MayBashendy commited on
Commit
341509e
·
verified ·
1 Parent(s): b4a7512

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_usingALLEssays_FineTuningAraBERT_run3_AugV5_k14_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_usingALLEssays_FineTuningAraBERT_run3_AugV5_k14_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: 0.8275
19
+ - Qwk: 0.5138
20
+ - Mse: 0.8275
21
+ - Rmse: 0.9097
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 | 0.0370 | 2 | 4.3722 | -0.0072 | 4.3722 | 2.0910 |
53
+ | No log | 0.0741 | 4 | 2.4101 | 0.0332 | 2.4101 | 1.5524 |
54
+ | No log | 0.1111 | 6 | 1.9484 | 0.1273 | 1.9484 | 1.3958 |
55
+ | No log | 0.1481 | 8 | 2.2133 | 0.1612 | 2.2133 | 1.4877 |
56
+ | No log | 0.1852 | 10 | 1.7216 | 0.1169 | 1.7216 | 1.3121 |
57
+ | No log | 0.2222 | 12 | 1.7450 | 0.1650 | 1.7450 | 1.3210 |
58
+ | No log | 0.2593 | 14 | 1.5303 | 0.1636 | 1.5303 | 1.2371 |
59
+ | No log | 0.2963 | 16 | 1.4452 | 0.0667 | 1.4452 | 1.2022 |
60
+ | No log | 0.3333 | 18 | 1.6039 | 0.1763 | 1.6039 | 1.2664 |
61
+ | No log | 0.3704 | 20 | 1.5991 | 0.1357 | 1.5991 | 1.2646 |
62
+ | No log | 0.4074 | 22 | 1.4327 | 0.0403 | 1.4327 | 1.1970 |
63
+ | No log | 0.4444 | 24 | 1.4571 | 0.0723 | 1.4571 | 1.2071 |
64
+ | No log | 0.4815 | 26 | 1.2924 | 0.0898 | 1.2924 | 1.1368 |
65
+ | No log | 0.5185 | 28 | 1.2798 | 0.1427 | 1.2798 | 1.1313 |
66
+ | No log | 0.5556 | 30 | 1.3692 | 0.1689 | 1.3692 | 1.1701 |
67
+ | No log | 0.5926 | 32 | 1.4550 | 0.2129 | 1.4550 | 1.2062 |
68
+ | No log | 0.6296 | 34 | 1.2036 | 0.2450 | 1.2036 | 1.0971 |
69
+ | No log | 0.6667 | 36 | 0.9333 | 0.3590 | 0.9333 | 0.9661 |
70
+ | No log | 0.7037 | 38 | 0.8415 | 0.4237 | 0.8415 | 0.9173 |
71
+ | No log | 0.7407 | 40 | 0.8752 | 0.4213 | 0.8752 | 0.9355 |
72
+ | No log | 0.7778 | 42 | 0.9948 | 0.3881 | 0.9948 | 0.9974 |
73
+ | No log | 0.8148 | 44 | 1.2087 | 0.3326 | 1.2087 | 1.0994 |
74
+ | No log | 0.8519 | 46 | 1.1057 | 0.4383 | 1.1057 | 1.0515 |
75
+ | No log | 0.8889 | 48 | 1.2641 | 0.3812 | 1.2641 | 1.1243 |
76
+ | No log | 0.9259 | 50 | 1.1410 | 0.3387 | 1.1410 | 1.0682 |
77
+ | No log | 0.9630 | 52 | 0.8148 | 0.3992 | 0.8148 | 0.9027 |
78
+ | No log | 1.0 | 54 | 0.9577 | 0.4029 | 0.9577 | 0.9786 |
79
+ | No log | 1.0370 | 56 | 1.8234 | -0.0190 | 1.8234 | 1.3503 |
80
+ | No log | 1.0741 | 58 | 2.2725 | -0.1254 | 2.2725 | 1.5075 |
81
+ | No log | 1.1111 | 60 | 1.6981 | 0.0317 | 1.6981 | 1.3031 |
82
+ | No log | 1.1481 | 62 | 1.1985 | 0.2446 | 1.1985 | 1.0948 |
83
+ | No log | 1.1852 | 64 | 1.1337 | 0.2473 | 1.1337 | 1.0648 |
84
+ | No log | 1.2222 | 66 | 1.0396 | 0.2446 | 1.0396 | 1.0196 |
85
+ | No log | 1.2593 | 68 | 1.0417 | 0.2446 | 1.0417 | 1.0206 |
86
+ | No log | 1.2963 | 70 | 0.9200 | 0.4483 | 0.9200 | 0.9592 |
87
+ | No log | 1.3333 | 72 | 0.8561 | 0.4853 | 0.8561 | 0.9253 |
88
+ | No log | 1.3704 | 74 | 0.8447 | 0.4976 | 0.8447 | 0.9191 |
89
+ | No log | 1.4074 | 76 | 0.8303 | 0.5205 | 0.8303 | 0.9112 |
90
+ | No log | 1.4444 | 78 | 0.8769 | 0.5169 | 0.8769 | 0.9364 |
91
+ | No log | 1.4815 | 80 | 0.8791 | 0.5169 | 0.8791 | 0.9376 |
92
+ | No log | 1.5185 | 82 | 0.9181 | 0.5530 | 0.9181 | 0.9582 |
93
+ | No log | 1.5556 | 84 | 0.9652 | 0.4457 | 0.9652 | 0.9824 |
94
+ | No log | 1.5926 | 86 | 0.8113 | 0.5216 | 0.8113 | 0.9007 |
95
+ | No log | 1.6296 | 88 | 0.8077 | 0.5176 | 0.8077 | 0.8987 |
96
+ | No log | 1.6667 | 90 | 0.8075 | 0.5315 | 0.8075 | 0.8986 |
97
+ | No log | 1.7037 | 92 | 0.8475 | 0.5006 | 0.8475 | 0.9206 |
98
+ | No log | 1.7407 | 94 | 1.0371 | 0.4480 | 1.0371 | 1.0184 |
99
+ | No log | 1.7778 | 96 | 1.0687 | 0.4153 | 1.0687 | 1.0338 |
100
+ | No log | 1.8148 | 98 | 0.9038 | 0.4804 | 0.9038 | 0.9507 |
101
+ | No log | 1.8519 | 100 | 0.8185 | 0.4941 | 0.8185 | 0.9047 |
102
+ | No log | 1.8889 | 102 | 0.8078 | 0.5093 | 0.8078 | 0.8988 |
103
+ | No log | 1.9259 | 104 | 0.8072 | 0.5027 | 0.8072 | 0.8985 |
104
+ | No log | 1.9630 | 106 | 0.8129 | 0.5027 | 0.8129 | 0.9016 |
105
+ | No log | 2.0 | 108 | 0.8317 | 0.5826 | 0.8317 | 0.9120 |
106
+ | No log | 2.0370 | 110 | 0.7546 | 0.5590 | 0.7546 | 0.8687 |
107
+ | No log | 2.0741 | 112 | 0.7540 | 0.4931 | 0.7540 | 0.8683 |
108
+ | No log | 2.1111 | 114 | 0.7499 | 0.4931 | 0.7499 | 0.8660 |
109
+ | No log | 2.1481 | 116 | 0.7046 | 0.5982 | 0.7046 | 0.8394 |
110
+ | No log | 2.1852 | 118 | 0.7069 | 0.6214 | 0.7069 | 0.8408 |
111
+ | No log | 2.2222 | 120 | 0.6733 | 0.6227 | 0.6733 | 0.8205 |
112
+ | No log | 2.2593 | 122 | 0.7189 | 0.5517 | 0.7189 | 0.8479 |
113
+ | No log | 2.2963 | 124 | 0.8639 | 0.5044 | 0.8639 | 0.9294 |
114
+ | No log | 2.3333 | 126 | 0.7741 | 0.5374 | 0.7741 | 0.8798 |
115
+ | No log | 2.3704 | 128 | 0.7205 | 0.5767 | 0.7205 | 0.8488 |
116
+ | No log | 2.4074 | 130 | 0.8793 | 0.4596 | 0.8793 | 0.9377 |
117
+ | No log | 2.4444 | 132 | 0.9548 | 0.4494 | 0.9548 | 0.9771 |
118
+ | No log | 2.4815 | 134 | 0.7918 | 0.4813 | 0.7918 | 0.8898 |
119
+ | No log | 2.5185 | 136 | 0.7249 | 0.6282 | 0.7249 | 0.8514 |
120
+ | No log | 2.5556 | 138 | 0.7125 | 0.5987 | 0.7125 | 0.8441 |
121
+ | No log | 2.5926 | 140 | 0.7344 | 0.5930 | 0.7344 | 0.8570 |
122
+ | No log | 2.6296 | 142 | 0.7177 | 0.5744 | 0.7177 | 0.8472 |
123
+ | No log | 2.6667 | 144 | 0.7431 | 0.6357 | 0.7431 | 0.8620 |
124
+ | No log | 2.7037 | 146 | 0.7721 | 0.6446 | 0.7721 | 0.8787 |
125
+ | No log | 2.7407 | 148 | 0.7210 | 0.6272 | 0.7210 | 0.8491 |
126
+ | No log | 2.7778 | 150 | 0.7067 | 0.5675 | 0.7067 | 0.8406 |
127
+ | No log | 2.8148 | 152 | 0.8026 | 0.5406 | 0.8026 | 0.8959 |
128
+ | No log | 2.8519 | 154 | 0.8272 | 0.4744 | 0.8272 | 0.9095 |
129
+ | No log | 2.8889 | 156 | 1.0038 | 0.5304 | 1.0038 | 1.0019 |
130
+ | No log | 2.9259 | 158 | 0.9561 | 0.5241 | 0.9561 | 0.9778 |
131
+ | No log | 2.9630 | 160 | 0.8976 | 0.5 | 0.8976 | 0.9474 |
132
+ | No log | 3.0 | 162 | 0.8228 | 0.4984 | 0.8228 | 0.9071 |
133
+ | No log | 3.0370 | 164 | 0.8668 | 0.4836 | 0.8668 | 0.9310 |
134
+ | No log | 3.0741 | 166 | 0.7644 | 0.5622 | 0.7644 | 0.8743 |
135
+ | No log | 3.1111 | 168 | 0.7958 | 0.5722 | 0.7958 | 0.8921 |
136
+ | No log | 3.1481 | 170 | 0.8813 | 0.5582 | 0.8813 | 0.9388 |
137
+ | No log | 3.1852 | 172 | 0.7813 | 0.5812 | 0.7813 | 0.8839 |
138
+ | No log | 3.2222 | 174 | 0.7500 | 0.6129 | 0.7500 | 0.8660 |
139
+ | No log | 3.2593 | 176 | 0.7605 | 0.5329 | 0.7605 | 0.8721 |
140
+ | No log | 3.2963 | 178 | 0.7667 | 0.5094 | 0.7667 | 0.8756 |
141
+ | No log | 3.3333 | 180 | 0.7770 | 0.5522 | 0.7770 | 0.8815 |
142
+ | No log | 3.3704 | 182 | 0.8467 | 0.5462 | 0.8467 | 0.9202 |
143
+ | No log | 3.4074 | 184 | 0.7698 | 0.5919 | 0.7698 | 0.8774 |
144
+ | No log | 3.4444 | 186 | 0.7466 | 0.5355 | 0.7466 | 0.8641 |
145
+ | No log | 3.4815 | 188 | 0.8184 | 0.5710 | 0.8184 | 0.9047 |
146
+ | No log | 3.5185 | 190 | 0.8618 | 0.5554 | 0.8618 | 0.9283 |
147
+ | No log | 3.5556 | 192 | 0.7526 | 0.5136 | 0.7526 | 0.8675 |
148
+ | No log | 3.5926 | 194 | 0.7831 | 0.6274 | 0.7831 | 0.8849 |
149
+ | No log | 3.6296 | 196 | 0.9136 | 0.5222 | 0.9136 | 0.9558 |
150
+ | No log | 3.6667 | 198 | 0.8287 | 0.5022 | 0.8287 | 0.9103 |
151
+ | No log | 3.7037 | 200 | 0.8194 | 0.5098 | 0.8194 | 0.9052 |
152
+ | No log | 3.7407 | 202 | 0.9442 | 0.4526 | 0.9442 | 0.9717 |
153
+ | No log | 3.7778 | 204 | 0.9617 | 0.4262 | 0.9617 | 0.9806 |
154
+ | No log | 3.8148 | 206 | 0.8450 | 0.5098 | 0.8450 | 0.9192 |
155
+ | No log | 3.8519 | 208 | 0.8730 | 0.4666 | 0.8730 | 0.9343 |
156
+ | No log | 3.8889 | 210 | 0.8528 | 0.4763 | 0.8528 | 0.9235 |
157
+ | No log | 3.9259 | 212 | 0.7952 | 0.5577 | 0.7952 | 0.8917 |
158
+ | No log | 3.9630 | 214 | 0.9210 | 0.4765 | 0.9210 | 0.9597 |
159
+ | No log | 4.0 | 216 | 1.0130 | 0.3409 | 1.0130 | 1.0065 |
160
+ | No log | 4.0370 | 218 | 0.8991 | 0.4402 | 0.8991 | 0.9482 |
161
+ | No log | 4.0741 | 220 | 0.8336 | 0.4946 | 0.8336 | 0.9130 |
162
+ | No log | 4.1111 | 222 | 0.8366 | 0.4946 | 0.8366 | 0.9147 |
163
+ | No log | 4.1481 | 224 | 0.8602 | 0.5047 | 0.8602 | 0.9275 |
164
+ | No log | 4.1852 | 226 | 0.8316 | 0.4912 | 0.8316 | 0.9119 |
165
+ | No log | 4.2222 | 228 | 0.8340 | 0.5283 | 0.8340 | 0.9132 |
166
+ | No log | 4.2593 | 230 | 0.8493 | 0.5320 | 0.8493 | 0.9216 |
167
+ | No log | 4.2963 | 232 | 0.8515 | 0.5024 | 0.8515 | 0.9228 |
168
+ | No log | 4.3333 | 234 | 0.8317 | 0.5291 | 0.8317 | 0.9120 |
169
+ | No log | 4.3704 | 236 | 0.8281 | 0.4811 | 0.8281 | 0.9100 |
170
+ | No log | 4.4074 | 238 | 0.7994 | 0.5404 | 0.7994 | 0.8941 |
171
+ | No log | 4.4444 | 240 | 0.8046 | 0.4929 | 0.8046 | 0.8970 |
172
+ | No log | 4.4815 | 242 | 0.8209 | 0.4512 | 0.8209 | 0.9060 |
173
+ | No log | 4.5185 | 244 | 0.9023 | 0.4676 | 0.9023 | 0.9499 |
174
+ | No log | 4.5556 | 246 | 0.8791 | 0.4811 | 0.8791 | 0.9376 |
175
+ | No log | 4.5926 | 248 | 0.8582 | 0.4722 | 0.8582 | 0.9264 |
176
+ | No log | 4.6296 | 250 | 0.8309 | 0.4555 | 0.8309 | 0.9116 |
177
+ | No log | 4.6667 | 252 | 0.8592 | 0.5642 | 0.8592 | 0.9269 |
178
+ | No log | 4.7037 | 254 | 0.8858 | 0.5394 | 0.8858 | 0.9412 |
179
+ | No log | 4.7407 | 256 | 0.8160 | 0.6573 | 0.8160 | 0.9033 |
180
+ | No log | 4.7778 | 258 | 0.7600 | 0.5262 | 0.7600 | 0.8718 |
181
+ | No log | 4.8148 | 260 | 0.7645 | 0.5351 | 0.7645 | 0.8744 |
182
+ | No log | 4.8519 | 262 | 0.7518 | 0.6141 | 0.7518 | 0.8670 |
183
+ | No log | 4.8889 | 264 | 0.8840 | 0.5688 | 0.8840 | 0.9402 |
184
+ | No log | 4.9259 | 266 | 0.9858 | 0.5199 | 0.9858 | 0.9929 |
185
+ | No log | 4.9630 | 268 | 1.2209 | 0.3955 | 1.2209 | 1.1049 |
186
+ | No log | 5.0 | 270 | 1.0024 | 0.4240 | 1.0024 | 1.0012 |
187
+ | No log | 5.0370 | 272 | 0.7898 | 0.4098 | 0.7898 | 0.8887 |
188
+ | No log | 5.0741 | 274 | 0.7864 | 0.5093 | 0.7864 | 0.8868 |
189
+ | No log | 5.1111 | 276 | 0.7995 | 0.4434 | 0.7995 | 0.8942 |
190
+ | No log | 5.1481 | 278 | 0.7787 | 0.5966 | 0.7787 | 0.8824 |
191
+ | No log | 5.1852 | 280 | 0.7767 | 0.5966 | 0.7767 | 0.8813 |
192
+ | No log | 5.2222 | 282 | 0.7964 | 0.5247 | 0.7964 | 0.8924 |
193
+ | No log | 5.2593 | 284 | 0.7991 | 0.4944 | 0.7991 | 0.8939 |
194
+ | No log | 5.2963 | 286 | 0.7827 | 0.5505 | 0.7827 | 0.8847 |
195
+ | No log | 5.3333 | 288 | 0.8249 | 0.5751 | 0.8249 | 0.9082 |
196
+ | No log | 5.3704 | 290 | 0.8309 | 0.5270 | 0.8309 | 0.9116 |
197
+ | No log | 5.4074 | 292 | 0.8042 | 0.5270 | 0.8042 | 0.8968 |
198
+ | No log | 5.4444 | 294 | 0.7517 | 0.6212 | 0.7517 | 0.8670 |
199
+ | No log | 5.4815 | 296 | 0.7488 | 0.6194 | 0.7488 | 0.8653 |
200
+ | No log | 5.5185 | 298 | 0.7222 | 0.6667 | 0.7222 | 0.8498 |
201
+ | No log | 5.5556 | 300 | 0.7373 | 0.6204 | 0.7373 | 0.8587 |
202
+ | No log | 5.5926 | 302 | 0.8005 | 0.4902 | 0.8005 | 0.8947 |
203
+ | No log | 5.6296 | 304 | 0.7851 | 0.4916 | 0.7851 | 0.8860 |
204
+ | No log | 5.6667 | 306 | 0.7379 | 0.6046 | 0.7379 | 0.8590 |
205
+ | No log | 5.7037 | 308 | 0.8155 | 0.5209 | 0.8155 | 0.9030 |
206
+ | No log | 5.7407 | 310 | 0.8447 | 0.4785 | 0.8447 | 0.9191 |
207
+ | No log | 5.7778 | 312 | 0.8097 | 0.5247 | 0.8097 | 0.8998 |
208
+ | No log | 5.8148 | 314 | 0.7891 | 0.5112 | 0.7891 | 0.8883 |
209
+ | No log | 5.8519 | 316 | 0.7660 | 0.5027 | 0.7660 | 0.8752 |
210
+ | No log | 5.8889 | 318 | 0.7404 | 0.5164 | 0.7404 | 0.8605 |
211
+ | No log | 5.9259 | 320 | 0.7195 | 0.5606 | 0.7195 | 0.8482 |
212
+ | No log | 5.9630 | 322 | 0.8096 | 0.4425 | 0.8096 | 0.8998 |
213
+ | No log | 6.0 | 324 | 0.7882 | 0.4425 | 0.7882 | 0.8878 |
214
+ | No log | 6.0370 | 326 | 0.7282 | 0.5606 | 0.7282 | 0.8533 |
215
+ | No log | 6.0741 | 328 | 0.7350 | 0.5327 | 0.7350 | 0.8573 |
216
+ | No log | 6.1111 | 330 | 0.7377 | 0.5279 | 0.7377 | 0.8589 |
217
+ | No log | 6.1481 | 332 | 0.7369 | 0.5544 | 0.7369 | 0.8584 |
218
+ | No log | 6.1852 | 334 | 0.7297 | 0.5215 | 0.7297 | 0.8542 |
219
+ | No log | 6.2222 | 336 | 0.7519 | 0.4620 | 0.7519 | 0.8671 |
220
+ | No log | 6.2593 | 338 | 0.7683 | 0.4757 | 0.7683 | 0.8765 |
221
+ | No log | 6.2963 | 340 | 0.8185 | 0.4284 | 0.8185 | 0.9047 |
222
+ | No log | 6.3333 | 342 | 0.8447 | 0.4873 | 0.8447 | 0.9191 |
223
+ | No log | 6.3704 | 344 | 0.8287 | 0.4563 | 0.8287 | 0.9103 |
224
+ | No log | 6.4074 | 346 | 0.8360 | 0.5279 | 0.8360 | 0.9143 |
225
+ | No log | 6.4444 | 348 | 0.7964 | 0.5720 | 0.7964 | 0.8924 |
226
+ | No log | 6.4815 | 350 | 0.7619 | 0.5917 | 0.7619 | 0.8729 |
227
+ | No log | 6.5185 | 352 | 0.7484 | 0.5917 | 0.7484 | 0.8651 |
228
+ | No log | 6.5556 | 354 | 0.7648 | 0.5479 | 0.7648 | 0.8745 |
229
+ | No log | 6.5926 | 356 | 0.8844 | 0.4553 | 0.8844 | 0.9404 |
230
+ | No log | 6.6296 | 358 | 0.9345 | 0.4639 | 0.9345 | 0.9667 |
231
+ | No log | 6.6667 | 360 | 0.8579 | 0.4787 | 0.8579 | 0.9262 |
232
+ | No log | 6.7037 | 362 | 0.7811 | 0.4960 | 0.7811 | 0.8838 |
233
+ | No log | 6.7407 | 364 | 0.8000 | 0.5010 | 0.8000 | 0.8944 |
234
+ | No log | 6.7778 | 366 | 0.8174 | 0.4976 | 0.8174 | 0.9041 |
235
+ | No log | 6.8148 | 368 | 0.8125 | 0.4893 | 0.8125 | 0.9014 |
236
+ | No log | 6.8519 | 370 | 0.8067 | 0.5622 | 0.8067 | 0.8982 |
237
+ | No log | 6.8889 | 372 | 0.7920 | 0.5495 | 0.7920 | 0.8899 |
238
+ | No log | 6.9259 | 374 | 0.7304 | 0.6120 | 0.7304 | 0.8546 |
239
+ | No log | 6.9630 | 376 | 0.7140 | 0.5909 | 0.7140 | 0.8450 |
240
+ | No log | 7.0 | 378 | 0.7260 | 0.5917 | 0.7260 | 0.8521 |
241
+ | No log | 7.0370 | 380 | 0.7352 | 0.6120 | 0.7352 | 0.8574 |
242
+ | No log | 7.0741 | 382 | 0.8175 | 0.5201 | 0.8175 | 0.9042 |
243
+ | No log | 7.1111 | 384 | 0.8066 | 0.5201 | 0.8066 | 0.8981 |
244
+ | No log | 7.1481 | 386 | 0.7397 | 0.5550 | 0.7397 | 0.8601 |
245
+ | No log | 7.1852 | 388 | 0.7277 | 0.6021 | 0.7277 | 0.8531 |
246
+ | No log | 7.2222 | 390 | 0.7373 | 0.5438 | 0.7373 | 0.8587 |
247
+ | No log | 7.2593 | 392 | 0.7362 | 0.5305 | 0.7362 | 0.8580 |
248
+ | No log | 7.2963 | 394 | 0.7292 | 0.5905 | 0.7292 | 0.8539 |
249
+ | No log | 7.3333 | 396 | 0.7479 | 0.5324 | 0.7479 | 0.8648 |
250
+ | No log | 7.3704 | 398 | 0.7287 | 0.5517 | 0.7287 | 0.8536 |
251
+ | No log | 7.4074 | 400 | 0.6828 | 0.6678 | 0.6828 | 0.8263 |
252
+ | No log | 7.4444 | 402 | 0.7690 | 0.5532 | 0.7690 | 0.8769 |
253
+ | No log | 7.4815 | 404 | 0.7889 | 0.5724 | 0.7889 | 0.8882 |
254
+ | No log | 7.5185 | 406 | 0.7111 | 0.6813 | 0.7111 | 0.8433 |
255
+ | No log | 7.5556 | 408 | 0.7084 | 0.6401 | 0.7084 | 0.8417 |
256
+ | No log | 7.5926 | 410 | 0.7452 | 0.5946 | 0.7452 | 0.8632 |
257
+ | No log | 7.6296 | 412 | 0.7469 | 0.5971 | 0.7469 | 0.8642 |
258
+ | No log | 7.6667 | 414 | 0.7537 | 0.5708 | 0.7537 | 0.8681 |
259
+ | No log | 7.7037 | 416 | 0.7611 | 0.5399 | 0.7611 | 0.8724 |
260
+ | No log | 7.7407 | 418 | 0.7984 | 0.5173 | 0.7984 | 0.8935 |
261
+ | No log | 7.7778 | 420 | 0.8330 | 0.4820 | 0.8330 | 0.9127 |
262
+ | No log | 7.8148 | 422 | 0.8670 | 0.4202 | 0.8670 | 0.9311 |
263
+ | No log | 7.8519 | 424 | 0.8530 | 0.4104 | 0.8530 | 0.9236 |
264
+ | No log | 7.8889 | 426 | 0.8176 | 0.4780 | 0.8176 | 0.9042 |
265
+ | No log | 7.9259 | 428 | 0.8074 | 0.5404 | 0.8074 | 0.8986 |
266
+ | No log | 7.9630 | 430 | 0.8047 | 0.4995 | 0.8047 | 0.8971 |
267
+ | No log | 8.0 | 432 | 0.8395 | 0.5107 | 0.8395 | 0.9162 |
268
+ | No log | 8.0370 | 434 | 0.8208 | 0.4734 | 0.8208 | 0.9060 |
269
+ | No log | 8.0741 | 436 | 0.7534 | 0.5495 | 0.7534 | 0.8680 |
270
+ | No log | 8.1111 | 438 | 0.7294 | 0.5957 | 0.7294 | 0.8541 |
271
+ | No log | 8.1481 | 440 | 0.7510 | 0.5601 | 0.7510 | 0.8666 |
272
+ | No log | 8.1852 | 442 | 0.8780 | 0.5105 | 0.8780 | 0.9370 |
273
+ | No log | 8.2222 | 444 | 0.9196 | 0.5153 | 0.9196 | 0.9590 |
274
+ | No log | 8.2593 | 446 | 0.8433 | 0.4615 | 0.8433 | 0.9183 |
275
+ | No log | 8.2963 | 448 | 0.7515 | 0.5524 | 0.7515 | 0.8669 |
276
+ | No log | 8.3333 | 450 | 0.7489 | 0.5223 | 0.7489 | 0.8654 |
277
+ | No log | 8.3704 | 452 | 0.7553 | 0.5125 | 0.7553 | 0.8691 |
278
+ | No log | 8.4074 | 454 | 0.7846 | 0.5569 | 0.7846 | 0.8858 |
279
+ | No log | 8.4444 | 456 | 0.7993 | 0.5230 | 0.7993 | 0.8940 |
280
+ | No log | 8.4815 | 458 | 0.7743 | 0.5265 | 0.7743 | 0.8800 |
281
+ | No log | 8.5185 | 460 | 0.8058 | 0.5562 | 0.8058 | 0.8976 |
282
+ | No log | 8.5556 | 462 | 0.8837 | 0.4563 | 0.8837 | 0.9400 |
283
+ | No log | 8.5926 | 464 | 0.8498 | 0.4867 | 0.8498 | 0.9218 |
284
+ | No log | 8.6296 | 466 | 0.7793 | 0.5562 | 0.7793 | 0.8828 |
285
+ | No log | 8.6667 | 468 | 0.7455 | 0.6664 | 0.7455 | 0.8634 |
286
+ | No log | 8.7037 | 470 | 0.7484 | 0.6225 | 0.7484 | 0.8651 |
287
+ | No log | 8.7407 | 472 | 0.7471 | 0.6664 | 0.7471 | 0.8644 |
288
+ | No log | 8.7778 | 474 | 0.7620 | 0.5898 | 0.7620 | 0.8729 |
289
+ | No log | 8.8148 | 476 | 0.7654 | 0.6324 | 0.7654 | 0.8749 |
290
+ | No log | 8.8519 | 478 | 0.7855 | 0.5870 | 0.7855 | 0.8863 |
291
+ | No log | 8.8889 | 480 | 0.7803 | 0.6190 | 0.7803 | 0.8834 |
292
+ | No log | 8.9259 | 482 | 0.7996 | 0.5570 | 0.7996 | 0.8942 |
293
+ | No log | 8.9630 | 484 | 0.8169 | 0.4998 | 0.8169 | 0.9039 |
294
+ | No log | 9.0 | 486 | 0.8078 | 0.5447 | 0.8078 | 0.8988 |
295
+ | No log | 9.0370 | 488 | 0.8014 | 0.4734 | 0.8014 | 0.8952 |
296
+ | No log | 9.0741 | 490 | 0.7957 | 0.4681 | 0.7957 | 0.8920 |
297
+ | No log | 9.1111 | 492 | 0.7962 | 0.4879 | 0.7962 | 0.8923 |
298
+ | No log | 9.1481 | 494 | 0.7842 | 0.5831 | 0.7842 | 0.8855 |
299
+ | No log | 9.1852 | 496 | 0.7750 | 0.6046 | 0.7750 | 0.8803 |
300
+ | No log | 9.2222 | 498 | 0.7811 | 0.5676 | 0.7811 | 0.8838 |
301
+ | 0.3574 | 9.2593 | 500 | 0.7979 | 0.5403 | 0.7979 | 0.8933 |
302
+ | 0.3574 | 9.2963 | 502 | 0.8059 | 0.4768 | 0.8059 | 0.8977 |
303
+ | 0.3574 | 9.3333 | 504 | 0.8142 | 0.5399 | 0.8142 | 0.9024 |
304
+ | 0.3574 | 9.3704 | 506 | 0.8422 | 0.4926 | 0.8422 | 0.9177 |
305
+ | 0.3574 | 9.4074 | 508 | 0.8534 | 0.4657 | 0.8534 | 0.9238 |
306
+ | 0.3574 | 9.4444 | 510 | 0.8275 | 0.5138 | 0.8275 | 0.9097 |
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 sha256:784a7e0658804ddf545da8f8341fc2d44962ba6bdd6e6aa416ff18872e802f63
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ad909037bf729f6f4bf46c99695f99561b4fa815dce95504fa4771803239ecdf
3
+ size 5240