predict-perception-bertino-cause-concept
This model is a fine-tuned version of indigo-ai/BERTino on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2035
- R2: -0.3662
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: 20
- eval_batch_size: 8
- seed: 1996
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 47
Training results
| Training Loss | Epoch | Step | Validation Loss | R2 |
|---|---|---|---|---|
| 0.3498 | 1.0 | 14 | 0.1845 | -0.2382 |
| 0.2442 | 2.0 | 28 | 0.1575 | -0.0573 |
| 0.1553 | 3.0 | 42 | 0.2216 | -0.4872 |
| 0.0726 | 4.0 | 56 | 0.1972 | -0.3234 |
| 0.0564 | 5.0 | 70 | 0.2832 | -0.9009 |
| 0.0525 | 6.0 | 84 | 0.1854 | -0.2444 |
| 0.0385 | 7.0 | 98 | 0.2816 | -0.8900 |
| 0.0257 | 8.0 | 112 | 0.1815 | -0.2183 |
| 0.03 | 9.0 | 126 | 0.3065 | -1.0576 |
| 0.0275 | 10.0 | 140 | 0.1991 | -0.3367 |
| 0.0175 | 11.0 | 154 | 0.2400 | -0.6110 |
| 0.017 | 12.0 | 168 | 0.1915 | -0.2856 |
| 0.0158 | 13.0 | 182 | 0.2008 | -0.3477 |
| 0.0127 | 14.0 | 196 | 0.1932 | -0.2968 |
| 0.009 | 15.0 | 210 | 0.2500 | -0.6783 |
| 0.0078 | 16.0 | 224 | 0.1969 | -0.3215 |
| 0.0075 | 17.0 | 238 | 0.1857 | -0.2463 |
| 0.0079 | 18.0 | 252 | 0.2405 | -0.6145 |
| 0.0089 | 19.0 | 266 | 0.1865 | -0.2517 |
| 0.0082 | 20.0 | 280 | 0.2275 | -0.5267 |
| 0.0078 | 21.0 | 294 | 0.1890 | -0.2687 |
| 0.0072 | 22.0 | 308 | 0.2230 | -0.4965 |
| 0.0064 | 23.0 | 322 | 0.2286 | -0.5346 |
| 0.0052 | 24.0 | 336 | 0.2154 | -0.4457 |
| 0.0049 | 25.0 | 350 | 0.1901 | -0.2757 |
| 0.0062 | 26.0 | 364 | 0.1917 | -0.2870 |
| 0.0043 | 27.0 | 378 | 0.2042 | -0.3704 |
| 0.0038 | 28.0 | 392 | 0.2251 | -0.5110 |
| 0.0049 | 29.0 | 406 | 0.2092 | -0.4040 |
| 0.0044 | 30.0 | 420 | 0.2119 | -0.4221 |
| 0.0041 | 31.0 | 434 | 0.2018 | -0.3542 |
| 0.0039 | 32.0 | 448 | 0.1875 | -0.2586 |
| 0.0038 | 33.0 | 462 | 0.1980 | -0.3291 |
| 0.0038 | 34.0 | 476 | 0.2071 | -0.3903 |
| 0.0043 | 35.0 | 490 | 0.1998 | -0.3412 |
| 0.0043 | 36.0 | 504 | 0.2052 | -0.3771 |
| 0.004 | 37.0 | 518 | 0.2143 | -0.4382 |
| 0.004 | 38.0 | 532 | 0.1977 | -0.3273 |
| 0.0039 | 39.0 | 546 | 0.2002 | -0.3439 |
| 0.0034 | 40.0 | 560 | 0.2035 | -0.3659 |
| 0.0036 | 41.0 | 574 | 0.1994 | -0.3387 |
| 0.0029 | 42.0 | 588 | 0.2036 | -0.3667 |
| 0.0032 | 43.0 | 602 | 0.2055 | -0.3797 |
| 0.0029 | 44.0 | 616 | 0.2025 | -0.3593 |
| 0.0027 | 45.0 | 630 | 0.2047 | -0.3743 |
| 0.0033 | 46.0 | 644 | 0.2067 | -0.3877 |
| 0.0027 | 47.0 | 658 | 0.2035 | -0.3662 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
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