Hugging Face's logo

MayBashendy
/
ArabicNewSplits7_B_usingALLEssays_FineTuningAraBERT_run3_AugV5_k19_task2_organization

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
 Community
MayBashendy commited on
Commit
368f834
·
verified ·
1 Parent(s): a95294e

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_usingALLEssays_FineTuningAraBERT_run3_AugV5_k18_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_usingALLEssays_FineTuningAraBERT_run3_AugV5_k18_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: 0.8177
19
+ - Qwk: 0.4036
20
+ - Mse: 0.8177
21
+ - Rmse: 0.9043
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 | 0.0217 | 2 | 4.4951 | 0.0010 | 4.4951 | 2.1202 |
53
+ | No log | 0.0435 | 4 | 2.5130 | -0.0287 | 2.5130 | 1.5852 |
54
+ | No log | 0.0652 | 6 | 1.7940 | 0.0062 | 1.7940 | 1.3394 |
55
+ | No log | 0.0870 | 8 | 1.3636 | 0.0878 | 1.3636 | 1.1677 |
56
+ | No log | 0.1087 | 10 | 1.1335 | 0.2255 | 1.1335 | 1.0646 |
57
+ | No log | 0.1304 | 12 | 1.1718 | 0.2476 | 1.1718 | 1.0825 |
58
+ | No log | 0.1522 | 14 | 1.8664 | 0.1412 | 1.8664 | 1.3661 |
59
+ | No log | 0.1739 | 16 | 1.6188 | 0.1587 | 1.6188 | 1.2723 |
60
+ | No log | 0.1957 | 18 | 1.1806 | 0.1841 | 1.1806 | 1.0866 |
61
+ | No log | 0.2174 | 20 | 1.1515 | 0.1761 | 1.1515 | 1.0731 |
62
+ | No log | 0.2391 | 22 | 1.1647 | 0.2086 | 1.1647 | 1.0792 |
63
+ | No log | 0.2609 | 24 | 1.1787 | 0.1447 | 1.1787 | 1.0857 |
64
+ | No log | 0.2826 | 26 | 1.2180 | 0.2705 | 1.2180 | 1.1036 |
65
+ | No log | 0.3043 | 28 | 1.1794 | 0.2675 | 1.1794 | 1.0860 |
66
+ | No log | 0.3261 | 30 | 1.0286 | 0.3646 | 1.0286 | 1.0142 |
67
+ | No log | 0.3478 | 32 | 0.9650 | 0.3689 | 0.9650 | 0.9823 |
68
+ | No log | 0.3696 | 34 | 1.0277 | 0.3243 | 1.0277 | 1.0138 |
69
+ | No log | 0.3913 | 36 | 0.9294 | 0.3720 | 0.9294 | 0.9641 |
70
+ | No log | 0.4130 | 38 | 1.0967 | 0.3168 | 1.0967 | 1.0472 |
71
+ | No log | 0.4348 | 40 | 1.9882 | 0.2092 | 1.9882 | 1.4100 |
72
+ | No log | 0.4565 | 42 | 2.2102 | 0.2123 | 2.2102 | 1.4867 |
73
+ | No log | 0.4783 | 44 | 1.7362 | 0.2761 | 1.7362 | 1.3176 |
74
+ | No log | 0.5 | 46 | 1.3625 | 0.3585 | 1.3625 | 1.1673 |
75
+ | No log | 0.5217 | 48 | 1.1973 | 0.3978 | 1.1973 | 1.0942 |
76
+ | No log | 0.5435 | 50 | 0.9740 | 0.3790 | 0.9740 | 0.9869 |
77
+ | No log | 0.5652 | 52 | 0.9560 | 0.375 | 0.9560 | 0.9777 |
78
+ | No log | 0.5870 | 54 | 1.1174 | 0.3442 | 1.1174 | 1.0571 |
79
+ | No log | 0.6087 | 56 | 1.5453 | 0.2905 | 1.5453 | 1.2431 |
80
+ | No log | 0.6304 | 58 | 1.9251 | 0.2314 | 1.9251 | 1.3875 |
81
+ | No log | 0.6522 | 60 | 1.7496 | 0.2550 | 1.7496 | 1.3227 |
82
+ | No log | 0.6739 | 62 | 1.3838 | 0.3738 | 1.3838 | 1.1764 |
83
+ | No log | 0.6957 | 64 | 0.9768 | 0.4817 | 0.9768 | 0.9883 |
84
+ | No log | 0.7174 | 66 | 0.8074 | 0.4701 | 0.8074 | 0.8985 |
85
+ | No log | 0.7391 | 68 | 0.7608 | 0.5094 | 0.7608 | 0.8722 |
86
+ | No log | 0.7609 | 70 | 0.7917 | 0.4104 | 0.7917 | 0.8897 |
87
+ | No log | 0.7826 | 72 | 0.7784 | 0.4104 | 0.7784 | 0.8823 |
88
+ | No log | 0.8043 | 74 | 0.7765 | 0.5115 | 0.7765 | 0.8812 |
89
+ | No log | 0.8261 | 76 | 1.2913 | 0.3923 | 1.2913 | 1.1363 |
90
+ | No log | 0.8478 | 78 | 1.5834 | 0.3252 | 1.5834 | 1.2583 |
91
+ | No log | 0.8696 | 80 | 1.2178 | 0.3932 | 1.2178 | 1.1035 |
92
+ | No log | 0.8913 | 82 | 0.8685 | 0.4513 | 0.8685 | 0.9320 |
93
+ | No log | 0.9130 | 84 | 0.8210 | 0.4077 | 0.8210 | 0.9061 |
94
+ | No log | 0.9348 | 86 | 0.8471 | 0.3621 | 0.8471 | 0.9204 |
95
+ | No log | 0.9565 | 88 | 0.8719 | 0.4202 | 0.8719 | 0.9337 |
96
+ | No log | 0.9783 | 90 | 0.8922 | 0.4164 | 0.8922 | 0.9446 |
97
+ | No log | 1.0 | 92 | 0.8818 | 0.4439 | 0.8818 | 0.9391 |
98
+ | No log | 1.0217 | 94 | 0.8632 | 0.4423 | 0.8632 | 0.9291 |
99
+ | No log | 1.0435 | 96 | 0.8959 | 0.4299 | 0.8959 | 0.9465 |
100
+ | No log | 1.0652 | 98 | 1.0627 | 0.3964 | 1.0627 | 1.0309 |
101
+ | No log | 1.0870 | 100 | 1.0707 | 0.4153 | 1.0707 | 1.0347 |
102
+ | No log | 1.1087 | 102 | 0.8886 | 0.4589 | 0.8886 | 0.9426 |
103
+ | No log | 1.1304 | 104 | 0.7450 | 0.5358 | 0.7450 | 0.8632 |
104
+ | No log | 1.1522 | 106 | 0.7264 | 0.6118 | 0.7264 | 0.8523 |
105
+ | No log | 1.1739 | 108 | 0.8687 | 0.5494 | 0.8687 | 0.9320 |
106
+ | No log | 1.1957 | 110 | 1.0694 | 0.4153 | 1.0694 | 1.0341 |
107
+ | No log | 1.2174 | 112 | 1.4060 | 0.3279 | 1.4060 | 1.1858 |
108
+ | No log | 1.2391 | 114 | 1.2379 | 0.4165 | 1.2379 | 1.1126 |
109
+ | No log | 1.2609 | 116 | 0.8603 | 0.5222 | 0.8603 | 0.9276 |
110
+ | No log | 1.2826 | 118 | 0.7569 | 0.5173 | 0.7569 | 0.8700 |
111
+ | No log | 1.3043 | 120 | 0.8592 | 0.5102 | 0.8592 | 0.9269 |
112
+ | No log | 1.3261 | 122 | 0.8168 | 0.4665 | 0.8168 | 0.9038 |
113
+ | No log | 1.3478 | 124 | 0.8191 | 0.4236 | 0.8191 | 0.9051 |
114
+ | No log | 1.3696 | 126 | 0.9042 | 0.4444 | 0.9042 | 0.9509 |
115
+ | No log | 1.3913 | 128 | 0.8815 | 0.4748 | 0.8815 | 0.9389 |
116
+ | No log | 1.4130 | 130 | 0.8243 | 0.5604 | 0.8243 | 0.9079 |
117
+ | No log | 1.4348 | 132 | 0.7567 | 0.5634 | 0.7567 | 0.8699 |
118
+ | No log | 1.4565 | 134 | 0.7898 | 0.5403 | 0.7898 | 0.8887 |
119
+ | No log | 1.4783 | 136 | 0.7952 | 0.5595 | 0.7952 | 0.8918 |
120
+ | No log | 1.5 | 138 | 0.7794 | 0.5403 | 0.7794 | 0.8828 |
121
+ | No log | 1.5217 | 140 | 0.7855 | 0.5042 | 0.7855 | 0.8863 |
122
+ | No log | 1.5435 | 142 | 0.9707 | 0.4693 | 0.9707 | 0.9852 |
123
+ | No log | 1.5652 | 144 | 1.2738 | 0.3970 | 1.2738 | 1.1286 |
124
+ | No log | 1.5870 | 146 | 1.1768 | 0.4219 | 1.1768 | 1.0848 |
125
+ | No log | 1.6087 | 148 | 0.9043 | 0.3914 | 0.9043 | 0.9509 |
126
+ | No log | 1.6304 | 150 | 0.7757 | 0.5265 | 0.7757 | 0.8807 |
127
+ | No log | 1.6522 | 152 | 0.8592 | 0.4775 | 0.8592 | 0.9269 |
128
+ | No log | 1.6739 | 154 | 0.9013 | 0.5275 | 0.9013 | 0.9494 |
129
+ | No log | 1.6957 | 156 | 0.7924 | 0.5124 | 0.7924 | 0.8902 |
130
+ | No log | 1.7174 | 158 | 0.7595 | 0.6284 | 0.7595 | 0.8715 |
131
+ | No log | 1.7391 | 160 | 1.2834 | 0.4291 | 1.2834 | 1.1329 |
132
+ | No log | 1.7609 | 162 | 1.5815 | 0.3913 | 1.5815 | 1.2576 |
133
+ | No log | 1.7826 | 164 | 1.3899 | 0.4502 | 1.3899 | 1.1790 |
134
+ | No log | 1.8043 | 166 | 0.9129 | 0.4909 | 0.9129 | 0.9555 |
135
+ | No log | 1.8261 | 168 | 0.6624 | 0.5634 | 0.6624 | 0.8139 |
136
+ | No log | 1.8478 | 170 | 0.7867 | 0.5171 | 0.7867 | 0.8869 |
137
+ | No log | 1.8696 | 172 | 0.8280 | 0.5118 | 0.8280 | 0.9099 |
138
+ | No log | 1.8913 | 174 | 0.7208 | 0.5471 | 0.7208 | 0.8490 |
139
+ | No log | 1.9130 | 176 | 0.6996 | 0.5922 | 0.6996 | 0.8364 |
140
+ | No log | 1.9348 | 178 | 0.7148 | 0.6546 | 0.7148 | 0.8454 |
141
+ | No log | 1.9565 | 180 | 0.7731 | 0.5954 | 0.7731 | 0.8792 |
142
+ | No log | 1.9783 | 182 | 0.7587 | 0.6013 | 0.7587 | 0.8711 |
143
+ | No log | 2.0 | 184 | 0.8058 | 0.6013 | 0.8058 | 0.8977 |
144
+ | No log | 2.0217 | 186 | 0.8087 | 0.5875 | 0.8087 | 0.8993 |
145
+ | No log | 2.0435 | 188 | 0.7759 | 0.5781 | 0.7759 | 0.8809 |
146
+ | No log | 2.0652 | 190 | 0.7737 | 0.6154 | 0.7737 | 0.8796 |
147
+ | No log | 2.0870 | 192 | 0.7314 | 0.6302 | 0.7314 | 0.8552 |
148
+ | No log | 2.1087 | 194 | 0.6960 | 0.6232 | 0.6960 | 0.8343 |
149
+ | No log | 2.1304 | 196 | 0.7189 | 0.6051 | 0.7189 | 0.8479 |
150
+ | No log | 2.1522 | 198 | 0.8293 | 0.5581 | 0.8293 | 0.9106 |
151
+ | No log | 2.1739 | 200 | 0.8063 | 0.5617 | 0.8063 | 0.8979 |
152
+ | No log | 2.1957 | 202 | 0.7386 | 0.6163 | 0.7386 | 0.8594 |
153
+ | No log | 2.2174 | 204 | 0.7467 | 0.5954 | 0.7467 | 0.8641 |
154
+ | No log | 2.2391 | 206 | 0.7581 | 0.6084 | 0.7581 | 0.8707 |
155
+ | No log | 2.2609 | 208 | 0.8980 | 0.4154 | 0.8980 | 0.9476 |
156
+ | No log | 2.2826 | 210 | 1.1736 | 0.4041 | 1.1736 | 1.0833 |
157
+ | No log | 2.3043 | 212 | 1.1048 | 0.4120 | 1.1048 | 1.0511 |
158
+ | No log | 2.3261 | 214 | 0.8479 | 0.4023 | 0.8479 | 0.9208 |
159
+ | No log | 2.3478 | 216 | 0.7599 | 0.5521 | 0.7599 | 0.8717 |
160
+ | No log | 2.3696 | 218 | 0.8341 | 0.5190 | 0.8341 | 0.9133 |
161
+ | No log | 2.3913 | 220 | 0.7701 | 0.5922 | 0.7701 | 0.8775 |
162
+ | No log | 2.4130 | 222 | 0.7611 | 0.5724 | 0.7611 | 0.8724 |
163
+ | No log | 2.4348 | 224 | 0.9455 | 0.4432 | 0.9455 | 0.9724 |
164
+ | No log | 2.4565 | 226 | 1.1576 | 0.4887 | 1.1576 | 1.0759 |
165
+ | No log | 2.4783 | 228 | 1.0097 | 0.4539 | 1.0097 | 1.0048 |
166
+ | No log | 2.5 | 230 | 0.8022 | 0.4782 | 0.8022 | 0.8956 |
167
+ | No log | 2.5217 | 232 | 0.8409 | 0.4381 | 0.8409 | 0.9170 |
168
+ | No log | 2.5435 | 234 | 0.8528 | 0.5062 | 0.8528 | 0.9234 |
169
+ | No log | 2.5652 | 236 | 0.8277 | 0.5046 | 0.8277 | 0.9098 |
170
+ | No log | 2.5870 | 238 | 0.9009 | 0.4213 | 0.9009 | 0.9491 |
171
+ | No log | 2.6087 | 240 | 1.0765 | 0.3734 | 1.0765 | 1.0376 |
172
+ | No log | 2.6304 | 242 | 1.0071 | 0.3828 | 1.0071 | 1.0036 |
173
+ | No log | 2.6522 | 244 | 0.8721 | 0.4258 | 0.8721 | 0.9338 |
174
+ | No log | 2.6739 | 246 | 0.8919 | 0.4482 | 0.8919 | 0.9444 |
175
+ | No log | 2.6957 | 248 | 0.8985 | 0.3629 | 0.8985 | 0.9479 |
176
+ | No log | 2.7174 | 250 | 0.9163 | 0.3814 | 0.9163 | 0.9572 |
177
+ | No log | 2.7391 | 252 | 0.9307 | 0.3498 | 0.9307 | 0.9647 |
178
+ | No log | 2.7609 | 254 | 0.8915 | 0.3814 | 0.8915 | 0.9442 |
179
+ | No log | 2.7826 | 256 | 0.9213 | 0.3902 | 0.9213 | 0.9599 |
180
+ | No log | 2.8043 | 258 | 1.0892 | 0.3658 | 1.0892 | 1.0437 |
181
+ | No log | 2.8261 | 260 | 1.0678 | 0.3133 | 1.0678 | 1.0334 |
182
+ | No log | 2.8478 | 262 | 1.1188 | 0.3658 | 1.1188 | 1.0577 |
183
+ | No log | 2.8696 | 264 | 1.0249 | 0.3561 | 1.0249 | 1.0124 |
184
+ | No log | 2.8913 | 266 | 0.8599 | 0.4294 | 0.8599 | 0.9273 |
185
+ | No log | 2.9130 | 268 | 0.8334 | 0.5308 | 0.8334 | 0.9129 |
186
+ | No log | 2.9348 | 270 | 0.8235 | 0.5308 | 0.8235 | 0.9074 |
187
+ | No log | 2.9565 | 272 | 0.8273 | 0.5132 | 0.8273 | 0.9096 |
188
+ | No log | 2.9783 | 274 | 0.9381 | 0.4794 | 0.9381 | 0.9685 |
189
+ | No log | 3.0 | 276 | 0.8869 | 0.5618 | 0.8869 | 0.9418 |
190
+ | No log | 3.0217 | 278 | 0.7920 | 0.5226 | 0.7920 | 0.8899 |
191
+ | No log | 3.0435 | 280 | 0.8008 | 0.4138 | 0.8008 | 0.8949 |
192
+ | No log | 3.0652 | 282 | 0.7779 | 0.4397 | 0.7779 | 0.8820 |
193
+ | No log | 3.0870 | 284 | 0.7857 | 0.4781 | 0.7857 | 0.8864 |
194
+ | No log | 3.1087 | 286 | 0.8069 | 0.4244 | 0.8069 | 0.8983 |
195
+ | No log | 3.1304 | 288 | 0.8169 | 0.4547 | 0.8169 | 0.9038 |
196
+ | No log | 3.1522 | 290 | 0.7931 | 0.5065 | 0.7931 | 0.8905 |
197
+ | No log | 3.1739 | 292 | 0.7613 | 0.5476 | 0.7613 | 0.8726 |
198
+ | No log | 3.1957 | 294 | 0.7819 | 0.5505 | 0.7819 | 0.8842 |
199
+ | No log | 3.2174 | 296 | 0.8107 | 0.5312 | 0.8107 | 0.9004 |
200
+ | No log | 3.2391 | 298 | 0.9452 | 0.4440 | 0.9452 | 0.9722 |
201
+ | No log | 3.2609 | 300 | 0.9526 | 0.4588 | 0.9526 | 0.9760 |
202
+ | No log | 3.2826 | 302 | 0.8945 | 0.4475 | 0.8945 | 0.9458 |
203
+ | No log | 3.3043 | 304 | 0.8603 | 0.4507 | 0.8603 | 0.9275 |
204
+ | No log | 3.3261 | 306 | 0.8352 | 0.4815 | 0.8352 | 0.9139 |
205
+ | No log | 3.3478 | 308 | 0.9097 | 0.4701 | 0.9097 | 0.9538 |
206
+ | No log | 3.3696 | 310 | 1.2352 | 0.4137 | 1.2352 | 1.1114 |
207
+ | No log | 3.3913 | 312 | 1.4672 | 0.3497 | 1.4672 | 1.2113 |
208
+ | No log | 3.4130 | 314 | 1.2159 | 0.3824 | 1.2159 | 1.1027 |
209
+ | No log | 3.4348 | 316 | 0.8620 | 0.4866 | 0.8620 | 0.9285 |
210
+ | No log | 3.4565 | 318 | 0.8015 | 0.5076 | 0.8015 | 0.8953 |
211
+ | No log | 3.4783 | 320 | 0.8310 | 0.4926 | 0.8310 | 0.9116 |
212
+ | No log | 3.5 | 322 | 0.8509 | 0.4926 | 0.8509 | 0.9224 |
213
+ | No log | 3.5217 | 324 | 0.8023 | 0.5305 | 0.8023 | 0.8957 |
214
+ | No log | 3.5435 | 326 | 0.8142 | 0.5014 | 0.8142 | 0.9023 |
215
+ | No log | 3.5652 | 328 | 0.8146 | 0.4811 | 0.8146 | 0.9025 |
216
+ | No log | 3.5870 | 330 | 0.8313 | 0.5476 | 0.8313 | 0.9117 |
217
+ | No log | 3.6087 | 332 | 0.8385 | 0.4908 | 0.8385 | 0.9157 |
218
+ | No log | 3.6304 | 334 | 0.8490 | 0.4408 | 0.8490 | 0.9214 |
219
+ | No log | 3.6522 | 336 | 0.7821 | 0.5759 | 0.7821 | 0.8843 |
220
+ | No log | 3.6739 | 338 | 0.8207 | 0.5346 | 0.8207 | 0.9059 |
221
+ | No log | 3.6957 | 340 | 0.8243 | 0.5368 | 0.8243 | 0.9079 |
222
+ | No log | 3.7174 | 342 | 0.8341 | 0.5981 | 0.8341 | 0.9133 |
223
+ | No log | 3.7391 | 344 | 0.8302 | 0.4839 | 0.8302 | 0.9111 |
224
+ | No log | 3.7609 | 346 | 0.8659 | 0.3756 | 0.8659 | 0.9306 |
225
+ | No log | 3.7826 | 348 | 0.8259 | 0.4748 | 0.8259 | 0.9088 |
226
+ | No log | 3.8043 | 350 | 0.8001 | 0.5130 | 0.8001 | 0.8945 |
227
+ | No log | 3.8261 | 352 | 0.7959 | 0.4998 | 0.7959 | 0.8922 |
228
+ | No log | 3.8478 | 354 | 0.8466 | 0.4966 | 0.8466 | 0.9201 |
229
+ | No log | 3.8696 | 356 | 0.8321 | 0.4966 | 0.8321 | 0.9122 |
230
+ | No log | 3.8913 | 358 | 0.7794 | 0.5721 | 0.7794 | 0.8828 |
231
+ | No log | 3.9130 | 360 | 0.7799 | 0.5061 | 0.7799 | 0.8831 |
232
+ | No log | 3.9348 | 362 | 0.8014 | 0.5226 | 0.8014 | 0.8952 |
233
+ | No log | 3.9565 | 364 | 0.7947 | 0.5226 | 0.7947 | 0.8914 |
234
+ | No log | 3.9783 | 366 | 0.7525 | 0.5335 | 0.7525 | 0.8675 |
235
+ | No log | 4.0 | 368 | 0.7287 | 0.5658 | 0.7287 | 0.8536 |
236
+ | No log | 4.0217 | 370 | 0.7254 | 0.6116 | 0.7254 | 0.8517 |
237
+ | No log | 4.0435 | 372 | 0.7189 | 0.6116 | 0.7189 | 0.8479 |
238
+ | No log | 4.0652 | 374 | 0.7149 | 0.5632 | 0.7149 | 0.8455 |
239
+ | No log | 4.0870 | 376 | 0.7456 | 0.5387 | 0.7456 | 0.8635 |
240
+ | No log | 4.1087 | 378 | 0.7435 | 0.5172 | 0.7435 | 0.8623 |
241
+ | No log | 4.1304 | 380 | 0.7370 | 0.5089 | 0.7370 | 0.8585 |
242
+ | No log | 4.1522 | 382 | 0.7393 | 0.4977 | 0.7393 | 0.8598 |
243
+ | No log | 4.1739 | 384 | 0.7531 | 0.5606 | 0.7531 | 0.8678 |
244
+ | No log | 4.1957 | 386 | 0.7695 | 0.4591 | 0.7695 | 0.8772 |
245
+ | No log | 4.2174 | 388 | 0.8085 | 0.4404 | 0.8085 | 0.8992 |
246
+ | No log | 4.2391 | 390 | 0.9350 | 0.4237 | 0.9350 | 0.9669 |
247
+ | No log | 4.2609 | 392 | 0.9872 | 0.4653 | 0.9872 | 0.9936 |
248
+ | No log | 4.2826 | 394 | 0.8607 | 0.4620 | 0.8607 | 0.9277 |
249
+ | No log | 4.3043 | 396 | 0.7578 | 0.5800 | 0.7578 | 0.8705 |
250
+ | No log | 4.3261 | 398 | 0.8406 | 0.5470 | 0.8406 | 0.9168 |
251
+ | No log | 4.3478 | 400 | 0.8431 | 0.5255 | 0.8431 | 0.9182 |
252
+ | No log | 4.3696 | 402 | 0.7807 | 0.5279 | 0.7807 | 0.8836 |
253
+ | No log | 4.3913 | 404 | 0.8069 | 0.4471 | 0.8069 | 0.8983 |
254
+ | No log | 4.4130 | 406 | 0.8148 | 0.4570 | 0.8148 | 0.9027 |
255
+ | No log | 4.4348 | 408 | 0.7973 | 0.4220 | 0.7973 | 0.8929 |
256
+ | No log | 4.4565 | 410 | 0.8814 | 0.4765 | 0.8814 | 0.9388 |
257
+ | No log | 4.4783 | 412 | 1.0000 | 0.4658 | 1.0000 | 1.0000 |
258
+ | No log | 4.5 | 414 | 0.9949 | 0.4894 | 0.9949 | 0.9974 |
259
+ | No log | 4.5217 | 416 | 0.8784 | 0.5111 | 0.8784 | 0.9372 |
260
+ | No log | 4.5435 | 418 | 0.7863 | 0.5012 | 0.7863 | 0.8868 |
261
+ | No log | 4.5652 | 420 | 0.7977 | 0.4471 | 0.7977 | 0.8932 |
262
+ | No log | 4.5870 | 422 | 0.7788 | 0.4471 | 0.7788 | 0.8825 |
263
+ | No log | 4.6087 | 424 | 0.7394 | 0.6120 | 0.7394 | 0.8599 |
264
+ | No log | 4.6304 | 426 | 0.7439 | 0.6358 | 0.7439 | 0.8625 |
265
+ | No log | 4.6522 | 428 | 0.7357 | 0.6421 | 0.7357 | 0.8577 |
266
+ | No log | 4.6739 | 430 | 0.7610 | 0.5192 | 0.7610 | 0.8723 |
267
+ | No log | 4.6957 | 432 | 0.7981 | 0.4659 | 0.7981 | 0.8934 |
268
+ | No log | 4.7174 | 434 | 0.7867 | 0.4225 | 0.7867 | 0.8869 |
269
+ | No log | 4.7391 | 436 | 0.7384 | 0.5759 | 0.7384 | 0.8593 |
270
+ | No log | 4.7609 | 438 | 0.7244 | 0.6364 | 0.7244 | 0.8511 |
271
+ | No log | 4.7826 | 440 | 0.7200 | 0.6301 | 0.7200 | 0.8485 |
272
+ | No log | 4.8043 | 442 | 0.7030 | 0.6364 | 0.7030 | 0.8385 |
273
+ | No log | 4.8261 | 444 | 0.6990 | 0.5982 | 0.6990 | 0.8360 |
274
+ | No log | 4.8478 | 446 | 0.7547 | 0.5133 | 0.7547 | 0.8687 |
275
+ | No log | 4.8696 | 448 | 0.7453 | 0.5451 | 0.7453 | 0.8633 |
276
+ | No log | 4.8913 | 450 | 0.6826 | 0.5661 | 0.6826 | 0.8262 |
277
+ | No log | 4.9130 | 452 | 0.6591 | 0.6082 | 0.6591 | 0.8119 |
278
+ | No log | 4.9348 | 454 | 0.6762 | 0.5562 | 0.6762 | 0.8223 |
279
+ | No log | 4.9565 | 456 | 0.7117 | 0.5647 | 0.7117 | 0.8436 |
280
+ | No log | 4.9783 | 458 | 0.7367 | 0.5779 | 0.7367 | 0.8583 |
281
+ | No log | 5.0 | 460 | 0.7341 | 0.5779 | 0.7341 | 0.8568 |
282
+ | No log | 5.0217 | 462 | 0.7173 | 0.5010 | 0.7173 | 0.8469 |
283
+ | No log | 5.0435 | 464 | 0.7087 | 0.5112 | 0.7087 | 0.8418 |
284
+ | No log | 5.0652 | 466 | 0.7130 | 0.5375 | 0.7130 | 0.8444 |
285
+ | No log | 5.0870 | 468 | 0.7298 | 0.5045 | 0.7298 | 0.8543 |
286
+ | No log | 5.1087 | 470 | 0.7387 | 0.4832 | 0.7387 | 0.8595 |
287
+ | No log | 5.1304 | 472 | 0.7204 | 0.5244 | 0.7204 | 0.8488 |
288
+ | No log | 5.1522 | 474 | 0.6991 | 0.5505 | 0.6991 | 0.8361 |
289
+ | No log | 5.1739 | 476 | 0.6924 | 0.5684 | 0.6924 | 0.8321 |
290
+ | No log | 5.1957 | 478 | 0.7963 | 0.5417 | 0.7963 | 0.8923 |
291
+ | No log | 5.2174 | 480 | 0.8787 | 0.5605 | 0.8787 | 0.9374 |
292
+ | No log | 5.2391 | 482 | 0.7582 | 0.5766 | 0.7582 | 0.8708 |
293
+ | No log | 5.2609 | 484 | 0.6878 | 0.5802 | 0.6878 | 0.8293 |
294
+ | No log | 5.2826 | 486 | 0.6938 | 0.5957 | 0.6938 | 0.8330 |
295
+ | No log | 5.3043 | 488 | 0.7305 | 0.5079 | 0.7305 | 0.8547 |
296
+ | No log | 5.3261 | 490 | 0.7806 | 0.4476 | 0.7806 | 0.8835 |
297
+ | No log | 5.3478 | 492 | 0.8190 | 0.4808 | 0.8190 | 0.9050 |
298
+ | No log | 5.3696 | 494 | 0.7693 | 0.5007 | 0.7693 | 0.8771 |
299
+ | No log | 5.3913 | 496 | 0.7414 | 0.6067 | 0.7414 | 0.8610 |
300
+ | No log | 5.4130 | 498 | 0.7271 | 0.5463 | 0.7271 | 0.8527 |
301
+ | 0.3663 | 5.4348 | 500 | 0.7277 | 0.5763 | 0.7277 | 0.8530 |
302
+ | 0.3663 | 5.4565 | 502 | 0.7431 | 0.5112 | 0.7431 | 0.8620 |
303
+ | 0.3663 | 5.4783 | 504 | 0.7604 | 0.5061 | 0.7604 | 0.8720 |
304
+ | 0.3663 | 5.5 | 506 | 0.7868 | 0.4220 | 0.7868 | 0.8870 |
305
+ | 0.3663 | 5.5217 | 508 | 0.8081 | 0.4077 | 0.8081 | 0.8990 |
306
+ | 0.3663 | 5.5435 | 510 | 0.8177 | 0.4036 | 0.8177 | 0.9043 |
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 sha256:254f81aa033bc7e2cf70e361af205c1f1f6576778a28c8e9f1a336b09b34cfe7
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5e34c5c4a44c3052c2dd4eb592f3795297c14f78147d5e5b8f3b01e812fc8c95
3
+ size 5304