T6
This model is a fine-tuned version of eslamxm/mt5-base-finetuned-arur on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5941
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 64
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2591 | 1.0 | 37 | 0.2616 |
| 0.1639 | 2.0 | 74 | 0.2497 |
| 0.1771 | 3.0 | 111 | 0.2448 |
| 0.1465 | 4.0 | 148 | 0.2486 |
| 0.1294 | 5.0 | 185 | 0.2499 |
| 0.118 | 6.0 | 222 | 0.2520 |
| 0.1014 | 7.0 | 259 | 0.2582 |
| 0.0986 | 8.0 | 296 | 0.2631 |
| 0.1021 | 9.0 | 333 | 0.2775 |
| 0.0783 | 10.0 | 370 | 0.2867 |
| 0.0699 | 11.0 | 407 | 0.2906 |
| 0.062 | 12.0 | 444 | 0.3010 |
| 0.059 | 13.0 | 481 | 0.3144 |
| 0.0592 | 14.0 | 518 | 0.3265 |
| 0.0513 | 15.0 | 555 | 0.3365 |
| 0.0404 | 16.0 | 592 | 0.3550 |
| 0.0417 | 17.0 | 629 | 0.3552 |
| 0.0385 | 18.0 | 666 | 0.3682 |
| 0.0303 | 19.0 | 703 | 0.3728 |
| 0.0355 | 20.0 | 740 | 0.3947 |
| 0.0232 | 21.0 | 777 | 0.4208 |
| 0.024 | 22.0 | 814 | 0.4080 |
| 0.023 | 23.0 | 851 | 0.4265 |
| 0.0169 | 24.0 | 888 | 0.4233 |
| 0.0185 | 25.0 | 925 | 0.4450 |
| 0.0214 | 26.0 | 962 | 0.4528 |
| 0.0159 | 27.0 | 999 | 0.4486 |
| 0.0156 | 28.0 | 1036 | 0.4926 |
| 0.017 | 29.0 | 1073 | 0.4927 |
| 0.0137 | 30.0 | 1110 | 0.4886 |
| 0.0139 | 31.0 | 1147 | 0.5205 |
| 0.0108 | 32.0 | 1184 | 0.4953 |
| 0.0136 | 33.0 | 1221 | 0.4925 |
| 0.0129 | 34.0 | 1258 | 0.5081 |
| 0.0099 | 35.0 | 1295 | 0.5252 |
| 0.0116 | 36.0 | 1332 | 0.5241 |
| 0.0134 | 37.0 | 1369 | 0.5352 |
| 0.0111 | 38.0 | 1406 | 0.5469 |
| 0.0089 | 39.0 | 1443 | 0.5618 |
| 0.0103 | 40.0 | 1480 | 0.5781 |
| 0.0083 | 41.0 | 1517 | 0.5896 |
| 0.0091 | 42.0 | 1554 | 0.5287 |
| 0.0115 | 43.0 | 1591 | 0.5556 |
| 0.0069 | 44.0 | 1628 | 0.5497 |
| 0.0069 | 45.0 | 1665 | 0.5896 |
| 0.0089 | 46.0 | 1702 | 0.5799 |
| 0.0056 | 47.0 | 1739 | 0.5654 |
| 0.0072 | 48.0 | 1776 | 0.5683 |
| 0.0097 | 49.0 | 1813 | 0.5642 |
| 0.0065 | 50.0 | 1850 | 0.5623 |
| 0.0073 | 51.0 | 1887 | 0.5906 |
| 0.0078 | 52.0 | 1924 | 0.5932 |
| 0.0068 | 53.0 | 1961 | 0.5923 |
| 0.006 | 54.0 | 1998 | 0.5978 |
| 0.005 | 55.0 | 2035 | 0.5846 |
| 0.0082 | 56.0 | 2072 | 0.5886 |
| 0.0081 | 57.0 | 2109 | 0.5844 |
| 0.0056 | 58.0 | 2146 | 0.5878 |
| 0.0069 | 59.0 | 2183 | 0.5890 |
| 0.0075 | 60.0 | 2220 | 0.5946 |
| 0.0077 | 61.0 | 2257 | 0.5897 |
| 0.0064 | 62.0 | 2294 | 0.5908 |
| 0.0049 | 63.0 | 2331 | 0.5934 |
| 0.005 | 64.0 | 2368 | 0.5941 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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