Hugging Face's logo

MayBashendy
/
ArabicNewSplits7_B_usingWellWrittenEssays_FineTuningAraBERT_run2_AugV5_k8_task1_organization

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
 Community
MayBashendy commited on
Commit
a3e798f
·
verified ·
1 Parent(s): 3a5a513

Training in progress, step 500

Browse files
Files changed (4) hide show
  1. README.md +315 -0
  2. config.json +32 -0
  3. model.safetensors +3 -0
  4. training_args.bin +3 -0
README.md ADDED
@@ -0,0 +1,315 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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_run2_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_run2_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: 0.6448
19
+ - Qwk: 0.7383
20
+ - Mse: 0.6448
21
+ - Rmse: 0.8030
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 | 6.6863 | 0.0308 | 6.6863 | 2.5858 |
53
+ | No log | 0.1111 | 4 | 4.4449 | 0.0397 | 4.4449 | 2.1083 |
54
+ | No log | 0.1667 | 6 | 3.0327 | 0.0513 | 3.0327 | 1.7415 |
55
+ | No log | 0.2222 | 8 | 2.5763 | 0.1216 | 2.5763 | 1.6051 |
56
+ | No log | 0.2778 | 10 | 2.0104 | 0.1719 | 2.0104 | 1.4179 |
57
+ | No log | 0.3333 | 12 | 2.1249 | 0.1695 | 2.1249 | 1.4577 |
58
+ | No log | 0.3889 | 14 | 3.1642 | 0.0121 | 3.1642 | 1.7788 |
59
+ | No log | 0.4444 | 16 | 3.2038 | 0.0606 | 3.2038 | 1.7899 |
60
+ | No log | 0.5 | 18 | 2.6844 | 0.0516 | 2.6844 | 1.6384 |
61
+ | No log | 0.5556 | 20 | 1.9254 | 0.2393 | 1.9254 | 1.3876 |
62
+ | No log | 0.6111 | 22 | 1.7399 | 0.2037 | 1.7399 | 1.3191 |
63
+ | No log | 0.6667 | 24 | 1.7876 | 0.1964 | 1.7876 | 1.3370 |
64
+ | No log | 0.7222 | 26 | 1.7766 | 0.2759 | 1.7766 | 1.3329 |
65
+ | No log | 0.7778 | 28 | 1.6003 | 0.2523 | 1.6003 | 1.2650 |
66
+ | No log | 0.8333 | 30 | 1.5205 | 0.2202 | 1.5205 | 1.2331 |
67
+ | No log | 0.8889 | 32 | 1.5923 | 0.3009 | 1.5923 | 1.2619 |
68
+ | No log | 0.9444 | 34 | 1.8350 | 0.3810 | 1.8350 | 1.3546 |
69
+ | No log | 1.0 | 36 | 2.1694 | 0.1722 | 2.1694 | 1.4729 |
70
+ | No log | 1.0556 | 38 | 2.4164 | 0.0994 | 2.4164 | 1.5545 |
71
+ | No log | 1.1111 | 40 | 2.4589 | 0.0994 | 2.4589 | 1.5681 |
72
+ | No log | 1.1667 | 42 | 2.2544 | 0.1951 | 2.2544 | 1.5015 |
73
+ | No log | 1.2222 | 44 | 1.7411 | 0.4196 | 1.7411 | 1.3195 |
74
+ | No log | 1.2778 | 46 | 1.3599 | 0.4818 | 1.3599 | 1.1662 |
75
+ | No log | 1.3333 | 48 | 1.1882 | 0.4962 | 1.1882 | 1.0900 |
76
+ | No log | 1.3889 | 50 | 1.4085 | 0.5166 | 1.4085 | 1.1868 |
77
+ | No log | 1.4444 | 52 | 2.1021 | 0.3708 | 2.1021 | 1.4499 |
78
+ | No log | 1.5 | 54 | 2.2156 | 0.3333 | 2.2156 | 1.4885 |
79
+ | No log | 1.5556 | 56 | 1.9779 | 0.4277 | 1.9779 | 1.4064 |
80
+ | No log | 1.6111 | 58 | 1.4287 | 0.4444 | 1.4287 | 1.1953 |
81
+ | No log | 1.6667 | 60 | 0.9571 | 0.6 | 0.9571 | 0.9783 |
82
+ | No log | 1.7222 | 62 | 1.0344 | 0.5161 | 1.0344 | 1.0171 |
83
+ | No log | 1.7778 | 64 | 0.9984 | 0.6202 | 0.9984 | 0.9992 |
84
+ | No log | 1.8333 | 66 | 0.8824 | 0.625 | 0.8824 | 0.9394 |
85
+ | No log | 1.8889 | 68 | 0.9997 | 0.6015 | 0.9997 | 0.9998 |
86
+ | No log | 1.9444 | 70 | 0.8712 | 0.7133 | 0.8712 | 0.9334 |
87
+ | No log | 2.0 | 72 | 0.7382 | 0.6713 | 0.7382 | 0.8592 |
88
+ | No log | 2.0556 | 74 | 0.7159 | 0.6980 | 0.7159 | 0.8461 |
89
+ | No log | 2.1111 | 76 | 0.8204 | 0.7114 | 0.8204 | 0.9058 |
90
+ | No log | 2.1667 | 78 | 1.2901 | 0.4460 | 1.2901 | 1.1358 |
91
+ | No log | 2.2222 | 80 | 1.5230 | 0.3676 | 1.5230 | 1.2341 |
92
+ | No log | 2.2778 | 82 | 0.9660 | 0.7134 | 0.9660 | 0.9829 |
93
+ | No log | 2.3333 | 84 | 0.6053 | 0.7799 | 0.6053 | 0.7780 |
94
+ | No log | 2.3889 | 86 | 0.5178 | 0.8263 | 0.5178 | 0.7196 |
95
+ | No log | 2.4444 | 88 | 0.5968 | 0.8402 | 0.5968 | 0.7725 |
96
+ | No log | 2.5 | 90 | 0.5106 | 0.8148 | 0.5106 | 0.7146 |
97
+ | No log | 2.5556 | 92 | 0.6641 | 0.7595 | 0.6641 | 0.8149 |
98
+ | No log | 2.6111 | 94 | 0.6292 | 0.7662 | 0.6292 | 0.7932 |
99
+ | No log | 2.6667 | 96 | 0.6095 | 0.7550 | 0.6095 | 0.7807 |
100
+ | No log | 2.7222 | 98 | 1.2339 | 0.6296 | 1.2339 | 1.1108 |
101
+ | No log | 2.7778 | 100 | 1.1708 | 0.6296 | 1.1708 | 1.0820 |
102
+ | No log | 2.8333 | 102 | 0.6770 | 0.7067 | 0.6770 | 0.8228 |
103
+ | No log | 2.8889 | 104 | 0.7575 | 0.7020 | 0.7575 | 0.8704 |
104
+ | No log | 2.9444 | 106 | 1.2615 | 0.5205 | 1.2615 | 1.1232 |
105
+ | No log | 3.0 | 108 | 1.4017 | 0.4762 | 1.4017 | 1.1839 |
106
+ | No log | 3.0556 | 110 | 1.1012 | 0.5972 | 1.1012 | 1.0494 |
107
+ | No log | 3.1111 | 112 | 0.7273 | 0.7613 | 0.7273 | 0.8528 |
108
+ | No log | 3.1667 | 114 | 0.7896 | 0.7273 | 0.7896 | 0.8886 |
109
+ | No log | 3.2222 | 116 | 0.9395 | 0.6755 | 0.9395 | 0.9693 |
110
+ | No log | 3.2778 | 118 | 0.8150 | 0.6933 | 0.8150 | 0.9028 |
111
+ | No log | 3.3333 | 120 | 0.6674 | 0.7733 | 0.6674 | 0.8169 |
112
+ | No log | 3.3889 | 122 | 0.7614 | 0.7092 | 0.7614 | 0.8726 |
113
+ | No log | 3.4444 | 124 | 0.8209 | 0.6809 | 0.8209 | 0.9060 |
114
+ | No log | 3.5 | 126 | 0.7219 | 0.7092 | 0.7219 | 0.8496 |
115
+ | No log | 3.5556 | 128 | 0.5732 | 0.7949 | 0.5732 | 0.7571 |
116
+ | No log | 3.6111 | 130 | 0.6403 | 0.7320 | 0.6403 | 0.8002 |
117
+ | No log | 3.6667 | 132 | 0.6306 | 0.7421 | 0.6306 | 0.7941 |
118
+ | No log | 3.7222 | 134 | 0.6237 | 0.7516 | 0.6237 | 0.7897 |
119
+ | No log | 3.7778 | 136 | 0.6116 | 0.7848 | 0.6116 | 0.7821 |
120
+ | No log | 3.8333 | 138 | 0.6125 | 0.7613 | 0.6125 | 0.7826 |
121
+ | No log | 3.8889 | 140 | 0.5820 | 0.7643 | 0.5820 | 0.7629 |
122
+ | No log | 3.9444 | 142 | 0.6670 | 0.75 | 0.6670 | 0.8167 |
123
+ | No log | 4.0 | 144 | 0.7487 | 0.6846 | 0.7487 | 0.8653 |
124
+ | No log | 4.0556 | 146 | 0.6693 | 0.7310 | 0.6693 | 0.8181 |
125
+ | No log | 4.1111 | 148 | 0.6197 | 0.7792 | 0.6197 | 0.7872 |
126
+ | No log | 4.1667 | 150 | 0.6404 | 0.7613 | 0.6404 | 0.8002 |
127
+ | No log | 4.2222 | 152 | 0.6391 | 0.7692 | 0.6391 | 0.7994 |
128
+ | No log | 4.2778 | 154 | 0.6551 | 0.7742 | 0.6551 | 0.8094 |
129
+ | No log | 4.3333 | 156 | 0.7018 | 0.7613 | 0.7018 | 0.8377 |
130
+ | No log | 4.3889 | 158 | 0.6970 | 0.7662 | 0.6970 | 0.8349 |
131
+ | No log | 4.4444 | 160 | 0.6692 | 0.7895 | 0.6692 | 0.8181 |
132
+ | No log | 4.5 | 162 | 0.6795 | 0.7733 | 0.6795 | 0.8243 |
133
+ | No log | 4.5556 | 164 | 0.6392 | 0.7662 | 0.6392 | 0.7995 |
134
+ | No log | 4.6111 | 166 | 0.6614 | 0.7582 | 0.6614 | 0.8132 |
135
+ | No log | 4.6667 | 168 | 0.6205 | 0.7643 | 0.6205 | 0.7877 |
136
+ | No log | 4.7222 | 170 | 0.6834 | 0.7531 | 0.6834 | 0.8267 |
137
+ | No log | 4.7778 | 172 | 0.7086 | 0.7226 | 0.7086 | 0.8418 |
138
+ | No log | 4.8333 | 174 | 0.6583 | 0.7582 | 0.6583 | 0.8113 |
139
+ | No log | 4.8889 | 176 | 0.6558 | 0.7662 | 0.6558 | 0.8098 |
140
+ | No log | 4.9444 | 178 | 0.6924 | 0.7613 | 0.6924 | 0.8321 |
141
+ | No log | 5.0 | 180 | 0.6249 | 0.76 | 0.6249 | 0.7905 |
142
+ | No log | 5.0556 | 182 | 0.6136 | 0.7733 | 0.6136 | 0.7834 |
143
+ | No log | 5.1111 | 184 | 0.6201 | 0.7568 | 0.6201 | 0.7875 |
144
+ | No log | 5.1667 | 186 | 0.6360 | 0.7347 | 0.6360 | 0.7975 |
145
+ | No log | 5.2222 | 188 | 0.6380 | 0.7632 | 0.6380 | 0.7988 |
146
+ | No log | 5.2778 | 190 | 0.6970 | 0.7517 | 0.6970 | 0.8349 |
147
+ | No log | 5.3333 | 192 | 0.7107 | 0.7143 | 0.7107 | 0.8430 |
148
+ | No log | 5.3889 | 194 | 0.6795 | 0.7662 | 0.6795 | 0.8243 |
149
+ | No log | 5.4444 | 196 | 0.6569 | 0.7582 | 0.6569 | 0.8105 |
150
+ | No log | 5.5 | 198 | 0.6991 | 0.7248 | 0.6991 | 0.8361 |
151
+ | No log | 5.5556 | 200 | 0.7057 | 0.7248 | 0.7057 | 0.8401 |
152
+ | No log | 5.6111 | 202 | 0.6815 | 0.7582 | 0.6815 | 0.8255 |
153
+ | No log | 5.6667 | 204 | 0.6545 | 0.7613 | 0.6545 | 0.8090 |
154
+ | No log | 5.7222 | 206 | 0.6140 | 0.7662 | 0.6140 | 0.7836 |
155
+ | No log | 5.7778 | 208 | 0.5965 | 0.7821 | 0.5965 | 0.7723 |
156
+ | No log | 5.8333 | 210 | 0.6042 | 0.7742 | 0.6042 | 0.7773 |
157
+ | No log | 5.8889 | 212 | 0.7193 | 0.7632 | 0.7193 | 0.8481 |
158
+ | No log | 5.9444 | 214 | 0.7184 | 0.7123 | 0.7184 | 0.8476 |
159
+ | No log | 6.0 | 216 | 0.6531 | 0.7248 | 0.6531 | 0.8081 |
160
+ | No log | 6.0556 | 218 | 0.6716 | 0.7532 | 0.6716 | 0.8195 |
161
+ | No log | 6.1111 | 220 | 0.6538 | 0.7692 | 0.6538 | 0.8086 |
162
+ | No log | 6.1667 | 222 | 0.6281 | 0.7771 | 0.6281 | 0.7925 |
163
+ | No log | 6.2222 | 224 | 0.6122 | 0.7682 | 0.6122 | 0.7824 |
164
+ | No log | 6.2778 | 226 | 0.6572 | 0.76 | 0.6572 | 0.8107 |
165
+ | No log | 6.3333 | 228 | 0.7288 | 0.7448 | 0.7288 | 0.8537 |
166
+ | No log | 6.3889 | 230 | 0.7627 | 0.7310 | 0.7627 | 0.8733 |
167
+ | No log | 6.4444 | 232 | 0.6877 | 0.7682 | 0.6877 | 0.8293 |
168
+ | No log | 6.5 | 234 | 0.6473 | 0.7871 | 0.6473 | 0.8045 |
169
+ | No log | 6.5556 | 236 | 0.6983 | 0.75 | 0.6983 | 0.8356 |
170
+ | No log | 6.6111 | 238 | 0.6527 | 0.7792 | 0.6527 | 0.8079 |
171
+ | No log | 6.6667 | 240 | 0.5438 | 0.8 | 0.5438 | 0.7374 |
172
+ | No log | 6.7222 | 242 | 0.5314 | 0.7843 | 0.5314 | 0.7290 |
173
+ | No log | 6.7778 | 244 | 0.5464 | 0.7843 | 0.5464 | 0.7392 |
174
+ | No log | 6.8333 | 246 | 0.5574 | 0.8182 | 0.5574 | 0.7466 |
175
+ | No log | 6.8889 | 248 | 0.6252 | 0.8182 | 0.6252 | 0.7907 |
176
+ | No log | 6.9444 | 250 | 0.6183 | 0.8182 | 0.6183 | 0.7863 |
177
+ | No log | 7.0 | 252 | 0.5833 | 0.7947 | 0.5833 | 0.7637 |
178
+ | No log | 7.0556 | 254 | 0.6070 | 0.7947 | 0.6070 | 0.7791 |
179
+ | No log | 7.1111 | 256 | 0.5639 | 0.8153 | 0.5639 | 0.7509 |
180
+ | No log | 7.1667 | 258 | 0.5558 | 0.8153 | 0.5558 | 0.7455 |
181
+ | No log | 7.2222 | 260 | 0.5581 | 0.8153 | 0.5581 | 0.7471 |
182
+ | No log | 7.2778 | 262 | 0.5770 | 0.8153 | 0.5770 | 0.7596 |
183
+ | No log | 7.3333 | 264 | 0.5628 | 0.8153 | 0.5628 | 0.7502 |
184
+ | No log | 7.3889 | 266 | 0.6046 | 0.7712 | 0.6046 | 0.7776 |
185
+ | No log | 7.4444 | 268 | 0.6318 | 0.7662 | 0.6318 | 0.7949 |
186
+ | No log | 7.5 | 270 | 0.6697 | 0.75 | 0.6697 | 0.8183 |
187
+ | No log | 7.5556 | 272 | 0.6832 | 0.75 | 0.6832 | 0.8266 |
188
+ | No log | 7.6111 | 274 | 0.5283 | 0.7821 | 0.5283 | 0.7268 |
189
+ | No log | 7.6667 | 276 | 0.5307 | 0.7949 | 0.5307 | 0.7285 |
190
+ | No log | 7.7222 | 278 | 0.5856 | 0.7582 | 0.5856 | 0.7652 |
191
+ | No log | 7.7778 | 280 | 0.6006 | 0.7582 | 0.6006 | 0.7750 |
192
+ | No log | 7.8333 | 282 | 0.6385 | 0.7815 | 0.6385 | 0.7991 |
193
+ | No log | 7.8889 | 284 | 0.6836 | 0.75 | 0.6836 | 0.8268 |
194
+ | No log | 7.9444 | 286 | 0.7083 | 0.7448 | 0.7083 | 0.8416 |
195
+ | No log | 8.0 | 288 | 0.6982 | 0.76 | 0.6982 | 0.8356 |
196
+ | No log | 8.0556 | 290 | 0.7082 | 0.7517 | 0.7082 | 0.8416 |
197
+ | No log | 8.1111 | 292 | 0.7256 | 0.72 | 0.7256 | 0.8518 |
198
+ | No log | 8.1667 | 294 | 0.6871 | 0.7682 | 0.6871 | 0.8289 |
199
+ | No log | 8.2222 | 296 | 0.6649 | 0.7582 | 0.6649 | 0.8154 |
200
+ | No log | 8.2778 | 298 | 0.6689 | 0.7821 | 0.6689 | 0.8179 |
201
+ | No log | 8.3333 | 300 | 0.7082 | 0.7320 | 0.7082 | 0.8416 |
202
+ | No log | 8.3889 | 302 | 0.6918 | 0.7403 | 0.6918 | 0.8317 |
203
+ | No log | 8.4444 | 304 | 0.6520 | 0.7662 | 0.6520 | 0.8075 |
204
+ | No log | 8.5 | 306 | 0.6247 | 0.7662 | 0.6247 | 0.7904 |
205
+ | No log | 8.5556 | 308 | 0.6213 | 0.7742 | 0.6213 | 0.7882 |
206
+ | No log | 8.6111 | 310 | 0.6331 | 0.7742 | 0.6331 | 0.7957 |
207
+ | No log | 8.6667 | 312 | 0.6216 | 0.7712 | 0.6216 | 0.7884 |
208
+ | No log | 8.7222 | 314 | 0.6406 | 0.7632 | 0.6406 | 0.8004 |
209
+ | No log | 8.7778 | 316 | 0.6745 | 0.7467 | 0.6745 | 0.8213 |
210
+ | No log | 8.8333 | 318 | 0.6998 | 0.7162 | 0.6998 | 0.8365 |
211
+ | No log | 8.8889 | 320 | 0.6623 | 0.7483 | 0.6623 | 0.8138 |
212
+ | No log | 8.9444 | 322 | 0.6077 | 0.7733 | 0.6077 | 0.7796 |
213
+ | No log | 9.0 | 324 | 0.6256 | 0.7568 | 0.6256 | 0.7909 |
214
+ | No log | 9.0556 | 326 | 0.7117 | 0.75 | 0.7117 | 0.8436 |
215
+ | No log | 9.1111 | 328 | 0.6792 | 0.7578 | 0.6792 | 0.8242 |
216
+ | No log | 9.1667 | 330 | 0.5625 | 0.7857 | 0.5625 | 0.7500 |
217
+ | No log | 9.2222 | 332 | 0.5430 | 0.8 | 0.5430 | 0.7369 |
218
+ | No log | 9.2778 | 334 | 0.5777 | 0.7712 | 0.5777 | 0.7601 |
219
+ | No log | 9.3333 | 336 | 0.6253 | 0.7534 | 0.6253 | 0.7907 |
220
+ | No log | 9.3889 | 338 | 0.6849 | 0.7534 | 0.6849 | 0.8276 |
221
+ | No log | 9.4444 | 340 | 0.6898 | 0.7534 | 0.6898 | 0.8305 |
222
+ | No log | 9.5 | 342 | 0.6386 | 0.7517 | 0.6386 | 0.7991 |
223
+ | No log | 9.5556 | 344 | 0.6227 | 0.7417 | 0.6227 | 0.7891 |
224
+ | No log | 9.6111 | 346 | 0.5379 | 0.7922 | 0.5379 | 0.7334 |
225
+ | No log | 9.6667 | 348 | 0.5009 | 0.8302 | 0.5009 | 0.7078 |
226
+ | No log | 9.7222 | 350 | 0.4832 | 0.8364 | 0.4832 | 0.6951 |
227
+ | No log | 9.7778 | 352 | 0.4887 | 0.8364 | 0.4887 | 0.6991 |
228
+ | No log | 9.8333 | 354 | 0.4913 | 0.8364 | 0.4913 | 0.7009 |
229
+ | No log | 9.8889 | 356 | 0.5048 | 0.8121 | 0.5048 | 0.7105 |
230
+ | No log | 9.9444 | 358 | 0.4956 | 0.85 | 0.4956 | 0.7040 |
231
+ | No log | 10.0 | 360 | 0.5502 | 0.8148 | 0.5502 | 0.7418 |
232
+ | No log | 10.0556 | 362 | 0.5782 | 0.85 | 0.5782 | 0.7604 |
233
+ | No log | 10.1111 | 364 | 0.6060 | 0.8258 | 0.6060 | 0.7785 |
234
+ | No log | 10.1667 | 366 | 0.6414 | 0.7947 | 0.6414 | 0.8009 |
235
+ | No log | 10.2222 | 368 | 0.6577 | 0.7651 | 0.6577 | 0.8110 |
236
+ | No log | 10.2778 | 370 | 0.6484 | 0.7651 | 0.6484 | 0.8052 |
237
+ | No log | 10.3333 | 372 | 0.6784 | 0.7467 | 0.6784 | 0.8236 |
238
+ | No log | 10.3889 | 374 | 0.6601 | 0.7550 | 0.6601 | 0.8124 |
239
+ | No log | 10.4444 | 376 | 0.6284 | 0.7651 | 0.6284 | 0.7927 |
240
+ | No log | 10.5 | 378 | 0.5969 | 0.7682 | 0.5969 | 0.7726 |
241
+ | No log | 10.5556 | 380 | 0.5871 | 0.7682 | 0.5871 | 0.7662 |
242
+ | No log | 10.6111 | 382 | 0.6257 | 0.7763 | 0.6257 | 0.7910 |
243
+ | No log | 10.6667 | 384 | 0.6017 | 0.7763 | 0.6017 | 0.7757 |
244
+ | No log | 10.7222 | 386 | 0.5515 | 0.8101 | 0.5515 | 0.7426 |
245
+ | No log | 10.7778 | 388 | 0.5981 | 0.7821 | 0.5981 | 0.7734 |
246
+ | No log | 10.8333 | 390 | 0.5837 | 0.7875 | 0.5837 | 0.7640 |
247
+ | No log | 10.8889 | 392 | 0.5665 | 0.825 | 0.5665 | 0.7526 |
248
+ | No log | 10.9444 | 394 | 0.5946 | 0.7871 | 0.5946 | 0.7711 |
249
+ | No log | 11.0 | 396 | 0.6349 | 0.7712 | 0.6349 | 0.7968 |
250
+ | No log | 11.0556 | 398 | 0.6676 | 0.7417 | 0.6676 | 0.8171 |
251
+ | No log | 11.1111 | 400 | 0.6706 | 0.7534 | 0.6706 | 0.8189 |
252
+ | No log | 11.1667 | 402 | 0.6609 | 0.8054 | 0.6609 | 0.8130 |
253
+ | No log | 11.2222 | 404 | 0.6515 | 0.8054 | 0.6515 | 0.8072 |
254
+ | No log | 11.2778 | 406 | 0.6407 | 0.7785 | 0.6407 | 0.8005 |
255
+ | No log | 11.3333 | 408 | 0.6166 | 0.7763 | 0.6166 | 0.7852 |
256
+ | No log | 11.3889 | 410 | 0.6092 | 0.7712 | 0.6092 | 0.7805 |
257
+ | No log | 11.4444 | 412 | 0.5997 | 0.7821 | 0.5997 | 0.7744 |
258
+ | No log | 11.5 | 414 | 0.6354 | 0.7821 | 0.6354 | 0.7971 |
259
+ | No log | 11.5556 | 416 | 0.6563 | 0.7843 | 0.6563 | 0.8101 |
260
+ | No log | 11.6111 | 418 | 0.6681 | 0.7432 | 0.6681 | 0.8174 |
261
+ | No log | 11.6667 | 420 | 0.6766 | 0.7260 | 0.6766 | 0.8226 |
262
+ | No log | 11.7222 | 422 | 0.6475 | 0.7483 | 0.6475 | 0.8046 |
263
+ | No log | 11.7778 | 424 | 0.6151 | 0.7733 | 0.6151 | 0.7843 |
264
+ | No log | 11.8333 | 426 | 0.6473 | 0.7843 | 0.6473 | 0.8046 |
265
+ | No log | 11.8889 | 428 | 0.6566 | 0.7682 | 0.6566 | 0.8103 |
266
+ | No log | 11.9444 | 430 | 0.6760 | 0.7895 | 0.6760 | 0.8222 |
267
+ | No log | 12.0 | 432 | 0.7018 | 0.7703 | 0.7018 | 0.8377 |
268
+ | No log | 12.0556 | 434 | 0.7157 | 0.7133 | 0.7157 | 0.8460 |
269
+ | No log | 12.1111 | 436 | 0.7153 | 0.7347 | 0.7153 | 0.8458 |
270
+ | No log | 12.1667 | 438 | 0.7855 | 0.7578 | 0.7855 | 0.8863 |
271
+ | No log | 12.2222 | 440 | 0.7605 | 0.7578 | 0.7605 | 0.8720 |
272
+ | No log | 12.2778 | 442 | 0.6798 | 0.7432 | 0.6798 | 0.8245 |
273
+ | No log | 12.3333 | 444 | 0.7281 | 0.7143 | 0.7281 | 0.8533 |
274
+ | No log | 12.3889 | 446 | 0.8010 | 0.6471 | 0.8010 | 0.8950 |
275
+ | No log | 12.4444 | 448 | 0.8302 | 0.5954 | 0.8302 | 0.9111 |
276
+ | No log | 12.5 | 450 | 0.7915 | 0.7007 | 0.7915 | 0.8896 |
277
+ | No log | 12.5556 | 452 | 0.7543 | 0.7606 | 0.7543 | 0.8685 |
278
+ | No log | 12.6111 | 454 | 0.7282 | 0.7606 | 0.7282 | 0.8534 |
279
+ | No log | 12.6667 | 456 | 0.6902 | 0.7324 | 0.6902 | 0.8308 |
280
+ | No log | 12.7222 | 458 | 0.6754 | 0.7651 | 0.6754 | 0.8218 |
281
+ | No log | 12.7778 | 460 | 0.6673 | 0.7974 | 0.6673 | 0.8169 |
282
+ | No log | 12.8333 | 462 | 0.6667 | 0.7974 | 0.6667 | 0.8165 |
283
+ | No log | 12.8889 | 464 | 0.6488 | 0.7733 | 0.6488 | 0.8055 |
284
+ | No log | 12.9444 | 466 | 0.6828 | 0.7586 | 0.6828 | 0.8263 |
285
+ | No log | 13.0 | 468 | 0.7125 | 0.75 | 0.7125 | 0.8441 |
286
+ | No log | 13.0556 | 470 | 0.7236 | 0.7448 | 0.7236 | 0.8506 |
287
+ | No log | 13.1111 | 472 | 0.7393 | 0.6901 | 0.7393 | 0.8598 |
288
+ | No log | 13.1667 | 474 | 0.7396 | 0.6897 | 0.7396 | 0.8600 |
289
+ | No log | 13.2222 | 476 | 0.6917 | 0.75 | 0.6917 | 0.8317 |
290
+ | No log | 13.2778 | 478 | 0.5826 | 0.8077 | 0.5826 | 0.7633 |
291
+ | No log | 13.3333 | 480 | 0.5260 | 0.8026 | 0.5260 | 0.7253 |
292
+ | No log | 13.3889 | 482 | 0.5410 | 0.7867 | 0.5410 | 0.7355 |
293
+ | No log | 13.4444 | 484 | 0.5790 | 0.7639 | 0.5790 | 0.7609 |
294
+ | No log | 13.5 | 486 | 0.6358 | 0.7746 | 0.6358 | 0.7974 |
295
+ | No log | 13.5556 | 488 | 0.7130 | 0.7299 | 0.7130 | 0.8444 |
296
+ | No log | 13.6111 | 490 | 0.7696 | 0.7015 | 0.7696 | 0.8773 |
297
+ | No log | 13.6667 | 492 | 0.7673 | 0.7015 | 0.7673 | 0.8760 |
298
+ | No log | 13.7222 | 494 | 0.7248 | 0.7007 | 0.7248 | 0.8513 |
299
+ | No log | 13.7778 | 496 | 0.6725 | 0.7194 | 0.6725 | 0.8200 |
300
+ | No log | 13.8333 | 498 | 0.6191 | 0.7639 | 0.6191 | 0.7868 |
301
+ | 0.4219 | 13.8889 | 500 | 0.5878 | 0.7862 | 0.5878 | 0.7667 |
302
+ | 0.4219 | 13.9444 | 502 | 0.5533 | 0.7871 | 0.5533 | 0.7439 |
303
+ | 0.4219 | 14.0 | 504 | 0.5787 | 0.7654 | 0.5787 | 0.7607 |
304
+ | 0.4219 | 14.0556 | 506 | 0.6191 | 0.7595 | 0.6191 | 0.7868 |
305
+ | 0.4219 | 14.1111 | 508 | 0.6732 | 0.7421 | 0.6732 | 0.8205 |
306
+ | 0.4219 | 14.1667 | 510 | 0.6727 | 0.7397 | 0.6727 | 0.8202 |
307
+ | 0.4219 | 14.2222 | 512 | 0.6448 | 0.7383 | 0.6448 | 0.8030 |
308
+
309
+
310
+ ### Framework versions
311
+
312
+ - Transformers 4.44.2
313
+ - Pytorch 2.4.0+cu118
314
+ - Datasets 2.21.0
315
+ - 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 sha256:18c703470ab10d9648d85a1d462a5fe51ec9196a5e70e3f01bc0e9cec59c839a
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9107c500ccfe2fbd7074111982b74a58ed6ded6a90d69c56cf57a2942b961d67
3
+ size 5368