Hugging Face's logo

MayBashendy
/
ArabicNewSplits7_OSS_usingWellWrittenEssays_FineTuningAraBERT_run2_AugV5_k20_task2_organization

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
 Community
MayBashendy commited on
Commit
0f964e8
·
verified ·
1 Parent(s): 3161f8f

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_run2_AugV5_k19_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_run2_AugV5_k19_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: 1.0323
19
+ - Qwk: 0.5626
20
+ - Mse: 1.0323
21
+ - Rmse: 1.0160
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 | 0.0187 | 2 | 4.5742 | 0.0010 | 4.5742 | 2.1387 |
53
+ | No log | 0.0374 | 4 | 2.7808 | -0.0280 | 2.7808 | 1.6676 |
54
+ | No log | 0.0561 | 6 | 1.4907 | 0.0372 | 1.4907 | 1.2210 |
55
+ | No log | 0.0748 | 8 | 1.2752 | 0.1112 | 1.2752 | 1.1293 |
56
+ | No log | 0.0935 | 10 | 1.1950 | 0.1247 | 1.1950 | 1.0932 |
57
+ | No log | 0.1121 | 12 | 1.2196 | 0.1482 | 1.2196 | 1.1044 |
58
+ | No log | 0.1308 | 14 | 1.2320 | 0.1176 | 1.2320 | 1.1100 |
59
+ | No log | 0.1495 | 16 | 1.2284 | 0.1308 | 1.2284 | 1.1083 |
60
+ | No log | 0.1682 | 18 | 1.3662 | -0.0165 | 1.3662 | 1.1689 |
61
+ | No log | 0.1869 | 20 | 1.3489 | 0.1426 | 1.3489 | 1.1614 |
62
+ | No log | 0.2056 | 22 | 1.1815 | 0.2089 | 1.1815 | 1.0870 |
63
+ | No log | 0.2243 | 24 | 1.1298 | 0.2176 | 1.1298 | 1.0629 |
64
+ | No log | 0.2430 | 26 | 1.0777 | 0.3146 | 1.0777 | 1.0381 |
65
+ | No log | 0.2617 | 28 | 1.2038 | 0.3402 | 1.2038 | 1.0972 |
66
+ | No log | 0.2804 | 30 | 1.3131 | 0.2880 | 1.3131 | 1.1459 |
67
+ | No log | 0.2991 | 32 | 1.0127 | 0.4344 | 1.0127 | 1.0063 |
68
+ | No log | 0.3178 | 34 | 0.9437 | 0.4938 | 0.9437 | 0.9714 |
69
+ | No log | 0.3364 | 36 | 1.2058 | 0.4652 | 1.2058 | 1.0981 |
70
+ | No log | 0.3551 | 38 | 0.9460 | 0.5115 | 0.9460 | 0.9726 |
71
+ | No log | 0.3738 | 40 | 0.8743 | 0.5789 | 0.8743 | 0.9350 |
72
+ | No log | 0.3925 | 42 | 1.1487 | 0.4136 | 1.1487 | 1.0718 |
73
+ | No log | 0.4112 | 44 | 1.0855 | 0.4270 | 1.0855 | 1.0419 |
74
+ | No log | 0.4299 | 46 | 0.8817 | 0.5210 | 0.8817 | 0.9390 |
75
+ | No log | 0.4486 | 48 | 0.7964 | 0.5491 | 0.7964 | 0.8924 |
76
+ | No log | 0.4673 | 50 | 0.7752 | 0.4086 | 0.7752 | 0.8804 |
77
+ | No log | 0.4860 | 52 | 0.7908 | 0.5580 | 0.7908 | 0.8892 |
78
+ | No log | 0.5047 | 54 | 0.8772 | 0.5994 | 0.8772 | 0.9366 |
79
+ | No log | 0.5234 | 56 | 1.1544 | 0.3403 | 1.1544 | 1.0744 |
80
+ | No log | 0.5421 | 58 | 1.1979 | 0.3529 | 1.1979 | 1.0945 |
81
+ | No log | 0.5607 | 60 | 1.0665 | 0.4489 | 1.0665 | 1.0327 |
82
+ | No log | 0.5794 | 62 | 1.0434 | 0.4881 | 1.0434 | 1.0215 |
83
+ | No log | 0.5981 | 64 | 0.8545 | 0.6046 | 0.8545 | 0.9244 |
84
+ | No log | 0.6168 | 66 | 1.0237 | 0.5306 | 1.0237 | 1.0118 |
85
+ | No log | 0.6355 | 68 | 1.3465 | 0.4407 | 1.3465 | 1.1604 |
86
+ | No log | 0.6542 | 70 | 1.1866 | 0.4670 | 1.1866 | 1.0893 |
87
+ | No log | 0.6729 | 72 | 0.9180 | 0.5109 | 0.9180 | 0.9581 |
88
+ | No log | 0.6916 | 74 | 0.8089 | 0.5149 | 0.8089 | 0.8994 |
89
+ | No log | 0.7103 | 76 | 0.8358 | 0.5607 | 0.8358 | 0.9142 |
90
+ | No log | 0.7290 | 78 | 0.8475 | 0.5726 | 0.8475 | 0.9206 |
91
+ | No log | 0.7477 | 80 | 0.8585 | 0.6118 | 0.8585 | 0.9266 |
92
+ | No log | 0.7664 | 82 | 0.8526 | 0.6107 | 0.8526 | 0.9234 |
93
+ | No log | 0.7850 | 84 | 0.8976 | 0.4677 | 0.8976 | 0.9474 |
94
+ | No log | 0.8037 | 86 | 0.9249 | 0.4802 | 0.9249 | 0.9617 |
95
+ | No log | 0.8224 | 88 | 0.8791 | 0.5773 | 0.8791 | 0.9376 |
96
+ | No log | 0.8411 | 90 | 0.8222 | 0.5418 | 0.8222 | 0.9068 |
97
+ | No log | 0.8598 | 92 | 0.8667 | 0.5458 | 0.8667 | 0.9310 |
98
+ | No log | 0.8785 | 94 | 0.9783 | 0.4867 | 0.9783 | 0.9891 |
99
+ | No log | 0.8972 | 96 | 0.9549 | 0.5310 | 0.9549 | 0.9772 |
100
+ | No log | 0.9159 | 98 | 0.7622 | 0.6088 | 0.7622 | 0.8730 |
101
+ | No log | 0.9346 | 100 | 0.7348 | 0.6225 | 0.7348 | 0.8572 |
102
+ | No log | 0.9533 | 102 | 0.7299 | 0.5889 | 0.7299 | 0.8543 |
103
+ | No log | 0.9720 | 104 | 0.8288 | 0.4619 | 0.8288 | 0.9104 |
104
+ | No log | 0.9907 | 106 | 0.7285 | 0.6029 | 0.7285 | 0.8535 |
105
+ | No log | 1.0093 | 108 | 0.7229 | 0.6590 | 0.7229 | 0.8502 |
106
+ | No log | 1.0280 | 110 | 0.7887 | 0.6348 | 0.7887 | 0.8881 |
107
+ | No log | 1.0467 | 112 | 0.8820 | 0.5310 | 0.8820 | 0.9392 |
108
+ | No log | 1.0654 | 114 | 0.7363 | 0.6793 | 0.7363 | 0.8581 |
109
+ | No log | 1.0841 | 116 | 0.6788 | 0.6476 | 0.6788 | 0.8239 |
110
+ | No log | 1.1028 | 118 | 0.6633 | 0.6476 | 0.6633 | 0.8144 |
111
+ | No log | 1.1215 | 120 | 0.7199 | 0.6859 | 0.7199 | 0.8485 |
112
+ | No log | 1.1402 | 122 | 0.7061 | 0.6721 | 0.7061 | 0.8403 |
113
+ | No log | 1.1589 | 124 | 0.6233 | 0.6234 | 0.6233 | 0.7895 |
114
+ | No log | 1.1776 | 126 | 0.6389 | 0.6051 | 0.6389 | 0.7993 |
115
+ | No log | 1.1963 | 128 | 0.6038 | 0.6160 | 0.6038 | 0.7770 |
116
+ | No log | 1.2150 | 130 | 0.6940 | 0.6682 | 0.6940 | 0.8331 |
117
+ | No log | 1.2336 | 132 | 1.1083 | 0.5694 | 1.1083 | 1.0527 |
118
+ | No log | 1.2523 | 134 | 1.1363 | 0.5372 | 1.1363 | 1.0660 |
119
+ | No log | 1.2710 | 136 | 0.8082 | 0.6021 | 0.8082 | 0.8990 |
120
+ | No log | 1.2897 | 138 | 0.6307 | 0.6602 | 0.6307 | 0.7942 |
121
+ | No log | 1.3084 | 140 | 0.6429 | 0.6350 | 0.6429 | 0.8018 |
122
+ | No log | 1.3271 | 142 | 0.7191 | 0.6544 | 0.7191 | 0.8480 |
123
+ | No log | 1.3458 | 144 | 0.9188 | 0.5570 | 0.9188 | 0.9585 |
124
+ | No log | 1.3645 | 146 | 0.9202 | 0.5570 | 0.9202 | 0.9593 |
125
+ | No log | 1.3832 | 148 | 0.9090 | 0.5570 | 0.9090 | 0.9534 |
126
+ | No log | 1.4019 | 150 | 0.8081 | 0.5693 | 0.8081 | 0.8989 |
127
+ | No log | 1.4206 | 152 | 0.7209 | 0.5793 | 0.7209 | 0.8490 |
128
+ | No log | 1.4393 | 154 | 0.7145 | 0.5877 | 0.7145 | 0.8453 |
129
+ | No log | 1.4579 | 156 | 0.7067 | 0.6692 | 0.7067 | 0.8407 |
130
+ | No log | 1.4766 | 158 | 0.7543 | 0.6554 | 0.7543 | 0.8685 |
131
+ | No log | 1.4953 | 160 | 0.8483 | 0.5878 | 0.8483 | 0.9210 |
132
+ | No log | 1.5140 | 162 | 1.0342 | 0.5765 | 1.0342 | 1.0170 |
133
+ | No log | 1.5327 | 164 | 1.0431 | 0.5340 | 1.0431 | 1.0213 |
134
+ | No log | 1.5514 | 166 | 0.8056 | 0.5902 | 0.8056 | 0.8976 |
135
+ | No log | 1.5701 | 168 | 0.6654 | 0.6840 | 0.6654 | 0.8157 |
136
+ | No log | 1.5888 | 170 | 0.6515 | 0.6736 | 0.6515 | 0.8072 |
137
+ | No log | 1.6075 | 172 | 0.6642 | 0.6516 | 0.6642 | 0.8150 |
138
+ | No log | 1.6262 | 174 | 0.7940 | 0.5932 | 0.7940 | 0.8911 |
139
+ | No log | 1.6449 | 176 | 1.0159 | 0.5637 | 1.0159 | 1.0079 |
140
+ | No log | 1.6636 | 178 | 0.9652 | 0.5751 | 0.9652 | 0.9825 |
141
+ | No log | 1.6822 | 180 | 0.9152 | 0.5751 | 0.9152 | 0.9566 |
142
+ | No log | 1.7009 | 182 | 0.7218 | 0.6245 | 0.7218 | 0.8496 |
143
+ | No log | 1.7196 | 184 | 0.6519 | 0.6637 | 0.6519 | 0.8074 |
144
+ | No log | 1.7383 | 186 | 0.6563 | 0.6619 | 0.6563 | 0.8101 |
145
+ | No log | 1.7570 | 188 | 0.7540 | 0.6167 | 0.7540 | 0.8684 |
146
+ | No log | 1.7757 | 190 | 0.9572 | 0.5564 | 0.9572 | 0.9783 |
147
+ | No log | 1.7944 | 192 | 0.8626 | 0.5584 | 0.8626 | 0.9288 |
148
+ | No log | 1.8131 | 194 | 0.6946 | 0.6224 | 0.6946 | 0.8334 |
149
+ | No log | 1.8318 | 196 | 0.7091 | 0.5989 | 0.7091 | 0.8421 |
150
+ | No log | 1.8505 | 198 | 0.7525 | 0.5825 | 0.7525 | 0.8674 |
151
+ | No log | 1.8692 | 200 | 0.6880 | 0.6609 | 0.6880 | 0.8294 |
152
+ | No log | 1.8879 | 202 | 0.7236 | 0.6235 | 0.7236 | 0.8507 |
153
+ | No log | 1.9065 | 204 | 0.8839 | 0.5584 | 0.8839 | 0.9402 |
154
+ | No log | 1.9252 | 206 | 0.8501 | 0.5614 | 0.8501 | 0.9220 |
155
+ | No log | 1.9439 | 208 | 0.7564 | 0.5359 | 0.7564 | 0.8697 |
156
+ | No log | 1.9626 | 210 | 0.7212 | 0.5069 | 0.7212 | 0.8492 |
157
+ | No log | 1.9813 | 212 | 0.7238 | 0.4994 | 0.7238 | 0.8507 |
158
+ | No log | 2.0 | 214 | 0.7327 | 0.5941 | 0.7327 | 0.8560 |
159
+ | No log | 2.0187 | 216 | 0.7425 | 0.6286 | 0.7425 | 0.8617 |
160
+ | No log | 2.0374 | 218 | 0.7449 | 0.6722 | 0.7449 | 0.8631 |
161
+ | No log | 2.0561 | 220 | 1.0873 | 0.5683 | 1.0873 | 1.0427 |
162
+ | No log | 2.0748 | 222 | 1.4994 | 0.4505 | 1.4994 | 1.2245 |
163
+ | No log | 2.0935 | 224 | 1.4575 | 0.4259 | 1.4575 | 1.2073 |
164
+ | No log | 2.1121 | 226 | 1.1224 | 0.5710 | 1.1224 | 1.0594 |
165
+ | No log | 2.1308 | 228 | 0.7642 | 0.6426 | 0.7642 | 0.8742 |
166
+ | No log | 2.1495 | 230 | 0.6880 | 0.5981 | 0.6880 | 0.8295 |
167
+ | No log | 2.1682 | 232 | 0.7112 | 0.5653 | 0.7112 | 0.8433 |
168
+ | No log | 2.1869 | 234 | 0.6641 | 0.5830 | 0.6641 | 0.8149 |
169
+ | No log | 2.2056 | 236 | 0.6985 | 0.5596 | 0.6985 | 0.8357 |
170
+ | No log | 2.2243 | 238 | 0.7410 | 0.6121 | 0.7410 | 0.8608 |
171
+ | No log | 2.2430 | 240 | 0.7106 | 0.6066 | 0.7106 | 0.8430 |
172
+ | No log | 2.2617 | 242 | 0.7305 | 0.5927 | 0.7305 | 0.8547 |
173
+ | No log | 2.2804 | 244 | 0.7468 | 0.5704 | 0.7468 | 0.8642 |
174
+ | No log | 2.2991 | 246 | 0.7300 | 0.5587 | 0.7300 | 0.8544 |
175
+ | No log | 2.3178 | 248 | 0.6805 | 0.6065 | 0.6805 | 0.8249 |
176
+ | No log | 2.3364 | 250 | 0.6815 | 0.5846 | 0.6815 | 0.8255 |
177
+ | No log | 2.3551 | 252 | 0.6634 | 0.6718 | 0.6634 | 0.8145 |
178
+ | No log | 2.3738 | 254 | 0.7748 | 0.5747 | 0.7748 | 0.8802 |
179
+ | No log | 2.3925 | 256 | 0.8005 | 0.5686 | 0.8005 | 0.8947 |
180
+ | No log | 2.4112 | 258 | 0.7806 | 0.5469 | 0.7806 | 0.8835 |
181
+ | No log | 2.4299 | 260 | 0.7263 | 0.5291 | 0.7263 | 0.8522 |
182
+ | No log | 2.4486 | 262 | 0.7259 | 0.5574 | 0.7259 | 0.8520 |
183
+ | No log | 2.4673 | 264 | 0.8280 | 0.5210 | 0.8280 | 0.9100 |
184
+ | No log | 2.4860 | 266 | 0.8580 | 0.5495 | 0.8580 | 0.9263 |
185
+ | No log | 2.5047 | 268 | 0.7627 | 0.5827 | 0.7627 | 0.8733 |
186
+ | No log | 2.5234 | 270 | 0.7255 | 0.5683 | 0.7255 | 0.8518 |
187
+ | No log | 2.5421 | 272 | 0.7375 | 0.5683 | 0.7375 | 0.8588 |
188
+ | No log | 2.5607 | 274 | 0.8065 | 0.5326 | 0.8065 | 0.8980 |
189
+ | No log | 2.5794 | 276 | 0.9238 | 0.5280 | 0.9238 | 0.9611 |
190
+ | No log | 2.5981 | 278 | 1.0320 | 0.5324 | 1.0320 | 1.0159 |
191
+ | No log | 2.6168 | 280 | 0.9521 | 0.5379 | 0.9521 | 0.9757 |
192
+ | No log | 2.6355 | 282 | 0.8097 | 0.4757 | 0.8097 | 0.8998 |
193
+ | No log | 2.6542 | 284 | 0.7511 | 0.5470 | 0.7511 | 0.8667 |
194
+ | No log | 2.6729 | 286 | 0.7478 | 0.5592 | 0.7478 | 0.8647 |
195
+ | No log | 2.6916 | 288 | 0.7426 | 0.5404 | 0.7426 | 0.8618 |
196
+ | No log | 2.7103 | 290 | 0.7524 | 0.5752 | 0.7524 | 0.8674 |
197
+ | No log | 2.7290 | 292 | 0.8193 | 0.4470 | 0.8193 | 0.9052 |
198
+ | No log | 2.7477 | 294 | 0.8525 | 0.4513 | 0.8525 | 0.9233 |
199
+ | No log | 2.7664 | 296 | 0.8033 | 0.4369 | 0.8033 | 0.8963 |
200
+ | No log | 2.7850 | 298 | 0.7437 | 0.6199 | 0.7437 | 0.8624 |
201
+ | No log | 2.8037 | 300 | 0.7512 | 0.5712 | 0.7512 | 0.8667 |
202
+ | No log | 2.8224 | 302 | 0.7499 | 0.5888 | 0.7499 | 0.8659 |
203
+ | No log | 2.8411 | 304 | 0.7094 | 0.6129 | 0.7094 | 0.8423 |
204
+ | No log | 2.8598 | 306 | 0.7412 | 0.5693 | 0.7412 | 0.8609 |
205
+ | No log | 2.8785 | 308 | 0.8888 | 0.5208 | 0.8888 | 0.9428 |
206
+ | No log | 2.8972 | 310 | 0.9057 | 0.5208 | 0.9057 | 0.9517 |
207
+ | No log | 2.9159 | 312 | 0.8538 | 0.4328 | 0.8538 | 0.9240 |
208
+ | No log | 2.9346 | 314 | 0.8418 | 0.4328 | 0.8418 | 0.9175 |
209
+ | No log | 2.9533 | 316 | 0.7881 | 0.4364 | 0.7881 | 0.8878 |
210
+ | No log | 2.9720 | 318 | 0.7585 | 0.6212 | 0.7585 | 0.8709 |
211
+ | No log | 2.9907 | 320 | 0.7547 | 0.6097 | 0.7547 | 0.8688 |
212
+ | No log | 3.0093 | 322 | 0.7585 | 0.6218 | 0.7585 | 0.8709 |
213
+ | No log | 3.0280 | 324 | 0.8208 | 0.5581 | 0.8208 | 0.9060 |
214
+ | No log | 3.0467 | 326 | 0.8633 | 0.4879 | 0.8633 | 0.9291 |
215
+ | No log | 3.0654 | 328 | 0.8824 | 0.4754 | 0.8824 | 0.9394 |
216
+ | No log | 3.0841 | 330 | 0.8878 | 0.4107 | 0.8878 | 0.9422 |
217
+ | No log | 3.1028 | 332 | 0.8655 | 0.3632 | 0.8655 | 0.9303 |
218
+ | No log | 3.1215 | 334 | 0.8483 | 0.4572 | 0.8483 | 0.9210 |
219
+ | No log | 3.1402 | 336 | 0.7967 | 0.5702 | 0.7967 | 0.8926 |
220
+ | No log | 3.1589 | 338 | 0.7524 | 0.6109 | 0.7524 | 0.8674 |
221
+ | No log | 3.1776 | 340 | 0.7382 | 0.6277 | 0.7382 | 0.8592 |
222
+ | No log | 3.1963 | 342 | 0.7411 | 0.6420 | 0.7411 | 0.8609 |
223
+ | No log | 3.2150 | 344 | 0.7355 | 0.7001 | 0.7355 | 0.8576 |
224
+ | No log | 3.2336 | 346 | 0.7300 | 0.6128 | 0.7300 | 0.8544 |
225
+ | No log | 3.2523 | 348 | 0.7420 | 0.6206 | 0.7420 | 0.8614 |
226
+ | No log | 3.2710 | 350 | 0.7524 | 0.5658 | 0.7524 | 0.8674 |
227
+ | No log | 3.2897 | 352 | 0.7430 | 0.5474 | 0.7430 | 0.8620 |
228
+ | No log | 3.3084 | 354 | 0.7682 | 0.4444 | 0.7682 | 0.8765 |
229
+ | No log | 3.3271 | 356 | 0.8167 | 0.4142 | 0.8167 | 0.9037 |
230
+ | No log | 3.3458 | 358 | 0.8538 | 0.4394 | 0.8538 | 0.9240 |
231
+ | No log | 3.3645 | 360 | 0.8231 | 0.5077 | 0.8231 | 0.9072 |
232
+ | No log | 3.3832 | 362 | 0.7618 | 0.5501 | 0.7618 | 0.8728 |
233
+ | No log | 3.4019 | 364 | 0.7508 | 0.5892 | 0.7508 | 0.8665 |
234
+ | No log | 3.4206 | 366 | 0.7568 | 0.5547 | 0.7568 | 0.8700 |
235
+ | No log | 3.4393 | 368 | 0.7491 | 0.5797 | 0.7491 | 0.8655 |
236
+ | No log | 3.4579 | 370 | 0.7581 | 0.5571 | 0.7581 | 0.8707 |
237
+ | No log | 3.4766 | 372 | 0.8279 | 0.5556 | 0.8279 | 0.9099 |
238
+ | No log | 3.4953 | 374 | 0.8771 | 0.5614 | 0.8771 | 0.9365 |
239
+ | No log | 3.5140 | 376 | 0.8253 | 0.5683 | 0.8253 | 0.9084 |
240
+ | No log | 3.5327 | 378 | 0.7484 | 0.5523 | 0.7484 | 0.8651 |
241
+ | No log | 3.5514 | 380 | 0.7120 | 0.6010 | 0.7120 | 0.8438 |
242
+ | No log | 3.5701 | 382 | 0.7251 | 0.5660 | 0.7251 | 0.8516 |
243
+ | No log | 3.5888 | 384 | 0.8394 | 0.5814 | 0.8394 | 0.9162 |
244
+ | No log | 3.6075 | 386 | 0.9848 | 0.5605 | 0.9848 | 0.9924 |
245
+ | No log | 3.6262 | 388 | 0.9667 | 0.5530 | 0.9667 | 0.9832 |
246
+ | No log | 3.6449 | 390 | 0.8226 | 0.5658 | 0.8226 | 0.9070 |
247
+ | No log | 3.6636 | 392 | 0.7204 | 0.5645 | 0.7204 | 0.8488 |
248
+ | No log | 3.6822 | 394 | 0.7049 | 0.6206 | 0.7049 | 0.8396 |
249
+ | No log | 3.7009 | 396 | 0.6960 | 0.6206 | 0.6960 | 0.8343 |
250
+ | No log | 3.7196 | 398 | 0.6868 | 0.6718 | 0.6868 | 0.8287 |
251
+ | No log | 3.7383 | 400 | 0.7279 | 0.5801 | 0.7279 | 0.8532 |
252
+ | No log | 3.7570 | 402 | 0.7546 | 0.5026 | 0.7546 | 0.8687 |
253
+ | No log | 3.7757 | 404 | 0.7738 | 0.5586 | 0.7738 | 0.8797 |
254
+ | No log | 3.7944 | 406 | 0.7313 | 0.5801 | 0.7313 | 0.8551 |
255
+ | No log | 3.8131 | 408 | 0.6802 | 0.6702 | 0.6802 | 0.8247 |
256
+ | No log | 3.8318 | 410 | 0.6792 | 0.6957 | 0.6792 | 0.8241 |
257
+ | No log | 3.8505 | 412 | 0.6678 | 0.6312 | 0.6678 | 0.8172 |
258
+ | No log | 3.8692 | 414 | 0.6786 | 0.6312 | 0.6786 | 0.8237 |
259
+ | No log | 3.8879 | 416 | 0.6877 | 0.6344 | 0.6877 | 0.8293 |
260
+ | No log | 3.9065 | 418 | 0.6947 | 0.6029 | 0.6947 | 0.8335 |
261
+ | No log | 3.9252 | 420 | 0.7179 | 0.6302 | 0.7179 | 0.8473 |
262
+ | No log | 3.9439 | 422 | 0.7313 | 0.5892 | 0.7313 | 0.8551 |
263
+ | No log | 3.9626 | 424 | 0.7957 | 0.5712 | 0.7957 | 0.8920 |
264
+ | No log | 3.9813 | 426 | 0.8376 | 0.5913 | 0.8376 | 0.9152 |
265
+ | No log | 4.0 | 428 | 0.7631 | 0.5706 | 0.7631 | 0.8735 |
266
+ | No log | 4.0187 | 430 | 0.7443 | 0.5587 | 0.7443 | 0.8627 |
267
+ | No log | 4.0374 | 432 | 0.7315 | 0.5295 | 0.7315 | 0.8553 |
268
+ | No log | 4.0561 | 434 | 0.7192 | 0.5413 | 0.7192 | 0.8481 |
269
+ | No log | 4.0748 | 436 | 0.7549 | 0.5914 | 0.7549 | 0.8689 |
270
+ | No log | 4.0935 | 438 | 0.8840 | 0.5791 | 0.8840 | 0.9402 |
271
+ | No log | 4.1121 | 440 | 0.9597 | 0.5567 | 0.9597 | 0.9796 |
272
+ | No log | 4.1308 | 442 | 0.9966 | 0.5567 | 0.9966 | 0.9983 |
273
+ | No log | 4.1495 | 444 | 0.9727 | 0.5443 | 0.9727 | 0.9862 |
274
+ | No log | 4.1682 | 446 | 0.8682 | 0.5498 | 0.8682 | 0.9318 |
275
+ | No log | 4.1869 | 448 | 0.7888 | 0.5264 | 0.7888 | 0.8881 |
276
+ | No log | 4.2056 | 450 | 0.7894 | 0.5098 | 0.7894 | 0.8885 |
277
+ | No log | 4.2243 | 452 | 0.8226 | 0.5022 | 0.8226 | 0.9070 |
278
+ | No log | 4.2430 | 454 | 0.7916 | 0.5009 | 0.7916 | 0.8897 |
279
+ | No log | 4.2617 | 456 | 0.7990 | 0.4877 | 0.7990 | 0.8939 |
280
+ | No log | 4.2804 | 458 | 0.9364 | 0.5614 | 0.9364 | 0.9677 |
281
+ | No log | 4.2991 | 460 | 1.0472 | 0.5649 | 1.0472 | 1.0233 |
282
+ | No log | 4.3178 | 462 | 1.0383 | 0.5649 | 1.0383 | 1.0190 |
283
+ | No log | 4.3364 | 464 | 0.9462 | 0.5405 | 0.9462 | 0.9727 |
284
+ | No log | 4.3551 | 466 | 0.8734 | 0.4335 | 0.8734 | 0.9345 |
285
+ | No log | 4.3738 | 468 | 0.8444 | 0.4364 | 0.8444 | 0.9189 |
286
+ | No log | 4.3925 | 470 | 0.8090 | 0.5126 | 0.8090 | 0.8994 |
287
+ | No log | 4.4112 | 472 | 0.7907 | 0.5126 | 0.7907 | 0.8892 |
288
+ | No log | 4.4299 | 474 | 0.8195 | 0.4982 | 0.8195 | 0.9053 |
289
+ | No log | 4.4486 | 476 | 0.8560 | 0.4579 | 0.8560 | 0.9252 |
290
+ | No log | 4.4673 | 478 | 0.8706 | 0.4808 | 0.8706 | 0.9331 |
291
+ | No log | 4.4860 | 480 | 0.8521 | 0.5184 | 0.8521 | 0.9231 |
292
+ | No log | 4.5047 | 482 | 0.8453 | 0.4757 | 0.8453 | 0.9194 |
293
+ | No log | 4.5234 | 484 | 0.8408 | 0.4757 | 0.8408 | 0.9169 |
294
+ | No log | 4.5421 | 486 | 0.8057 | 0.4685 | 0.8057 | 0.8976 |
295
+ | No log | 4.5607 | 488 | 0.8012 | 0.4729 | 0.8012 | 0.8951 |
296
+ | No log | 4.5794 | 490 | 0.8360 | 0.5550 | 0.8360 | 0.9143 |
297
+ | No log | 4.5981 | 492 | 0.8987 | 0.5320 | 0.8987 | 0.9480 |
298
+ | No log | 4.6168 | 494 | 0.9088 | 0.5105 | 0.9088 | 0.9533 |
299
+ | No log | 4.6355 | 496 | 0.8803 | 0.5180 | 0.8803 | 0.9382 |
300
+ | No log | 4.6542 | 498 | 0.8069 | 0.5487 | 0.8069 | 0.8983 |
301
+ | 0.3386 | 4.6729 | 500 | 0.7641 | 0.6095 | 0.7641 | 0.8741 |
302
+ | 0.3386 | 4.6916 | 502 | 0.7546 | 0.6095 | 0.7546 | 0.8687 |
303
+ | 0.3386 | 4.7103 | 504 | 0.7642 | 0.5443 | 0.7642 | 0.8742 |
304
+ | 0.3386 | 4.7290 | 506 | 0.8479 | 0.5196 | 0.8479 | 0.9208 |
305
+ | 0.3386 | 4.7477 | 508 | 0.9612 | 0.5334 | 0.9612 | 0.9804 |
306
+ | 0.3386 | 4.7664 | 510 | 1.0323 | 0.5626 | 1.0323 | 1.0160 |
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 sha256:8b68aeeaaaec64460ca22abd453f5f3250fa5b61c783f5fda1b7fd15f3d9360a
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d19c71a70df3ef871a3ef2a100bd0037486f9f7ac14b954d38ff57e709d1af09
3
+ size 5304