indic-bert-finetuned-ours-DS
This model is a fine-tuned version of ai4bharat/indic-bert on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1832
- Accuracy: 0.655
- Precision: 0.6023
- Recall: 0.6027
- F1: 0.6025
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
Training results
Training Loss |
Epoch |
Step |
Validation Loss |
Accuracy |
Precision |
Recall |
F1 |
1.0681 |
0.99 |
99 |
1.0180 |
0.365 |
0.3435 |
0.4038 |
0.2773 |
0.9384 |
1.98 |
198 |
0.8475 |
0.62 |
0.6235 |
0.5610 |
0.4821 |
0.8201 |
2.97 |
297 |
0.8187 |
0.68 |
0.6839 |
0.6086 |
0.5812 |
0.7178 |
3.96 |
396 |
0.7717 |
0.7 |
0.7117 |
0.6670 |
0.6470 |
0.62 |
4.95 |
495 |
0.7839 |
0.66 |
0.6165 |
0.6244 |
0.6174 |
0.5135 |
5.94 |
594 |
0.8392 |
0.675 |
0.6270 |
0.6234 |
0.6246 |
0.4073 |
6.93 |
693 |
0.8930 |
0.665 |
0.6251 |
0.6254 |
0.6240 |
0.3365 |
7.92 |
792 |
0.9362 |
0.675 |
0.6298 |
0.6276 |
0.6242 |
0.2719 |
8.91 |
891 |
1.0108 |
0.685 |
0.6388 |
0.6293 |
0.6326 |
0.2007 |
9.9 |
990 |
1.1214 |
0.675 |
0.6300 |
0.6299 |
0.6290 |
0.1567 |
10.89 |
1089 |
1.1367 |
0.67 |
0.6193 |
0.6212 |
0.6178 |
0.1074 |
11.88 |
1188 |
1.3157 |
0.65 |
0.6292 |
0.6317 |
0.6227 |
0.0821 |
12.87 |
1287 |
1.5412 |
0.665 |
0.6415 |
0.6330 |
0.6259 |
0.0588 |
13.86 |
1386 |
1.7215 |
0.64 |
0.5862 |
0.5869 |
0.5865 |
0.0337 |
14.85 |
1485 |
1.7556 |
0.64 |
0.6078 |
0.6082 |
0.6032 |
0.0244 |
15.84 |
1584 |
1.8713 |
0.66 |
0.6173 |
0.6186 |
0.6158 |
0.0166 |
16.83 |
1683 |
1.9666 |
0.66 |
0.5995 |
0.5973 |
0.5973 |
0.0124 |
17.82 |
1782 |
1.9245 |
0.66 |
0.6165 |
0.6194 |
0.6163 |
0.0079 |
18.81 |
1881 |
2.0814 |
0.65 |
0.6026 |
0.6023 |
0.6012 |
0.0051 |
19.8 |
1980 |
2.1029 |
0.645 |
0.6014 |
0.5986 |
0.5975 |
0.0031 |
20.79 |
2079 |
2.1155 |
0.655 |
0.6029 |
0.6027 |
0.6023 |
0.0029 |
21.78 |
2178 |
2.1221 |
0.655 |
0.6 |
0.6000 |
0.5999 |
0.0021 |
22.77 |
2277 |
2.2065 |
0.65 |
0.5917 |
0.5898 |
0.5905 |
0.0017 |
23.76 |
2376 |
2.1903 |
0.65 |
0.5910 |
0.5898 |
0.5902 |
0.0016 |
24.75 |
2475 |
2.1832 |
0.655 |
0.6023 |
0.6027 |
0.6025 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1