ | No log | 1.1081 | 82 | 1.1112 | 0.4341 | 1.1112 | 1.0541 |
93
+ | No log | 1.1351 | 84 | 0.9104 | 0.4786 | 0.9104 | 0.9541 |
94
+ | No log | 1.1622 | 86 | 0.8026 | 0.4852 | 0.8026 | 0.8959 |
95
+ | No log | 1.1892 | 88 | 0.7544 | 0.4969 | 0.7544 | 0.8686 |
96
+ | No log | 1.2162 | 90 | 0.8186 | 0.4744 | 0.8186 | 0.9048 |
97
+ | No log | 1.2432 | 92 | 0.8288 | 0.4698 | 0.8288 | 0.9104 |
98
+ | No log | 1.2703 | 94 | 0.7670 | 0.4889 | 0.7670 | 0.8758 |
99
+ | No log | 1.2973 | 96 | 0.8309 | 0.5317 | 0.8309 | 0.9115 |
100
+ | No log | 1.3243 | 98 | 0.8374 | 0.5399 | 0.8374 | 0.9151 |
101
+ | No log | 1.3514 | 100 | 0.8054 | 0.4906 | 0.8054 | 0.8975 |
102
+ | No log | 1.3784 | 102 | 0.8101 | 0.4777 | 0.8101 | 0.9001 |
103
+ | No log | 1.4054 | 104 | 0.9400 | 0.4726 | 0.9400 | 0.9695 |
104
+ | No log | 1.4324 | 106 | 1.2027 | 0.3960 | 1.2027 | 1.0967 |
105
+ | No log | 1.4595 | 108 | 1.4250 | 0.3632 | 1.4250 | 1.1937 |
106
+ | No log | 1.4865 | 110 | 1.3376 | 0.3623 | 1.3376 | 1.1566 |
107
+ | No log | 1.5135 | 112 | 0.9860 | 0.4422 | 0.9860 | 0.9930 |
108
+ | No log | 1.5405 | 114 | 0.8105 | 0.4540 | 0.8105 | 0.9003 |
109
+ | No log | 1.5676 | 116 | 0.8122 | 0.4696 | 0.8122 | 0.9012 |
110
+ | No log | 1.5946 | 118 | 0.8117 | 0.4440 | 0.8117 | 0.9009 |
111
+ | No log | 1.6216 | 120 | 0.9125 | 0.4902 | 0.9125 | 0.9553 |
112
+ | No log | 1.6486 | 122 | 1.0160 | 0.5184 | 1.0160 | 1.0080 |
113
+ | No log | 1.6757 | 124 | 0.9197 | 0.5532 | 0.9197 | 0.9590 |
114
+ | No log | 1.7027 | 126 | 0.8570 | 0.5345 | 0.8570 | 0.9258 |
115
+ | No log | 1.7297 | 128 | 0.8016 | 0.4707 | 0.8016 | 0.8953 |
116
+ | No log | 1.7568 | 130 | 0.7899 | 0.4726 | 0.7899 | 0.8888 |
117
+ | No log | 1.7838 | 132 | 0.7679 | 0.4789 | 0.7679 | 0.8763 |
118
+ | No log | 1.8108 | 134 | 0.8009 | 0.5175 | 0.8009 | 0.8949 |
119
+ | No log | 1.8378 | 136 | 0.8021 | 0.5175 | 0.8021 | 0.8956 |
120
+ | No log | 1.8649 | 138 | 0.7721 | 0.4982 | 0.7721 | 0.8787 |
121
+ | No log | 1.8919 | 140 | 0.8008 | 0.5773 | 0.8008 | 0.8949 |
122
+ | No log | 1.9189 | 142 | 0.8311 | 0.5264 | 0.8311 | 0.9116 |
123
+ | No log | 1.9459 | 144 | 0.9079 | 0.5636 | 0.9079 | 0.9528 |
124
+ | No log | 1.9730 | 146 | 0.8751 | 0.5121 | 0.8751 | 0.9355 |
125
+ | No log | 2.0 | 148 | 0.8476 | 0.5487 | 0.8476 | 0.9206 |
126
+ | No log | 2.0270 | 150 | 0.9633 | 0.4743 | 0.9633 | 0.9815 |
127
+ | No log | 2.0541 | 152 | 1.1846 | 0.4549 | 1.1846 | 1.0884 |
128
+ | No log | 2.0811 | 154 | 1.1083 | 0.4835 | 1.1083 | 1.0528 |
129
+ | No log | 2.1081 | 156 | 0.8379 | 0.4665 | 0.8379 | 0.9153 |
130
+ | No log | 2.1351 | 158 | 0.7996 | 0.5550 | 0.7996 | 0.8942 |
131
+ | No log | 2.1622 | 160 | 0.8780 | 0.4745 | 0.8780 | 0.9370 |
132
+ | No log | 2.1892 | 162 | 1.0333 | 0.5260 | 1.0333 | 1.0165 |
133
+ | No log | 2.2162 | 164 | 1.0417 | 0.4990 | 1.0417 | 1.0206 |
134
+ | No log | 2.2432 | 166 | 0.8771 | 0.5786 | 0.8771 | 0.9366 |
135
+ | No log | 2.2703 | 168 | 0.8086 | 0.5875 | 0.8086 | 0.8992 |
136
+ | No log | 2.2973 | 170 | 0.8831 | 0.5521 | 0.8831 | 0.9398 |
137
+ | No log | 2.3243 | 172 | 1.1023 | 0.4349 | 1.1023 | 1.0499 |
138
+ | No log | 2.3514 | 174 | 1.0151 | 0.4593 | 1.0151 | 1.0075 |
139
+ | No log | 2.3784 | 176 | 0.9554 | 0.4431 | 0.9554 | 0.9774 |
140
+ | No log | 2.4054 | 178 | 0.9341 | 0.4945 | 0.9341 | 0.9665 |
141
+ | No log | 2.4324 | 180 | 1.0094 | 0.4760 | 1.0094 | 1.0047 |
142
+ | No log | 2.4595 | 182 | 1.2741 | 0.4620 | 1.2741 | 1.1288 |
143
+ | No log | 2.4865 | 184 | 1.3386 | 0.4523 | 1.3386 | 1.1570 |
144
+ | No log | 2.5135 | 186 | 1.1004 | 0.4377 | 1.1004 | 1.0490 |
145
+ | No log | 2.5405 | 188 | 0.9167 | 0.4546 | 0.9167 | 0.9575 |
146
+ | No log | 2.5676 | 190 | 0.9058 | 0.3787 | 0.9058 | 0.9517 |
147
+ | No log | 2.5946 | 192 | 0.9455 | 0.4826 | 0.9455 | 0.9724 |
148
+ | No log | 2.6216 | 194 | 1.0709 | 0.3755 | 1.0709 | 1.0349 |
149
+ | No log | 2.6486 | 196 | 1.1117 | 0.3525 | 1.1117 | 1.0544 |
150
+ | No log | 2.6757 | 198 | 1.0929 | 0.3863 | 1.0929 | 1.0454 |
151
+ | No log | 2.7027 | 200 | 0.9632 | 0.4490 | 0.9632 | 0.9814 |
152
+ | No log | 2.7297 | 202 | 0.8998 | 0.4794 | 0.8998 | 0.9486 |
153
+ | No log | 2.7568 | 204 | 0.9594 | 0.4697 | 0.9594 | 0.9795 |
154
+ | No log | 2.7838 | 206 | 1.0404 | 0.5105 | 1.0404 | 1.0200 |
155
+ | No log | 2.8108 | 208 | 0.9484 | 0.4689 | 0.9484 | 0.9739 |
156
+ | No log | 2.8378 | 210 | 0.8794 | 0.4914 | 0.8794 | 0.9377 |
157
+ | No log | 2.8649 | 212 | 0.8288 | 0.4996 | 0.8288 | 0.9104 |
158
+ | No log | 2.8919 | 214 | 0.8532 | 0.5025 | 0.8532 | 0.9237 |
159
+ | No log | 2.9189 | 216 | 1.1165 | 0.5335 | 1.1165 | 1.0567 |
160
+ | No log | 2.9459 | 218 | 1.6590 | 0.4291 | 1.6590 | 1.2880 |
161
+ | No log | 2.9730 | 220 | 1.6706 | 0.4068 | 1.6706 | 1.2925 |
162
+ | No log | 3.0 | 222 | 1.2717 | 0.4282 | 1.2717 | 1.1277 |
163
+ | No log | 3.0270 | 224 | 0.8369 | 0.5624 | 0.8369 | 0.9148 |
164
+ | No log | 3.0541 | 226 | 0.7687 | 0.5672 | 0.7687 | 0.8768 |
165
+ | No log | 3.0811 | 228 | 0.7685 | 0.5556 | 0.7685 | 0.8766 |
166
+ | No log | 3.1081 | 230 | 0.7936 | 0.4982 | 0.7936 | 0.8908 |
167
+ | No log | 3.1351 | 232 | 0.8892 | 0.5273 | 0.8892 | 0.9430 |
168
+ | No log | 3.1622 | 234 | 0.9855 | 0.5101 | 0.9855 | 0.9927 |
169
+ | No log | 3.1892 | 236 | 1.0394 | 0.4726 | 1.0394 | 1.0195 |
170
+ | No log | 3.2162 | 238 | 0.9717 | 0.5039 | 0.9717 | 0.9857 |
171
+ | No log | 3.2432 | 240 | 0.9176 | 0.4975 | 0.9176 | 0.9579 |
172
+ | No log | 3.2703 | 242 | 0.9378 | 0.4043 | 0.9378 | 0.9684 |
173
+ | No log | 3.2973 | 244 | 1.1052 | 0.3585 | 1.1052 | 1.0513 |
174
+ | No log | 3.3243 | 246 | 1.2291 | 0.3599 | 1.2291 | 1.1087 |
175
+ | No log | 3.3514 | 248 | 1.2450 | 0.3684 | 1.2450 | 1.1158 |
176
+ | No log | 3.3784 | 250 | 1.0095 | 0.4767 | 1.0095 | 1.0048 |
177
+ | No log | 3.4054 | 252 | 0.8248 | 0.5251 | 0.8248 | 0.9082 |
178
+ | No log | 3.4324 | 254 | 0.7977 | 0.5489 | 0.7977 | 0.8931 |
179
+ | No log | 3.4595 | 256 | 0.7984 | 0.5196 | 0.7984 | 0.8935 |
180
+ | No log | 3.4865 | 258 | 0.8322 | 0.4836 | 0.8322 | 0.9122 |
181
+ | No log | 3.5135 | 260 | 0.9609 | 0.4946 | 0.9609 | 0.9802 |
182
+ | No log | 3.5405 | 262 | 0.9953 | 0.4302 | 0.9953 | 0.9976 |
183
+ | No log | 3.5676 | 264 | 0.9870 | 0.4010 | 0.9870 | 0.9935 |
184
+ | No log | 3.5946 | 266 | 0.9081 | 0.4732 | 0.9081 | 0.9529 |
185
+ | No log | 3.6216 | 268 | 0.8288 | 0.5094 | 0.8288 | 0.9104 |
186
+ | No log | 3.6486 | 270 | 0.8226 | 0.4418 | 0.8226 | 0.9070 |
187
+ | No log | 3.6757 | 272 | 0.8137 | 0.4514 | 0.8137 | 0.9021 |
188
+ | No log | 3.7027 | 274 | 0.8081 | 0.4996 | 0.8081 | 0.8990 |
189
+ | No log | 3.7297 | 276 | 0.9369 | 0.4972 | 0.9369 | 0.9679 |
190
+ | No log | 3.7568 | 278 | 0.9825 | 0.4545 | 0.9825 | 0.9912 |
191
+ | No log | 3.7838 | 280 | 0.8880 | 0.4869 | 0.8880 | 0.9423 |
192
+ | No log | 3.8108 | 282 | 0.8257 | 0.5131 | 0.8257 | 0.9087 |
193
+ | No log | 3.8378 | 284 | 0.8357 | 0.5318 | 0.8357 | 0.9142 |
194
+ | No log | 3.8649 | 286 | 0.9596 | 0.4589 | 0.9596 | 0.9796 |
195
+ | No log | 3.8919 | 288 | 1.1871 | 0.4186 | 1.1871 | 1.0896 |
196
+ | No log | 3.9189 | 290 | 1.2865 | 0.3521 | 1.2865 | 1.1342 |
197
+ | No log | 3.9459 | 292 | 1.1691 | 0.3210 | 1.1691 | 1.0812 |
198
+ | No log | 3.9730 | 294 | 1.0415 | 0.4085 | 1.0415 | 1.0205 |
199
+ | No log | 4.0 | 296 | 1.0154 | 0.3428 | 1.0154 | 1.0077 |
200
+ | No log | 4.0270 | 298 | 0.9779 | 0.3372 | 0.9779 | 0.9889 |
201
+ | No log | 4.0541 | 300 | 0.8959 | 0.4734 | 0.8959 | 0.9465 |
202
+ | No log | 4.0811 | 302 | 0.8422 | 0.4910 | 0.8422 | 0.9177 |
203
+ | No log | 4.1081 | 304 | 0.8877 | 0.5027 | 0.8877 | 0.9422 |
204
+ | No log | 4.1351 | 306 | 1.0010 | 0.4661 | 1.0010 | 1.0005 |
205
+ | No log | 4.1622 | 308 | 0.9641 | 0.5058 | 0.9641 | 0.9819 |
206
+ | No log | 4.1892 | 310 | 0.8963 | 0.4311 | 0.8963 | 0.9468 |
207
+ | No log | 4.2162 | 312 | 0.8934 | 0.4334 | 0.8934 | 0.9452 |
208
+ | No log | 4.2432 | 314 | 0.8879 | 0.3230 | 0.8879 | 0.9423 |
209
+ | No log | 4.2703 | 316 | 0.8661 | 0.4879 | 0.8661 | 0.9307 |
210
+ | No log | 4.2973 | 318 | 0.9907 | 0.5016 | 0.9907 | 0.9954 |
211
+ | No log | 4.3243 | 320 | 1.0690 | 0.4890 | 1.0690 | 1.0339 |
212
+ | No log | 4.3514 | 322 | 0.9788 | 0.4817 | 0.9788 | 0.9893 |
213
+ | No log | 4.3784 | 324 | 0.8717 | 0.5230 | 0.8717 | 0.9337 |
214
+ | No log | 4.4054 | 326 | 0.8533 | 0.5362 | 0.8533 | 0.9238 |
215
+ | No log | 4.4324 | 328 | 0.8789 | 0.5230 | 0.8789 | 0.9375 |
216
+ | No log | 4.4595 | 330 | 0.9973 | 0.4387 | 0.9973 | 0.9986 |
217
+ | No log | 4.4865 | 332 | 1.0369 | 0.4462 | 1.0369 | 1.0183 |
218
+ | No log | 4.5135 | 334 | 0.9465 | 0.5029 | 0.9465 | 0.9729 |
219
+ | No log | 4.5405 | 336 | 0.8794 | 0.4761 | 0.8794 | 0.9378 |
220
+ | No log | 4.5676 | 338 | 0.8305 | 0.4648 | 0.8305 | 0.9113 |
221
+ | No log | 4.5946 | 340 | 0.8642 | 0.4831 | 0.8642 | 0.9296 |
222
+ | No log | 4.6216 | 342 | 0.9364 | 0.3962 | 0.9364 | 0.9677 |
223
+ | No log | 4.6486 | 344 | 0.9885 | 0.4466 | 0.9885 | 0.9942 |
224
+ | No log | 4.6757 | 346 | 0.9498 | 0.5408 | 0.9498 | 0.9746 |
225
+ | No log | 4.7027 | 348 | 0.9177 | 0.5493 | 0.9177 | 0.9579 |
226
+ | No log | 4.7297 | 350 | 0.9390 | 0.5474 | 0.9390 | 0.9690 |
227
+ | No log | 4.7568 | 352 | 0.9003 | 0.5493 | 0.9003 | 0.9488 |
228
+ | No log | 4.7838 | 354 | 0.8829 | 0.5557 | 0.8829 | 0.9396 |
229
+ | No log | 4.8108 | 356 | 0.8589 | 0.4985 | 0.8589 | 0.9268 |
230
+ | No log | 4.8378 | 358 | 0.8749 | 0.5041 | 0.8749 | 0.9354 |
231
+ | No log | 4.8649 | 360 | 0.8860 | 0.4690 | 0.8860 | 0.9413 |
232
+ | No log | 4.8919 | 362 | 0.8751 | 0.4242 | 0.8751 | 0.9355 |
233
+ | No log | 4.9189 | 364 | 0.9055 | 0.4021 | 0.9055 | 0.9516 |
234
+ | No log | 4.9459 | 366 | 0.9104 | 0.3777 | 0.9104 | 0.9541 |
235
+ | No log | 4.9730 | 368 | 0.9514 | 0.3891 | 0.9514 | 0.9754 |
236
+ | No log | 5.0 | 370 | 1.0432 | 0.4094 | 1.0432 | 1.0214 |
237
+ | No log | 5.0270 | 372 | 1.1159 | 0.4300 | 1.1159 | 1.0563 |
238
+ | No log | 5.0541 | 374 | 0.9918 | 0.4127 | 0.9918 | 0.9959 |
239
+ | No log | 5.0811 | 376 | 0.9416 | 0.4519 | 0.9416 | 0.9704 |
240
+ | No log | 5.1081 | 378 | 0.9169 | 0.4553 | 0.9169 | 0.9576 |
241
+ | No log | 5.1351 | 380 | 0.8928 | 0.4628 | 0.8928 | 0.9449 |
242
+ | No log | 5.1622 | 382 | 0.9226 | 0.4983 | 0.9226 | 0.9605 |
243
+ | No log | 5.1892 | 384 | 1.0098 | 0.4382 | 1.0098 | 1.0049 |
244
+ | No log | 5.2162 | 386 | 0.9654 | 0.4390 | 0.9654 | 0.9826 |
245
+ | No log | 5.2432 | 388 | 0.9092 | 0.4983 | 0.9092 | 0.9535 |
246
+ | No log | 5.2703 | 390 | 0.8921 | 0.4595 | 0.8921 | 0.9445 |
247
+ | No log | 5.2973 | 392 | 0.9207 | 0.4328 | 0.9207 | 0.9595 |
248
+ | No log | 5.3243 | 394 | 1.0064 | 0.4479 | 1.0064 | 1.0032 |
249
+ | No log | 5.3514 | 396 | 1.0258 | 0.4220 | 1.0258 | 1.0128 |
250
+ | No log | 5.3784 | 398 | 0.9230 | 0.4983 | 0.9230 | 0.9607 |
251
+ | No log | 5.4054 | 400 | 0.8892 | 0.4966 | 0.8892 | 0.9430 |
252
+ | No log | 5.4324 | 402 | 0.8770 | 0.4554 | 0.8770 | 0.9365 |
253
+ | No log | 5.4595 | 404 | 0.8918 | 0.4644 | 0.8918 | 0.9444 |
254
+ | No log | 5.4865 | 406 | 0.8752 | 0.4252 | 0.8752 | 0.9355 |
255
+ | No log | 5.5135 | 408 | 0.8802 | 0.4568 | 0.8802 | 0.9382 |
256
+ | No log | 5.5405 | 410 | 0.9038 | 0.4240 | 0.9038 | 0.9507 |
257
+ | No log | 5.5676 | 412 | 0.9445 | 0.4998 | 0.9445 | 0.9718 |
258
+ | No log | 5.5946 | 414 | 0.9651 | 0.4607 | 0.9651 | 0.9824 |
259
+ | No log | 5.6216 | 416 | 0.9185 | 0.4953 | 0.9185 | 0.9584 |
260
+ | No log | 5.6486 | 418 | 0.8843 | 0.5131 | 0.8843 | 0.9404 |
261
+ | No log | 5.6757 | 420 | 0.8884 | 0.5131 | 0.8884 | 0.9426 |
262
+ | No log | 5.7027 | 422 | 0.9170 | 0.4877 | 0.9170 | 0.9576 |
263
+ | No log | 5.7297 | 424 | 0.9039 | 0.5251 | 0.9039 | 0.9507 |
264
+ | No log | 5.7568 | 426 | 0.8957 | 0.4841 | 0.8957 | 0.9464 |
265
+ | No log | 5.7838 | 428 | 0.9176 | 0.3987 | 0.9176 | 0.9579 |
266
+ | No log | 5.8108 | 430 | 0.9506 | 0.4532 | 0.9506 | 0.9750 |
267
+ | No log | 5.8378 | 432 | 1.0297 | 0.4598 | 1.0297 | 1.0148 |
268
+ | No log | 5.8649 | 434 | 1.2059 | 0.3701 | 1.2059 | 1.0981 |
269
+ | No log | 5.8919 | 436 | 1.1999 | 0.4001 | 1.1999 | 1.0954 |
270
+ | No log | 5.9189 | 438 | 1.0193 | 0.4583 | 1.0193 | 1.0096 |
271
+ | No log | 5.9459 | 440 | 0.9144 | 0.3525 | 0.9144 | 0.9562 |
272
+ | No log | 5.9730 | 442 | 0.9358 | 0.3571 | 0.9358 | 0.9674 |
273
+ | No log | 6.0 | 444 | 0.9202 | 0.3283 | 0.9202 | 0.9593 |
274
+ | No log | 6.0270 | 446 | 0.9342 | 0.4470 | 0.9342 | 0.9665 |
275
+ | No log | 6.0541 | 448 | 1.0279 | 0.3967 | 1.0279 | 1.0139 |
276
+ | No log | 6.0811 | 450 | 1.1503 | 0.3963 | 1.1503 | 1.0725 |
277
+ | No log | 6.1081 | 452 | 1.1868 | 0.4341 | 1.1868 | 1.0894 |
278
+ | No log | 6.1351 | 454 | 1.0747 | 0.4377 | 1.0747 | 1.0367 |
279
+ | No log | 6.1622 | 456 | 0.9403 | 0.4591 | 0.9403 | 0.9697 |
280
+ | No log | 6.1892 | 458 | 0.8652 | 0.4470 | 0.8652 | 0.9302 |
281
+ | No log | 6.2162 | 460 | 0.8477 | 0.4181 | 0.8477 | 0.9207 |
282
+ | No log | 6.2432 | 462 | 0.8524 | 0.4980 | 0.8524 | 0.9233 |
283
+ | No log | 6.2703 | 464 | 0.8658 | 0.4724 | 0.8658 | 0.9305 |
284
+ | No log | 6.2973 | 466 | 0.8923 | 0.4533 | 0.8923 | 0.9446 |
285
+ | No log | 6.3243 | 468 | 0.9116 | 0.3595 | 0.9116 | 0.9548 |
286
+ | No log | 6.3514 | 470 | 0.8782 | 0.4337 | 0.8782 | 0.9371 |
287
+ | No log | 6.3784 | 472 | 0.8330 | 0.4278 | 0.8330 | 0.9127 |
288
+ | No log | 6.4054 | 474 | 0.8279 | 0.3908 | 0.8279 | 0.9099 |
289
+ | No log | 6.4324 | 476 | 0.8715 | 0.4398 | 0.8715 | 0.9336 |
290
+ | No log | 6.4595 | 478 | 0.9616 | 0.4649 | 0.9616 | 0.9806 |
291
+ | No log | 6.4865 | 480 | 0.9451 | 0.4479 | 0.9451 | 0.9722 |
292
+ | No log | 6.5135 | 482 | 0.8572 | 0.4787 | 0.8572 | 0.9258 |
293
+ | No log | 6.5405 | 484 | 0.8229 | 0.4898 | 0.8229 | 0.9071 |
294
+ | No log | 6.5676 | 486 | 0.8275 | 0.4705 | 0.8275 | 0.9097 |
295
+ | No log | 6.5946 | 488 | 0.8292 | 0.4771 | 0.8292 | 0.9106 |
296
+ | No log | 6.6216 | 490 | 0.8150 | 0.4801 | 0.8150 | 0.9028 |
297
+ | No log | 6.6486 | 492 | 0.8372 | 0.5131 | 0.8372 | 0.9150 |
298
+ | No log | 6.6757 | 494 | 0.8814 | 0.4864 | 0.8814 | 0.9388 |
299
+ | No log | 6.7027 | 496 | 0.9619 | 0.5529 | 0.9619 | 0.9808 |
300
+ | No log | 6.7297 | 498 | 1.0125 | 0.4767 | 1.0125 | 1.0062 |
301
+ | 0.3615 | 6.7568 | 500 | 0.9921 | 0.4777 | 0.9921 | 0.9960 |
302
+ | 0.3615 | 6.7838 | 502 | 0.9125 | 0.4479 | 0.9125 | 0.9552 |
303
+ | 0.3615 | 6.8108 | 504 | 0.8463 | 0.4724 | 0.8463 | 0.9200 |
304
+ | 0.3615 | 6.8378 | 506 | 0.8568 | 0.4628 | 0.8568 | 0.9256 |
305
+ | 0.3615 | 6.8649 | 508 | 0.8838 | 0.4295 | 0.8838 | 0.9401 |
306
+ | 0.3615 | 6.8919 | 510 | 0.8480 | 0.4413 | 0.8480 | 0.9209 |
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:ca0561ae0e708047a88c89943f4b0a6e85dffa7c033c7a21329694df822a1f00
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:38df4e3404fbb0c95ce48b8d76c394cf279fa5b9e3529c3955b173a7732ef470
3
+ size 5304