Hugging Face's logo

MayBashendy
/
ArabicNewSplits7_usingALLEssays_FineTuningAraBERT_run3_AugV5_k2_task5_organization

Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Model card Files
xet
 Community
MayBashendy commited on
Commit
1ab23ca
·
verified ·
1 Parent(s): f9701d8

Training in progress, step 500

Browse files
Files changed (4) hide show
  1. README.md +209 -0
  2. config.json +32 -0
  3. model.safetensors +3 -0
  4. training_args.bin +3 -0
README.md ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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_k1_task5_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_k1_task5_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: 1.4393
19
+ - Qwk: 0.0075
20
+ - Mse: 1.4393
21
+ - Rmse: 1.1997
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 | 0.6667 | 2 | 3.9596 | -0.0033 | 3.9596 | 1.9899 |
53
+ | No log | 1.3333 | 4 | 2.3300 | 0.0790 | 2.3300 | 1.5264 |
54
+ | No log | 2.0 | 6 | 1.8935 | -0.0572 | 1.8935 | 1.3761 |
55
+ | No log | 2.6667 | 8 | 1.3708 | 0.1009 | 1.3708 | 1.1708 |
56
+ | No log | 3.3333 | 10 | 1.3418 | -0.0292 | 1.3418 | 1.1584 |
57
+ | No log | 4.0 | 12 | 1.5601 | 0.0509 | 1.5601 | 1.2490 |
58
+ | No log | 4.6667 | 14 | 1.5239 | 0.0120 | 1.5239 | 1.2345 |
59
+ | No log | 5.3333 | 16 | 1.4218 | -0.0383 | 1.4218 | 1.1924 |
60
+ | No log | 6.0 | 18 | 1.2589 | 0.0545 | 1.2589 | 1.1220 |
61
+ | No log | 6.6667 | 20 | 1.1636 | 0.0606 | 1.1636 | 1.0787 |
62
+ | No log | 7.3333 | 22 | 1.1787 | 0.0078 | 1.1787 | 1.0857 |
63
+ | No log | 8.0 | 24 | 1.2630 | -0.0100 | 1.2630 | 1.1238 |
64
+ | No log | 8.6667 | 26 | 1.4538 | -0.1111 | 1.4538 | 1.2057 |
65
+ | No log | 9.3333 | 28 | 1.5863 | -0.0614 | 1.5863 | 1.2595 |
66
+ | No log | 10.0 | 30 | 1.7460 | -0.0829 | 1.7460 | 1.3214 |
67
+ | No log | 10.6667 | 32 | 1.8288 | -0.1299 | 1.8288 | 1.3523 |
68
+ | No log | 11.3333 | 34 | 1.7362 | -0.0289 | 1.7362 | 1.3176 |
69
+ | No log | 12.0 | 36 | 1.5934 | -0.1743 | 1.5934 | 1.2623 |
70
+ | No log | 12.6667 | 38 | 1.6946 | -0.1275 | 1.6946 | 1.3018 |
71
+ | No log | 13.3333 | 40 | 2.0232 | -0.0932 | 2.0232 | 1.4224 |
72
+ | No log | 14.0 | 42 | 2.2190 | -0.0658 | 2.2190 | 1.4896 |
73
+ | No log | 14.6667 | 44 | 1.9805 | -0.0561 | 1.9805 | 1.4073 |
74
+ | No log | 15.3333 | 46 | 1.5454 | 0.0493 | 1.5454 | 1.2432 |
75
+ | No log | 16.0 | 48 | 1.3890 | -0.0182 | 1.3890 | 1.1786 |
76
+ | No log | 16.6667 | 50 | 1.3579 | -0.0150 | 1.3579 | 1.1653 |
77
+ | No log | 17.3333 | 52 | 1.3370 | -0.0033 | 1.3370 | 1.1563 |
78
+ | No log | 18.0 | 54 | 1.4133 | 0.0359 | 1.4133 | 1.1888 |
79
+ | No log | 18.6667 | 56 | 1.4778 | 0.0468 | 1.4778 | 1.2157 |
80
+ | No log | 19.3333 | 58 | 1.6680 | 0.0178 | 1.6680 | 1.2915 |
81
+ | No log | 20.0 | 60 | 1.6056 | 0.0607 | 1.6056 | 1.2671 |
82
+ | No log | 20.6667 | 62 | 1.4592 | -0.0021 | 1.4592 | 1.2080 |
83
+ | No log | 21.3333 | 64 | 1.4579 | -0.0021 | 1.4579 | 1.2074 |
84
+ | No log | 22.0 | 66 | 1.4523 | 0.0857 | 1.4523 | 1.2051 |
85
+ | No log | 22.6667 | 68 | 1.4463 | 0.1296 | 1.4463 | 1.2026 |
86
+ | No log | 23.3333 | 70 | 1.4531 | 0.0523 | 1.4531 | 1.2054 |
87
+ | No log | 24.0 | 72 | 1.3733 | 0.1296 | 1.3733 | 1.1719 |
88
+ | No log | 24.6667 | 74 | 1.2889 | 0.0970 | 1.2889 | 1.1353 |
89
+ | No log | 25.3333 | 76 | 1.3235 | 0.1296 | 1.3235 | 1.1504 |
90
+ | No log | 26.0 | 78 | 1.4238 | 0.0287 | 1.4238 | 1.1932 |
91
+ | No log | 26.6667 | 80 | 1.3831 | 0.0287 | 1.3831 | 1.1760 |
92
+ | No log | 27.3333 | 82 | 1.2619 | 0.1296 | 1.2619 | 1.1233 |
93
+ | No log | 28.0 | 84 | 1.2083 | 0.1043 | 1.2083 | 1.0992 |
94
+ | No log | 28.6667 | 86 | 1.2485 | 0.1528 | 1.2485 | 1.1173 |
95
+ | No log | 29.3333 | 88 | 1.2450 | 0.0868 | 1.2450 | 1.1158 |
96
+ | No log | 30.0 | 90 | 1.2627 | 0.1067 | 1.2627 | 1.1237 |
97
+ | No log | 30.6667 | 92 | 1.4421 | 0.0880 | 1.4421 | 1.2009 |
98
+ | No log | 31.3333 | 94 | 1.5942 | -0.0238 | 1.5942 | 1.2626 |
99
+ | No log | 32.0 | 96 | 1.5569 | 0.0287 | 1.5569 | 1.2478 |
100
+ | No log | 32.6667 | 98 | 1.4206 | 0.0880 | 1.4206 | 1.1919 |
101
+ | No log | 33.3333 | 100 | 1.3515 | -0.0445 | 1.3515 | 1.1625 |
102
+ | No log | 34.0 | 102 | 1.3542 | -0.0022 | 1.3542 | 1.1637 |
103
+ | No log | 34.6667 | 104 | 1.4106 | 0.0462 | 1.4106 | 1.1877 |
104
+ | No log | 35.3333 | 106 | 1.4767 | 0.0405 | 1.4767 | 1.2152 |
105
+ | No log | 36.0 | 108 | 1.4581 | 0.0462 | 1.4581 | 1.2075 |
106
+ | No log | 36.6667 | 110 | 1.4338 | -0.0108 | 1.4338 | 1.1974 |
107
+ | No log | 37.3333 | 112 | 1.3998 | -0.0479 | 1.3998 | 1.1831 |
108
+ | No log | 38.0 | 114 | 1.4070 | 0.0149 | 1.4070 | 1.1862 |
109
+ | No log | 38.6667 | 116 | 1.4325 | -0.0479 | 1.4325 | 1.1969 |
110
+ | No log | 39.3333 | 118 | 1.5206 | -0.0108 | 1.5206 | 1.2331 |
111
+ | No log | 40.0 | 120 | 1.6335 | -0.0428 | 1.6335 | 1.2781 |
112
+ | No log | 40.6667 | 122 | 1.6533 | -0.0394 | 1.6533 | 1.2858 |
113
+ | No log | 41.3333 | 124 | 1.6239 | -0.0428 | 1.6239 | 1.2743 |
114
+ | No log | 42.0 | 126 | 1.5340 | 0.0462 | 1.5340 | 1.2385 |
115
+ | No log | 42.6667 | 128 | 1.4595 | 0.0098 | 1.4595 | 1.2081 |
116
+ | No log | 43.3333 | 130 | 1.4447 | -0.0800 | 1.4447 | 1.2019 |
117
+ | No log | 44.0 | 132 | 1.4197 | -0.0450 | 1.4197 | 1.1915 |
118
+ | No log | 44.6667 | 134 | 1.3821 | -0.0187 | 1.3821 | 1.1756 |
119
+ | No log | 45.3333 | 136 | 1.3461 | -0.0667 | 1.3461 | 1.1602 |
120
+ | No log | 46.0 | 138 | 1.3496 | -0.0293 | 1.3496 | 1.1617 |
121
+ | No log | 46.6667 | 140 | 1.3635 | 0.0011 | 1.3635 | 1.1677 |
122
+ | No log | 47.3333 | 142 | 1.4126 | 0.0043 | 1.4126 | 1.1885 |
123
+ | No log | 48.0 | 144 | 1.4514 | 0.0462 | 1.4514 | 1.2047 |
124
+ | No log | 48.6667 | 146 | 1.4490 | 0.0462 | 1.4490 | 1.2037 |
125
+ | No log | 49.3333 | 148 | 1.4524 | 0.0462 | 1.4524 | 1.2052 |
126
+ | No log | 50.0 | 150 | 1.4802 | -0.0193 | 1.4802 | 1.2167 |
127
+ | No log | 50.6667 | 152 | 1.4834 | 0.0225 | 1.4834 | 1.2179 |
128
+ | No log | 51.3333 | 154 | 1.4823 | -0.0193 | 1.4823 | 1.2175 |
129
+ | No log | 52.0 | 156 | 1.4611 | 0.0462 | 1.4611 | 1.2088 |
130
+ | No log | 52.6667 | 158 | 1.4356 | 0.0098 | 1.4356 | 1.1982 |
131
+ | No log | 53.3333 | 160 | 1.3917 | -0.0479 | 1.3917 | 1.1797 |
132
+ | No log | 54.0 | 162 | 1.3662 | -0.0479 | 1.3662 | 1.1688 |
133
+ | No log | 54.6667 | 164 | 1.3670 | 0.0098 | 1.3670 | 1.1692 |
134
+ | No log | 55.3333 | 166 | 1.3887 | 0.0850 | 1.3887 | 1.1784 |
135
+ | No log | 56.0 | 168 | 1.3863 | 0.0462 | 1.3863 | 1.1774 |
136
+ | No log | 56.6667 | 170 | 1.3661 | 0.0970 | 1.3661 | 1.1688 |
137
+ | No log | 57.3333 | 172 | 1.3684 | 0.0970 | 1.3684 | 1.1698 |
138
+ | No log | 58.0 | 174 | 1.3705 | 0.0520 | 1.3705 | 1.1707 |
139
+ | No log | 58.6667 | 176 | 1.3686 | 0.0098 | 1.3686 | 1.1699 |
140
+ | No log | 59.3333 | 178 | 1.3720 | -0.0054 | 1.3720 | 1.1713 |
141
+ | No log | 60.0 | 180 | 1.4031 | 0.0098 | 1.4031 | 1.1845 |
142
+ | No log | 60.6667 | 182 | 1.4198 | 0.0098 | 1.4198 | 1.1916 |
143
+ | No log | 61.3333 | 184 | 1.4321 | -0.0054 | 1.4321 | 1.1967 |
144
+ | No log | 62.0 | 186 | 1.4606 | 0.0162 | 1.4606 | 1.2086 |
145
+ | No log | 62.6667 | 188 | 1.4817 | 0.0581 | 1.4817 | 1.2173 |
146
+ | No log | 63.3333 | 190 | 1.5115 | -0.0193 | 1.5115 | 1.2295 |
147
+ | No log | 64.0 | 192 | 1.5265 | -0.0193 | 1.5265 | 1.2355 |
148
+ | No log | 64.6667 | 194 | 1.5622 | -0.0011 | 1.5622 | 1.2499 |
149
+ | No log | 65.3333 | 196 | 1.5992 | 0.0053 | 1.5992 | 1.2646 |
150
+ | No log | 66.0 | 198 | 1.5878 | 0.0053 | 1.5878 | 1.2601 |
151
+ | No log | 66.6667 | 200 | 1.5421 | -0.0160 | 1.5421 | 1.2418 |
152
+ | No log | 67.3333 | 202 | 1.4822 | 0.0462 | 1.4822 | 1.2175 |
153
+ | No log | 68.0 | 204 | 1.4336 | -0.0054 | 1.4336 | 1.1973 |
154
+ | No log | 68.6667 | 206 | 1.4032 | -0.0479 | 1.4032 | 1.1846 |
155
+ | No log | 69.3333 | 208 | 1.3799 | 0.0517 | 1.3799 | 1.1747 |
156
+ | No log | 70.0 | 210 | 1.3670 | 0.0363 | 1.3670 | 1.1692 |
157
+ | No log | 70.6667 | 212 | 1.3682 | 0.0517 | 1.3682 | 1.1697 |
158
+ | No log | 71.3333 | 214 | 1.3782 | -0.0087 | 1.3782 | 1.1740 |
159
+ | No log | 72.0 | 216 | 1.3968 | 0.0520 | 1.3968 | 1.1819 |
160
+ | No log | 72.6667 | 218 | 1.4174 | 0.0462 | 1.4174 | 1.1905 |
161
+ | No log | 73.3333 | 220 | 1.4183 | 0.0493 | 1.4183 | 1.1909 |
162
+ | No log | 74.0 | 222 | 1.4220 | -0.0011 | 1.4220 | 1.1925 |
163
+ | No log | 74.6667 | 224 | 1.4280 | 0.0436 | 1.4280 | 1.1950 |
164
+ | No log | 75.3333 | 226 | 1.4448 | 0.0053 | 1.4448 | 1.2020 |
165
+ | No log | 76.0 | 228 | 1.4575 | 0.0053 | 1.4575 | 1.2073 |
166
+ | No log | 76.6667 | 230 | 1.4719 | 0.0053 | 1.4719 | 1.2132 |
167
+ | No log | 77.3333 | 232 | 1.4785 | 0.0053 | 1.4785 | 1.2159 |
168
+ | No log | 78.0 | 234 | 1.4713 | 0.0053 | 1.4713 | 1.2130 |
169
+ | No log | 78.6667 | 236 | 1.4547 | -0.0011 | 1.4547 | 1.2061 |
170
+ | No log | 79.3333 | 238 | 1.4431 | -0.0160 | 1.4431 | 1.2013 |
171
+ | No log | 80.0 | 240 | 1.4248 | 0.0493 | 1.4248 | 1.1936 |
172
+ | No log | 80.6667 | 242 | 1.4047 | 0.0462 | 1.4047 | 1.1852 |
173
+ | No log | 81.3333 | 244 | 1.3876 | 0.0701 | 1.3876 | 1.1780 |
174
+ | No log | 82.0 | 246 | 1.3723 | 0.0941 | 1.3723 | 1.1715 |
175
+ | No log | 82.6667 | 248 | 1.3634 | 0.0520 | 1.3634 | 1.1676 |
176
+ | No log | 83.3333 | 250 | 1.3607 | -0.0054 | 1.3607 | 1.1665 |
177
+ | No log | 84.0 | 252 | 1.3674 | -0.0054 | 1.3674 | 1.1694 |
178
+ | No log | 84.6667 | 254 | 1.3781 | 0.0369 | 1.3781 | 1.1739 |
179
+ | No log | 85.3333 | 256 | 1.3932 | 0.0520 | 1.3932 | 1.1803 |
180
+ | No log | 86.0 | 258 | 1.4069 | 0.0941 | 1.4069 | 1.1861 |
181
+ | No log | 86.6667 | 260 | 1.4213 | 0.0581 | 1.4213 | 1.1922 |
182
+ | No log | 87.3333 | 262 | 1.4330 | 0.0581 | 1.4330 | 1.1971 |
183
+ | No log | 88.0 | 264 | 1.4456 | 0.0880 | 1.4456 | 1.2023 |
184
+ | No log | 88.6667 | 266 | 1.4623 | -0.0160 | 1.4623 | 1.2093 |
185
+ | No log | 89.3333 | 268 | 1.4749 | -0.0160 | 1.4749 | 1.2145 |
186
+ | No log | 90.0 | 270 | 1.4803 | -0.0160 | 1.4803 | 1.2167 |
187
+ | No log | 90.6667 | 272 | 1.4815 | -0.0160 | 1.4815 | 1.2172 |
188
+ | No log | 91.3333 | 274 | 1.4782 | -0.0160 | 1.4782 | 1.2158 |
189
+ | No log | 92.0 | 276 | 1.4719 | -0.0160 | 1.4719 | 1.2132 |
190
+ | No log | 92.6667 | 278 | 1.4694 | -0.0160 | 1.4694 | 1.2122 |
191
+ | No log | 93.3333 | 280 | 1.4659 | 0.0493 | 1.4659 | 1.2108 |
192
+ | No log | 94.0 | 282 | 1.4609 | 0.0075 | 1.4609 | 1.2087 |
193
+ | No log | 94.6667 | 284 | 1.4602 | 0.0075 | 1.4602 | 1.2084 |
194
+ | No log | 95.3333 | 286 | 1.4567 | 0.0075 | 1.4567 | 1.2069 |
195
+ | No log | 96.0 | 288 | 1.4519 | 0.0075 | 1.4519 | 1.2049 |
196
+ | No log | 96.6667 | 290 | 1.4488 | 0.0075 | 1.4488 | 1.2036 |
197
+ | No log | 97.3333 | 292 | 1.4463 | 0.0075 | 1.4463 | 1.2026 |
198
+ | No log | 98.0 | 294 | 1.4437 | 0.0075 | 1.4437 | 1.2015 |
199
+ | No log | 98.6667 | 296 | 1.4414 | 0.0075 | 1.4414 | 1.2006 |
200
+ | No log | 99.3333 | 298 | 1.4399 | 0.0075 | 1.4399 | 1.1999 |
201
+ | No log | 100.0 | 300 | 1.4393 | 0.0075 | 1.4393 | 1.1997 |
202
+
203
+
204
+ ### Framework versions
205
+
206
+ - Transformers 4.44.2
207
+ - Pytorch 2.4.0+cu118
208
+ - Datasets 2.21.0
209
+ - 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 sha256:8a04b6df8d5f1050d99896f8010ae6061d6ead4c601ef9d6fb897726b9860123
3
+ size 540799996
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e6fbef97a33f01272e366d318659bd8fadf4332132d20306dc6bf13fdbb2f5a6
3
+ size 5304