Hugging Face's logo

MayBashendy
/
ArabicNewSplits7_B_usingWellWrittenEssays_FineTuningAraBERT_run3_AugV5_k8_task1_organization

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
 Community
MayBashendy commited on
Commit
e523e83
·
verified ·
1 Parent(s): d2f70a8

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_task1_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_task1_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: 0.8389
19
+ - Qwk: 0.6370
20
+ - Mse: 0.8389
21
+ - Rmse: 0.9159
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 | 0.0556 | 2 | 6.9265 | 0.0057 | 6.9265 | 2.6318 |
53
+ | No log | 0.1111 | 4 | 4.5040 | 0.0494 | 4.5040 | 2.1223 |
54
+ | No log | 0.1667 | 6 | 3.4885 | -0.0227 | 3.4885 | 1.8677 |
55
+ | No log | 0.2222 | 8 | 2.7959 | 0.0580 | 2.7959 | 1.6721 |
56
+ | No log | 0.2778 | 10 | 2.1314 | 0.0645 | 2.1314 | 1.4599 |
57
+ | No log | 0.3333 | 12 | 1.9667 | 0.1930 | 1.9667 | 1.4024 |
58
+ | No log | 0.3889 | 14 | 1.8264 | 0.1538 | 1.8264 | 1.3515 |
59
+ | No log | 0.4444 | 16 | 1.9369 | 0.1165 | 1.9369 | 1.3917 |
60
+ | No log | 0.5 | 18 | 2.1547 | 0.0926 | 2.1547 | 1.4679 |
61
+ | No log | 0.5556 | 20 | 2.1594 | 0.0885 | 2.1594 | 1.4695 |
62
+ | No log | 0.6111 | 22 | 2.1192 | 0.1724 | 2.1192 | 1.4558 |
63
+ | No log | 0.6667 | 24 | 1.9755 | 0.1698 | 1.9755 | 1.4055 |
64
+ | No log | 0.7222 | 26 | 1.9165 | 0.1165 | 1.9165 | 1.3844 |
65
+ | No log | 0.7778 | 28 | 1.8231 | 0.1165 | 1.8231 | 1.3502 |
66
+ | No log | 0.8333 | 30 | 1.7344 | 0.0396 | 1.7344 | 1.3170 |
67
+ | No log | 0.8889 | 32 | 1.7884 | 0.0396 | 1.7884 | 1.3373 |
68
+ | No log | 0.9444 | 34 | 1.8749 | 0.0396 | 1.8749 | 1.3693 |
69
+ | No log | 1.0 | 36 | 1.8947 | 0.0784 | 1.8947 | 1.3765 |
70
+ | No log | 1.0556 | 38 | 1.8032 | 0.0396 | 1.8032 | 1.3428 |
71
+ | No log | 1.1111 | 40 | 1.7262 | 0.0396 | 1.7262 | 1.3138 |
72
+ | No log | 1.1667 | 42 | 1.6455 | 0.0784 | 1.6455 | 1.2828 |
73
+ | No log | 1.2222 | 44 | 1.6801 | 0.2056 | 1.6801 | 1.2962 |
74
+ | No log | 1.2778 | 46 | 1.7355 | 0.3448 | 1.7355 | 1.3174 |
75
+ | No log | 1.3333 | 48 | 1.8064 | 0.3065 | 1.8064 | 1.3440 |
76
+ | No log | 1.3889 | 50 | 1.7273 | 0.3252 | 1.7273 | 1.3143 |
77
+ | No log | 1.4444 | 52 | 1.5169 | 0.2703 | 1.5169 | 1.2316 |
78
+ | No log | 1.5 | 54 | 1.4735 | 0.1165 | 1.4735 | 1.2139 |
79
+ | No log | 1.5556 | 56 | 1.5190 | 0.2385 | 1.5190 | 1.2325 |
80
+ | No log | 1.6111 | 58 | 1.4499 | 0.2752 | 1.4499 | 1.2041 |
81
+ | No log | 1.6667 | 60 | 1.3909 | 0.2075 | 1.3909 | 1.1794 |
82
+ | No log | 1.7222 | 62 | 1.3979 | 0.3478 | 1.3979 | 1.1823 |
83
+ | No log | 1.7778 | 64 | 1.5850 | 0.3040 | 1.5850 | 1.2590 |
84
+ | No log | 1.8333 | 66 | 1.6390 | 0.3307 | 1.6390 | 1.2802 |
85
+ | No log | 1.8889 | 68 | 1.5199 | 0.4160 | 1.5199 | 1.2329 |
86
+ | No log | 1.9444 | 70 | 1.3568 | 0.4103 | 1.3568 | 1.1648 |
87
+ | No log | 2.0 | 72 | 1.3257 | 0.2778 | 1.3257 | 1.1514 |
88
+ | No log | 2.0556 | 74 | 1.3453 | 0.2430 | 1.3453 | 1.1599 |
89
+ | No log | 2.1111 | 76 | 1.3276 | 0.2430 | 1.3276 | 1.1522 |
90
+ | No log | 2.1667 | 78 | 1.2935 | 0.2430 | 1.2935 | 1.1373 |
91
+ | No log | 2.2222 | 80 | 1.2765 | 0.3091 | 1.2765 | 1.1298 |
92
+ | No log | 2.2778 | 82 | 1.2431 | 0.3063 | 1.2431 | 1.1150 |
93
+ | No log | 2.3333 | 84 | 1.2196 | 0.2909 | 1.2196 | 1.1044 |
94
+ | No log | 2.3889 | 86 | 1.2156 | 0.3036 | 1.2156 | 1.1025 |
95
+ | No log | 2.4444 | 88 | 1.2170 | 0.4237 | 1.2170 | 1.1032 |
96
+ | No log | 2.5 | 90 | 1.1814 | 0.3684 | 1.1814 | 1.0869 |
97
+ | No log | 2.5556 | 92 | 1.1623 | 0.3793 | 1.1623 | 1.0781 |
98
+ | No log | 2.6111 | 94 | 1.1335 | 0.4426 | 1.1335 | 1.0647 |
99
+ | No log | 2.6667 | 96 | 1.0998 | 0.4426 | 1.0998 | 1.0487 |
100
+ | No log | 2.7222 | 98 | 1.0710 | 0.4715 | 1.0710 | 1.0349 |
101
+ | No log | 2.7778 | 100 | 1.0404 | 0.5827 | 1.0404 | 1.0200 |
102
+ | No log | 2.8333 | 102 | 1.1062 | 0.5528 | 1.1062 | 1.0518 |
103
+ | No log | 2.8889 | 104 | 1.2620 | 0.4677 | 1.2620 | 1.1234 |
104
+ | No log | 2.9444 | 106 | 1.3304 | 0.5156 | 1.3304 | 1.1534 |
105
+ | No log | 3.0 | 108 | 1.2870 | 0.5 | 1.2870 | 1.1344 |
106
+ | No log | 3.0556 | 110 | 1.1946 | 0.5984 | 1.1946 | 1.0930 |
107
+ | No log | 3.1111 | 112 | 1.0777 | 0.608 | 1.0777 | 1.0381 |
108
+ | No log | 3.1667 | 114 | 0.9607 | 0.6299 | 0.9607 | 0.9802 |
109
+ | No log | 3.2222 | 116 | 0.9083 | 0.6142 | 0.9083 | 0.9531 |
110
+ | No log | 3.2778 | 118 | 0.9013 | 0.6562 | 0.9013 | 0.9494 |
111
+ | No log | 3.3333 | 120 | 1.0614 | 0.5909 | 1.0614 | 1.0302 |
112
+ | No log | 3.3889 | 122 | 1.1111 | 0.5606 | 1.1111 | 1.0541 |
113
+ | No log | 3.4444 | 124 | 0.9810 | 0.5909 | 0.9810 | 0.9905 |
114
+ | No log | 3.5 | 126 | 0.9082 | 0.6519 | 0.9082 | 0.9530 |
115
+ | No log | 3.5556 | 128 | 0.9778 | 0.5714 | 0.9778 | 0.9888 |
116
+ | No log | 3.6111 | 130 | 1.1034 | 0.5564 | 1.1034 | 1.0504 |
117
+ | No log | 3.6667 | 132 | 1.0712 | 0.5760 | 1.0712 | 1.0350 |
118
+ | No log | 3.7222 | 134 | 1.0509 | 0.5806 | 1.0509 | 1.0251 |
119
+ | No log | 3.7778 | 136 | 1.0079 | 0.6080 | 1.0079 | 1.0039 |
120
+ | No log | 3.8333 | 138 | 0.8070 | 0.6617 | 0.8070 | 0.8983 |
121
+ | No log | 3.8889 | 140 | 0.7281 | 0.7050 | 0.7281 | 0.8533 |
122
+ | No log | 3.9444 | 142 | 0.8301 | 0.6423 | 0.8301 | 0.9111 |
123
+ | No log | 4.0 | 144 | 0.8466 | 0.6418 | 0.8466 | 0.9201 |
124
+ | No log | 4.0556 | 146 | 0.7829 | 0.6767 | 0.7829 | 0.8848 |
125
+ | No log | 4.1111 | 148 | 0.7478 | 0.6667 | 0.7478 | 0.8647 |
126
+ | No log | 4.1667 | 150 | 0.7990 | 0.6667 | 0.7990 | 0.8939 |
127
+ | No log | 4.2222 | 152 | 0.8297 | 0.6667 | 0.8297 | 0.9109 |
128
+ | No log | 4.2778 | 154 | 0.9195 | 0.6716 | 0.9195 | 0.9589 |
129
+ | No log | 4.3333 | 156 | 0.9721 | 0.6466 | 0.9721 | 0.9860 |
130
+ | No log | 4.3889 | 158 | 0.9312 | 0.6308 | 0.9312 | 0.9650 |
131
+ | No log | 4.4444 | 160 | 0.9117 | 0.6861 | 0.9117 | 0.9548 |
132
+ | No log | 4.5 | 162 | 0.8837 | 0.6957 | 0.8837 | 0.9400 |
133
+ | No log | 4.5556 | 164 | 0.7650 | 0.6861 | 0.7650 | 0.8747 |
134
+ | No log | 4.6111 | 166 | 0.7931 | 0.7101 | 0.7931 | 0.8906 |
135
+ | No log | 4.6667 | 168 | 0.9629 | 0.6232 | 0.9629 | 0.9813 |
136
+ | No log | 4.7222 | 170 | 0.9007 | 0.6423 | 0.9007 | 0.9490 |
137
+ | No log | 4.7778 | 172 | 0.7518 | 0.6715 | 0.7518 | 0.8670 |
138
+ | No log | 4.8333 | 174 | 0.7321 | 0.6715 | 0.7321 | 0.8556 |
139
+ | No log | 4.8889 | 176 | 0.7737 | 0.6765 | 0.7737 | 0.8796 |
140
+ | No log | 4.9444 | 178 | 0.8352 | 0.6567 | 0.8352 | 0.9139 |
141
+ | No log | 5.0 | 180 | 0.9737 | 0.6119 | 0.9737 | 0.9868 |
142
+ | No log | 5.0556 | 182 | 1.0310 | 0.5970 | 1.0310 | 1.0154 |
143
+ | No log | 5.1111 | 184 | 0.9467 | 0.6519 | 0.9467 | 0.9730 |
144
+ | No log | 5.1667 | 186 | 1.0042 | 0.6286 | 1.0042 | 1.0021 |
145
+ | No log | 5.2222 | 188 | 0.9869 | 0.6483 | 0.9869 | 0.9934 |
146
+ | No log | 5.2778 | 190 | 0.9611 | 0.6099 | 0.9611 | 0.9804 |
147
+ | No log | 5.3333 | 192 | 1.0662 | 0.5839 | 1.0662 | 1.0326 |
148
+ | No log | 5.3889 | 194 | 1.0689 | 0.5344 | 1.0689 | 1.0339 |
149
+ | No log | 5.4444 | 196 | 1.1336 | 0.5077 | 1.1336 | 1.0647 |
150
+ | No log | 5.5 | 198 | 1.1165 | 0.5414 | 1.1165 | 1.0566 |
151
+ | No log | 5.5556 | 200 | 1.1400 | 0.5672 | 1.1400 | 1.0677 |
152
+ | No log | 5.6111 | 202 | 0.9423 | 0.6423 | 0.9423 | 0.9707 |
153
+ | No log | 5.6667 | 204 | 0.8025 | 0.6667 | 0.8025 | 0.8958 |
154
+ | No log | 5.7222 | 206 | 0.7912 | 0.6912 | 0.7912 | 0.8895 |
155
+ | No log | 5.7778 | 208 | 0.8354 | 0.6716 | 0.8354 | 0.9140 |
156
+ | No log | 5.8333 | 210 | 0.9494 | 0.6567 | 0.9494 | 0.9744 |
157
+ | No log | 5.8889 | 212 | 1.0092 | 0.6466 | 1.0092 | 1.0046 |
158
+ | No log | 5.9444 | 214 | 1.0025 | 0.6423 | 1.0025 | 1.0012 |
159
+ | No log | 6.0 | 216 | 0.9806 | 0.6475 | 0.9806 | 0.9902 |
160
+ | No log | 6.0556 | 218 | 0.8635 | 0.6765 | 0.8635 | 0.9293 |
161
+ | No log | 6.1111 | 220 | 0.6756 | 0.7324 | 0.6756 | 0.8219 |
162
+ | No log | 6.1667 | 222 | 0.6330 | 0.7234 | 0.6330 | 0.7956 |
163
+ | No log | 6.2222 | 224 | 0.6744 | 0.7194 | 0.6744 | 0.8212 |
164
+ | No log | 6.2778 | 226 | 0.7810 | 0.7 | 0.7810 | 0.8838 |
165
+ | No log | 6.3333 | 228 | 0.7493 | 0.7 | 0.7493 | 0.8656 |
166
+ | No log | 6.3889 | 230 | 0.6737 | 0.7101 | 0.6737 | 0.8208 |
167
+ | No log | 6.4444 | 232 | 0.6563 | 0.7059 | 0.6563 | 0.8101 |
168
+ | No log | 6.5 | 234 | 0.6797 | 0.7194 | 0.6797 | 0.8245 |
169
+ | No log | 6.5556 | 236 | 0.7396 | 0.7101 | 0.7396 | 0.8600 |
170
+ | No log | 6.6111 | 238 | 0.7939 | 0.6809 | 0.7939 | 0.8910 |
171
+ | No log | 6.6667 | 240 | 0.8458 | 0.6667 | 0.8458 | 0.9197 |
172
+ | No log | 6.7222 | 242 | 0.8934 | 0.6571 | 0.8934 | 0.9452 |
173
+ | No log | 6.7778 | 244 | 1.0311 | 0.6622 | 1.0311 | 1.0154 |
174
+ | No log | 6.8333 | 246 | 1.1054 | 0.6303 | 1.1054 | 1.0514 |
175
+ | No log | 6.8889 | 248 | 0.9515 | 0.6711 | 0.9515 | 0.9754 |
176
+ | No log | 6.9444 | 250 | 0.8437 | 0.6714 | 0.8437 | 0.9185 |
177
+ | No log | 7.0 | 252 | 0.7892 | 0.6567 | 0.7892 | 0.8884 |
178
+ | No log | 7.0556 | 254 | 0.7573 | 0.6667 | 0.7573 | 0.8702 |
179
+ | No log | 7.1111 | 256 | 0.7145 | 0.6767 | 0.7145 | 0.8453 |
180
+ | No log | 7.1667 | 258 | 0.7093 | 0.7536 | 0.7093 | 0.8422 |
181
+ | No log | 7.2222 | 260 | 0.7189 | 0.7429 | 0.7189 | 0.8479 |
182
+ | No log | 7.2778 | 262 | 0.6446 | 0.8028 | 0.6446 | 0.8029 |
183
+ | No log | 7.3333 | 264 | 0.6629 | 0.7310 | 0.6629 | 0.8142 |
184
+ | No log | 7.3889 | 266 | 0.8967 | 0.6479 | 0.8967 | 0.9470 |
185
+ | No log | 7.4444 | 268 | 0.9119 | 0.6575 | 0.9119 | 0.9549 |
186
+ | No log | 7.5 | 270 | 0.7286 | 0.6944 | 0.7286 | 0.8536 |
187
+ | No log | 7.5556 | 272 | 0.6186 | 0.7838 | 0.6186 | 0.7865 |
188
+ | No log | 7.6111 | 274 | 0.7451 | 0.7355 | 0.7451 | 0.8632 |
189
+ | No log | 7.6667 | 276 | 0.6858 | 0.7895 | 0.6858 | 0.8281 |
190
+ | No log | 7.7222 | 278 | 0.5898 | 0.7808 | 0.5898 | 0.7680 |
191
+ | No log | 7.7778 | 280 | 0.7154 | 0.7246 | 0.7154 | 0.8458 |
192
+ | No log | 7.8333 | 282 | 0.9763 | 0.6423 | 0.9763 | 0.9881 |
193
+ | No log | 7.8889 | 284 | 1.0511 | 0.6515 | 1.0511 | 1.0252 |
194
+ | No log | 7.9444 | 286 | 1.0605 | 0.6466 | 1.0605 | 1.0298 |
195
+ | No log | 8.0 | 288 | 0.9878 | 0.6466 | 0.9878 | 0.9939 |
196
+ | No log | 8.0556 | 290 | 0.8454 | 0.6763 | 0.8454 | 0.9194 |
197
+ | No log | 8.1111 | 292 | 0.7212 | 0.6812 | 0.7212 | 0.8492 |
198
+ | No log | 8.1667 | 294 | 0.6267 | 0.7324 | 0.6267 | 0.7916 |
199
+ | No log | 8.2222 | 296 | 0.6164 | 0.7917 | 0.6164 | 0.7851 |
200
+ | No log | 8.2778 | 298 | 0.6639 | 0.7586 | 0.6639 | 0.8148 |
201
+ | No log | 8.3333 | 300 | 0.6976 | 0.7361 | 0.6976 | 0.8352 |
202
+ | No log | 8.3889 | 302 | 0.6821 | 0.7376 | 0.6821 | 0.8259 |
203
+ | No log | 8.4444 | 304 | 0.6845 | 0.7246 | 0.6845 | 0.8273 |
204
+ | No log | 8.5 | 306 | 0.7363 | 0.6950 | 0.7363 | 0.8581 |
205
+ | No log | 8.5556 | 308 | 0.7921 | 0.6853 | 0.7921 | 0.8900 |
206
+ | No log | 8.6111 | 310 | 0.9030 | 0.6389 | 0.9030 | 0.9503 |
207
+ | No log | 8.6667 | 312 | 0.8652 | 0.6479 | 0.8652 | 0.9302 |
208
+ | No log | 8.7222 | 314 | 0.7990 | 0.6377 | 0.7990 | 0.8939 |
209
+ | No log | 8.7778 | 316 | 0.7888 | 0.6370 | 0.7888 | 0.8881 |
210
+ | No log | 8.8333 | 318 | 0.8021 | 0.6471 | 0.8021 | 0.8956 |
211
+ | No log | 8.8889 | 320 | 0.8577 | 0.6377 | 0.8577 | 0.9261 |
212
+ | No log | 8.9444 | 322 | 0.9197 | 0.5899 | 0.9197 | 0.9590 |
213
+ | No log | 9.0 | 324 | 0.9176 | 0.6015 | 0.9176 | 0.9579 |
214
+ | No log | 9.0556 | 326 | 0.8809 | 0.6370 | 0.8809 | 0.9386 |
215
+ | No log | 9.1111 | 328 | 0.8153 | 0.6308 | 0.8153 | 0.9029 |
216
+ | No log | 9.1667 | 330 | 0.7893 | 0.6618 | 0.7893 | 0.8884 |
217
+ | No log | 9.2222 | 332 | 0.8311 | 0.6857 | 0.8311 | 0.9116 |
218
+ | No log | 9.2778 | 334 | 0.9005 | 0.6667 | 0.9005 | 0.9489 |
219
+ | No log | 9.3333 | 336 | 0.8674 | 0.6667 | 0.8674 | 0.9313 |
220
+ | No log | 9.3889 | 338 | 0.7626 | 0.7260 | 0.7626 | 0.8733 |
221
+ | No log | 9.4444 | 340 | 0.7002 | 0.7391 | 0.7002 | 0.8368 |
222
+ | No log | 9.5 | 342 | 0.6851 | 0.7482 | 0.6851 | 0.8277 |
223
+ | No log | 9.5556 | 344 | 0.6952 | 0.7714 | 0.6952 | 0.8338 |
224
+ | No log | 9.6111 | 346 | 0.6866 | 0.7465 | 0.6866 | 0.8286 |
225
+ | No log | 9.6667 | 348 | 0.7392 | 0.7067 | 0.7392 | 0.8598 |
226
+ | No log | 9.7222 | 350 | 0.8197 | 0.7006 | 0.8197 | 0.9054 |
227
+ | No log | 9.7778 | 352 | 0.8433 | 0.6712 | 0.8433 | 0.9183 |
228
+ | No log | 9.8333 | 354 | 0.8703 | 0.6423 | 0.8703 | 0.9329 |
229
+ | No log | 9.8889 | 356 | 0.8392 | 0.6423 | 0.8392 | 0.9161 |
230
+ | No log | 9.9444 | 358 | 0.8713 | 0.6963 | 0.8713 | 0.9334 |
231
+ | No log | 10.0 | 360 | 0.8282 | 0.7206 | 0.8282 | 0.9101 |
232
+ | No log | 10.0556 | 362 | 0.8927 | 0.6567 | 0.8927 | 0.9448 |
233
+ | No log | 10.1111 | 364 | 0.9405 | 0.6043 | 0.9405 | 0.9698 |
234
+ | No log | 10.1667 | 366 | 0.8757 | 0.6316 | 0.8757 | 0.9358 |
235
+ | No log | 10.2222 | 368 | 0.7799 | 0.6667 | 0.7799 | 0.8831 |
236
+ | No log | 10.2778 | 370 | 0.7583 | 0.6567 | 0.7583 | 0.8708 |
237
+ | No log | 10.3333 | 372 | 0.7854 | 0.6471 | 0.7854 | 0.8862 |
238
+ | No log | 10.3889 | 374 | 0.7861 | 0.6475 | 0.7861 | 0.8866 |
239
+ | No log | 10.4444 | 376 | 0.7513 | 0.6986 | 0.7513 | 0.8668 |
240
+ | No log | 10.5 | 378 | 0.6781 | 0.7682 | 0.6781 | 0.8235 |
241
+ | No log | 10.5556 | 380 | 0.5830 | 0.7482 | 0.5830 | 0.7635 |
242
+ | No log | 10.6111 | 382 | 0.5930 | 0.7606 | 0.5930 | 0.7700 |
243
+ | No log | 10.6667 | 384 | 0.7155 | 0.7020 | 0.7155 | 0.8459 |
244
+ | No log | 10.7222 | 386 | 0.8568 | 0.6623 | 0.8568 | 0.9256 |
245
+ | No log | 10.7778 | 388 | 0.8602 | 0.64 | 0.8602 | 0.9275 |
246
+ | No log | 10.8333 | 390 | 0.7536 | 0.6857 | 0.7536 | 0.8681 |
247
+ | No log | 10.8889 | 392 | 0.7544 | 0.6471 | 0.7544 | 0.8685 |
248
+ | No log | 10.9444 | 394 | 0.8894 | 0.6345 | 0.8894 | 0.9431 |
249
+ | No log | 11.0 | 396 | 1.0778 | 0.6415 | 1.0778 | 1.0382 |
250
+ | No log | 11.0556 | 398 | 1.0513 | 0.6415 | 1.0513 | 1.0253 |
251
+ | No log | 11.1111 | 400 | 0.8970 | 0.625 | 0.8970 | 0.9471 |
252
+ | No log | 11.1667 | 402 | 0.9093 | 0.6099 | 0.9093 | 0.9536 |
253
+ | No log | 11.2222 | 404 | 0.8880 | 0.6338 | 0.8880 | 0.9423 |
254
+ | No log | 11.2778 | 406 | 0.8500 | 0.6383 | 0.8500 | 0.9220 |
255
+ | No log | 11.3333 | 408 | 0.8299 | 0.6619 | 0.8299 | 0.9110 |
256
+ | No log | 11.3889 | 410 | 0.8101 | 0.6957 | 0.8101 | 0.9001 |
257
+ | No log | 11.4444 | 412 | 0.8150 | 0.6815 | 0.8150 | 0.9028 |
258
+ | No log | 11.5 | 414 | 0.8662 | 0.6519 | 0.8662 | 0.9307 |
259
+ | No log | 11.5556 | 416 | 0.9410 | 0.6370 | 0.9410 | 0.9701 |
260
+ | No log | 11.6111 | 418 | 0.9071 | 0.6569 | 0.9071 | 0.9524 |
261
+ | No log | 11.6667 | 420 | 0.8774 | 0.6519 | 0.8774 | 0.9367 |
262
+ | No log | 11.7222 | 422 | 0.8567 | 0.7015 | 0.8567 | 0.9256 |
263
+ | No log | 11.7778 | 424 | 0.8537 | 0.7015 | 0.8537 | 0.9240 |
264
+ | No log | 11.8333 | 426 | 0.8836 | 0.6815 | 0.8836 | 0.9400 |
265
+ | No log | 11.8889 | 428 | 0.8995 | 0.6519 | 0.8995 | 0.9484 |
266
+ | No log | 11.9444 | 430 | 0.9592 | 0.6377 | 0.9592 | 0.9794 |
267
+ | No log | 12.0 | 432 | 0.9946 | 0.6479 | 0.9946 | 0.9973 |
268
+ | No log | 12.0556 | 434 | 1.0803 | 0.6069 | 1.0803 | 1.0394 |
269
+ | No log | 12.1111 | 436 | 1.0948 | 0.6069 | 1.0947 | 1.0463 |
270
+ | No log | 12.1667 | 438 | 1.1358 | 0.5769 | 1.1358 | 1.0657 |
271
+ | No log | 12.2222 | 440 | 1.1858 | 0.5769 | 1.1858 | 1.0889 |
272
+ | No log | 12.2778 | 442 | 1.3476 | 0.5889 | 1.3476 | 1.1609 |
273
+ | No log | 12.3333 | 444 | 1.3663 | 0.5652 | 1.3663 | 1.1689 |
274
+ | No log | 12.3889 | 446 | 1.1417 | 0.6211 | 1.1417 | 1.0685 |
275
+ | No log | 12.4444 | 448 | 0.8596 | 0.6897 | 0.8596 | 0.9271 |
276
+ | No log | 12.5 | 450 | 0.8046 | 0.7172 | 0.8046 | 0.8970 |
277
+ | No log | 12.5556 | 452 | 0.8586 | 0.6853 | 0.8586 | 0.9266 |
278
+ | No log | 12.6111 | 454 | 1.0111 | 0.6358 | 1.0111 | 1.0055 |
279
+ | No log | 12.6667 | 456 | 1.0727 | 0.6053 | 1.0727 | 1.0357 |
280
+ | No log | 12.7222 | 458 | 0.9567 | 0.6479 | 0.9567 | 0.9781 |
281
+ | No log | 12.7778 | 460 | 0.8302 | 0.6471 | 0.8302 | 0.9112 |
282
+ | No log | 12.8333 | 462 | 0.7799 | 0.6765 | 0.7799 | 0.8831 |
283
+ | No log | 12.8889 | 464 | 0.8241 | 0.6519 | 0.8241 | 0.9078 |
284
+ | No log | 12.9444 | 466 | 0.9437 | 0.6434 | 0.9437 | 0.9714 |
285
+ | No log | 13.0 | 468 | 1.1047 | 0.5897 | 1.1047 | 1.0511 |
286
+ | No log | 13.0556 | 470 | 1.0963 | 0.5906 | 1.0963 | 1.0471 |
287
+ | No log | 13.1111 | 472 | 0.9560 | 0.6143 | 0.9560 | 0.9778 |
288
+ | No log | 13.1667 | 474 | 0.8036 | 0.6565 | 0.8036 | 0.8964 |
289
+ | No log | 13.2222 | 476 | 0.7363 | 0.6970 | 0.7363 | 0.8581 |
290
+ | No log | 13.2778 | 478 | 0.7248 | 0.7218 | 0.7248 | 0.8514 |
291
+ | No log | 13.3333 | 480 | 0.7064 | 0.6963 | 0.7064 | 0.8405 |
292
+ | No log | 13.3889 | 482 | 0.7082 | 0.7153 | 0.7082 | 0.8416 |
293
+ | No log | 13.4444 | 484 | 0.7079 | 0.7092 | 0.7079 | 0.8414 |
294
+ | No log | 13.5 | 486 | 0.7063 | 0.7050 | 0.7063 | 0.8404 |
295
+ | No log | 13.5556 | 488 | 0.7590 | 0.6571 | 0.7590 | 0.8712 |
296
+ | No log | 13.6111 | 490 | 0.7595 | 0.6912 | 0.7595 | 0.8715 |
297
+ | No log | 13.6667 | 492 | 0.7941 | 0.6912 | 0.7941 | 0.8911 |
298
+ | No log | 13.7222 | 494 | 0.8511 | 0.6143 | 0.8511 | 0.9225 |
299
+ | No log | 13.7778 | 496 | 0.8279 | 0.6471 | 0.8279 | 0.9099 |
300
+ | No log | 13.8333 | 498 | 0.7526 | 0.6715 | 0.7526 | 0.8675 |
301
+ | 0.4279 | 13.8889 | 500 | 0.7045 | 0.6861 | 0.7045 | 0.8393 |
302
+ | 0.4279 | 13.9444 | 502 | 0.6989 | 0.6765 | 0.6989 | 0.8360 |
303
+ | 0.4279 | 14.0 | 504 | 0.7841 | 0.6301 | 0.7841 | 0.8855 |
304
+ | 0.4279 | 14.0556 | 506 | 0.8660 | 0.6351 | 0.8660 | 0.9306 |
305
+ | 0.4279 | 14.1111 | 508 | 0.8744 | 0.6056 | 0.8744 | 0.9351 |
306
+ | 0.4279 | 14.1667 | 510 | 0.8389 | 0.6370 | 0.8389 | 0.9159 |
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:eeb0ae497fdad5bbf52a659c4ac4bfbe92dd60b36dae8e082a2bc0a5c705a0f2
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f4202cc79de98b7b9d6e446d03bbff777be21c881b16240b76ffa271f31f8a5f
3
+ size 5368