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google-t5-v1_1-small-intra_model
This model is a fine-tuned version of google/t5-v1_1-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6973
- Losses: [0.4, 0.8, 0.8, 1, 0.0, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 1.0, 1, 1, 1.0, 1, 1.0, 0.6000000000000001, 0.4, 0.2, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 0.8, 0.6000000000000001, 1, 0.8, 0.8, 1, 0.8, 0.4, 0.8, 0.8, 0.4, 1, 1, 0.4, 0.8, 0.2, 1, 1, 0.4, 1, 1, 0.8, 1, 1, 1, 1, 0.6000000000000001, 1, 0.8, 0.0, 0.8, 0.0, 0.8, 1, 1, 0.4, 0.4, 0.2, 0.4, 0.8, 0.8, 0.4, 1, 0.2, 0.4, 0.8, 1, 1, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 0.8, 1, 0.8, 1, 0.0, 1, 0.0, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.4, 1, 0.8, 0.8, 0.8, 0.8, 0.0, 1, 0.8, 0.6000000000000001, 0.0, 1, 0.8, 1, 1, 1, 1, 0.0, 0.8, 1, 1, 0.8, 1, 1, 1, 0.4, 0.4, 1, 1, 0.8, 0.8, 0.6000000000000001, 0.0, 0.6000000000000001, 0.2, 1.0, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.6000000000000001, 1, 0.8, 0.8, 1, 1, 0.8, 0.6000000000000001, 0.4, 0.8, 0.0, 0.2, 0.8, 0.8, 0.6000000000000001, 0.8, 1, 0.8, 0.4, 1, 1, 1.0, 0.8, 0.8, 1, 1, 1, 0.8, 1.0, 0.4, 0.8, 0.4, 1, 0.4, 0.0, 0.8, 0.8, 0.0, 1, 0.8, 1, 0.6000000000000001, 1, 1.0, 0.8, 1.0, 0.4, 0.4, 0.8, 0.8, 0.6000000000000001, 1, 0.4, 1, 1, 0.2, 0.0, 0.6000000000000001, 0.4, 0.2, 0.2, 0.8, 0.8, 0.8, 1, 0.8, 1, 1, 0.8, 0.8, 0.6000000000000001, 0.4, 1, 0.4, 0.0, 1, 0.8, 0.2, 0.6000000000000001, 0.6000000000000001, 0.2, 0.4, 0.8, 0.6000000000000001, 1.0, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.4, 0.4, 1, 0.8, 0.2, 0.2, 1, 0.8, 0.8, 0.8, 1, 1, 0.0, 0.4, 0.6000000000000001, 1, 1, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.4, 1, 0.4, 1, 1, 0.8, 1, 1, 0.8, 0.8, 0.0, 0.4, 1, 1, 1.0, 1, 0.8, 0.4, 1, 0.6000000000000001, 1, 0.0, 1, 1, 0.8, 0.8, 0.6000000000000001, 1, 1, 0.2, 0.8, 0.6000000000000001, 0.8, 1, 0.6000000000000001, 0.8, 0.4, 1, 0.2, 0.8, 0.6000000000000001, 0.8, 1, 0.6000000000000001, 0.4, 1, 0.4, 0.0, 1, 1, 0.8, 1, 1, 0.8, 1, 0.2, 0.4, 0.8, 0.6000000000000001, 0.8, 0.4, 0.4, 0.8, 1, 0.0, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.0, 0.8, 1, 0.8, 0.8, 0.8, 0.4, 0.4, 0.8, 1, 1, 1, 0.6000000000000001, 0.0, 0.8, 0.8, 0.8, 0.4, 1, 1, 0.4, 0.4, 0.8, 0.8, 1, 0.4, 0.2, 0.6000000000000001, 1, 1, 1, 0.8, 1, 1, 0.8, 0.4, 0.4, 0.8, 1, 0.8, 1, 0.4, 0.6000000000000001, 0.4, 1]
- Train Loss: 0.7164
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Losses | Train Loss |
|---|---|---|---|---|---|
| 13.7913 | 1.0 | 99 | 11.9227 | [1.0, 1.0, 1, 1, 1.0, 1.0, 1, 0.8888888888888888, 0.875, 0.8461538461538461, 0.875, 0.8888888888888888, 1.0, 0.8, 1.0, 0.8888888888888888, 1.0, 1.0, 1, 1.0, 1, 1.0, 1.0, 1.0, 1.0, 0.85, 1.0, 1.0, 0.8235294117647058, 0.5555555555555556, 0.8888888888888888, 1, 1.0, 10.0, 1, 0.8888888888888888, 1.0, 1.0, 1.0, 0.8888888888888888, 1.0, 1, 0.8571428571428571, 0.6666666666666666, 0.8888888888888888, 0.8888888888888888, 0.8888888888888888, 1.0, 0.8, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.8888888888888888, 1, 1.0, 0.8888888888888888, 1.0, 1, 1, 0.5555555555555556, 1.0, 1.0, 1.0, 1.0, 1, 0.8235294117647058, 1.0, 0.3333333333333333, 1.0, 0.8888888888888888, 0.8571428571428571, 1, 1, 1, 1.0, 0.8, 1.0, 1.0, 1, 1.0, 1, 0.9090909090909091, 0.875, 1.0, 1, 1.0, 0.8461538461538461, 1.0, 1.0, 1, 0.8571428571428571, 1.0, 1, 0.8888888888888888, 0.8888888888888888, 1.0, 1.0, 1.0, 0.7777777777777778, 1, 0.8666666666666667, 1.0, 1, 0.8888888888888888, 1, 0.8888888888888888, 1.0, 1.0, 1, 1.0, 1.0, 1.0, 1.0, 1.0, 0.8888888888888888, 0.8, 0.8888888888888888, 1.0, 1.0, 1.0, 1.0, 0.875, 1.0, 1.0, 1.0, 0.8888888888888888, 0.8888888888888888, 1, 0.8, 0.8, 1.0, 1, 1, 1.0, 1.0, 1.0, 1.0, 0.8888888888888888, 1, 1.0, 1.0, 1.0, 0.8888888888888888, 0.8461538461538461, 1.0, 1.0, 0.9090909090909091, 1.0, 0.8181818181818182, 0.8, 0.8888888888888888, 0.8, 1, 1.0, 1, 0.9090909090909091, 1.0, 1.0, 1.0, 0.75, 1, 1.0, 1.0, 0.8888888888888888, 0.8235294117647058, 1.0, 1.0, 1.0, 0.8235294117647058, 1.0, 1.0, 1.0, 0.8888888888888888, 0.8235294117647058, 1.0, 1.0, 10.0, 1.0, 0.8888888888888888, 1.0, 1.0, 1, 1, 1.0, 1.0, 1, 1, 1.0, 0.8888888888888888, 1.0, 1.0, 0.8888888888888888, 0.8888888888888888, 1.0, 1.0, 0.8, 0.8888888888888888, 1.0, 1.0, 0.8888888888888888, 1.0, 0.875, 0.8888888888888888, 1.0, 0.5555555555555556, 0.8888888888888888, 1.0, 1, 0.875, 0.8888888888888888, 1.0, 10.0, 1, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1, 1.0, 1.0, 1.0, 1.0, 1.0, 1, 0.6666666666666666, 1.0, 1.0, 1.0, 0.8571428571428571, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.8888888888888888, 0.8888888888888888, 1.0, 0.7777777777777778, 1, 1.0, 1.0, 0.8461538461538461, 1.0, 0.8, 0.8888888888888888, 1.0, 1, 1.0, 1, 0.8, 1.0, 0.8, 0.8888888888888888, 1, 1.0, 1.0, 1.0, 1.0, 0.8181818181818182, 0.875, 0.7777777777777778, 0.8888888888888888, 10.0, 0.8888888888888888, 0.875, 1.0, 0.8888888888888888, 0.8888888888888888, 0.8, 1.0, 1.0, 1.0, 0.8888888888888888, 1.0, 1, 0.8125, 1.0, 0.9090909090909091, 1.0, 1.0, 0.8888888888888888, 1.0, 1.0, 0.8888888888888888, 0.8888888888888888, 0.75, 1, 1, 0.9090909090909091, 1.0, 0.75, 1, 0.875, 1.0, 1.0, 0.9, 1, 1.0, 0.4444444444444444, 1.0, 1, 1.0, 1, 1, 0.8888888888888888, 1.0, 1, 1.0, 0.8888888888888888, 1.0, 1.0, 1.0, 1.0, 0.8888888888888888, 1.0, 0.875, 1.0, 1.0, 1.0, 1.0, 1.0, 0.8, 1.0, 0.8888888888888888, 1.0, 1.0, 0.8888888888888888, 0.875, 0.4444444444444444, 1.0, 1.0, 1.0, 0.8888888888888888, 10.0, 0.7777777777777778, 1.0, 1.0, 1.0, 0.8461538461538461, 1, 0.8888888888888888, 0.8888888888888888, 0.8125, 0.6666666666666666, 1.0, 1.0, 0.8888888888888888, 1, 0.8461538461538461, 1.0] | 1.0697 |
| 6.0033 | 2.0 | 198 | 4.6189 | [1, 0.8, 1, 1, 1, 1.0, 1, 0.8, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 0.8, 0.8, 1, 1, 1, 1, 1.0, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 0.8, 1, 1, 1, 1, 0.8, 0.8, 1, 1, 1, 1, 1, 1, 1, 0.8, 1.0, 1, 1, 1, 1.0, 1, 1, 0.8, 1, 1, 0.8, 1, 0.8, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1.0, 1, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 0.8, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 0.8, 1, 1.0, 1, 0.8, 0.8, 1, 0.8, 1, 1, 1, 0.8, 1, 1, 1, 0.8, 1, 1, 0.8, 1, 0.8, 1, 1, 1, 1, 1, 0.8, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 1, 0.8, 1, 1, 0.8, 0.8, 1, 1, 1, 1, 1, 0.8, 1.0, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 0.8, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 1, 0.8, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 1] | 0.9694 |
| 2.5509 | 3.0 | 297 | 1.0645 | [1.0, 0.6000000000000001, 1.0, 1, 0.6000000000000001, 0.0, 0.6000000000000001, 0.6000000000000001, 0.4, 1.0, 0.8, 1, 1, 0.6000000000000001, 0.4, 0.4, 1.0, 1, 1, 1.0, 1, 1.0, 1.0, 0.6000000000000001, 0.4, 0.6000000000000001, 0.6000000000000001, 0.8, 0.6000000000000001, 1.0, 1.0, 0.6000000000000001, 0.6000000000000001, 1.0, 0.8, 0.8, 1.0, 1.0, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 1.0, 0.8, 1, 1.0, 0.8, 1, 1.0, 0.6000000000000001, 1.0, 1.0, 1.0, 1.0, 0.6000000000000001, 1.0, 1.0, 1, 1.0, 1.0, 1.0, 1, 1.0, 0.6000000000000001, 0.6000000000000001, 1.0, 0.8, 1, 1.0, 0.6000000000000001, 1, 0.6000000000000001, 0.4, 1.0, 1.0, 1.0, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.8, 1.0, 1.0, 1.0, 1.0, 1.0, 1, 0.6000000000000001, 1, 1.0, 0.6000000000000001, 1.0, 0.6000000000000001, 0.6000000000000001, 1.0, 0.6000000000000001, 1.0, 1, 0.6000000000000001, 0.4, 1.0, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.6000000000000001, 0.6000000000000001, 1.0, 1, 0.4, 0.8, 0.6000000000000001, 0.6000000000000001, 1, 0.6000000000000001, 1.0, 0.6000000000000001, 1, 1, 1.0, 0.6000000000000001, 0.0, 1.0, 1, 1, 0.4, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.8, 0.6000000000000001, 0.6000000000000001, 0.4, 0.6000000000000001, 1, 0.6000000000000001, 0.6000000000000001, 1, 0.6000000000000001, 0.8, 1.0, 0.6000000000000001, 1.0, 1, 0.6000000000000001, 0.6000000000000001, 0.8, 0.6000000000000001, 1.0, 1.0, 1, 1, 1, 0.8, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 1.0, 1, 1.0, 0.0, 1.0, 0.6000000000000001, 0.6000000000000001, 1, 0.8, 1.0, 0.6000000000000001, 0.6000000000000001, 1.0, 0.8, 1, 1, 1, 0.6000000000000001, 0.4, 0.6000000000000001, 0.4, 1.0, 1.0, 0.6000000000000001, 0.8, 0.4, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 1.0, 1, 0.6000000000000001, 0.4, 1.0, 0.6000000000000001, 1, 1, 1.0, 1.0, 1, 1.0, 1.0, 1.0, 1, 0.6000000000000001, 1, 1.0, 1.0, 1.0, 1.0, 1.0, 0.6000000000000001, 1, 1, 1.0, 1.0, 1.0, 0.6000000000000001, 1, 0.0, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 1.0, 0.6000000000000001, 1, 1.0, 1, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.6000000000000001, 1, 0.8, 0.6000000000000001, 1.0, 0.6000000000000001, 0.6000000000000001, 1.0, 0.4, 1, 1, 0.6000000000000001, 0.6000000000000001, 0.8, 1, 1.0, 1, 0.6000000000000001, 1.0, 0.8, 0.8, 0.6000000000000001, 1, 1, 0.6000000000000001, 0.6000000000000001, 1, 0.6000000000000001, 1.0, 1.0, 0.8, 0.6000000000000001, 0.0, 0.6000000000000001, 0.6000000000000001, 1.0, 0.8, 0.4, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 1.0, 0.6000000000000001, 0.6000000000000001, 0.4, 1, 1, 1, 1.0, 0.4, 1, 0.6000000000000001, 0.4, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.6000000000000001, 0.4, 1, 1, 0.4, 1.0, 1.0, 0.6000000000000001, 1.0, 1.0, 0.8, 1.0, 1.0, 0.6000000000000001, 0.6000000000000001, 0.8, 1, 0.6000000000000001, 0.6000000000000001, 0.4, 1, 1.0, 0.6000000000000001, 1.0, 1, 1.0, 0.4, 1, 1, 0.4, 0.6000000000000001, 1.0, 1, 1.0, 0.6000000000000001, 1.0, 1.0, 1.0, 0.6000000000000001, 1, 1, 1, 1, 1.0, 0.6000000000000001, 1.0, 0.6000000000000001, 0.6000000000000001, 0.8, 1, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 1.0, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 0.4, 1, 1, 0.6000000000000001, 0.6000000000000001, 0.8, 1, 0.6000000000000001, 1.0, 0.8, 1.0, 1.0, 1, 0.6000000000000001, 1.0, 0.4, 0.6000000000000001, 1] | 0.7944 |
| 1.323 | 4.0 | 396 | 0.7302 | [0.4, 0.8, 0.4, 1, 0.4, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 1, 1, 1, 1, 0.8, 1.0, 0.6000000000000001, 0.4, 0.8, 0.8, 1, 0.8, 0.4, 0.4, 0.8, 0.6000000000000001, 0.8, 0.8, 1, 0.8, 0.4, 0.4, 0.8, 0.8, 0.4, 1, 1, 0.4, 0.4, 0.8, 0.8, 0.8, 0.4, 1, 1, 0.4, 1, 1, 1, 0.8, 0.8, 1, 0.4, 0.4, 0.8, 0.4, 1, 1, 1, 0.4, 0.4, 0.8, 0.4, 0.8, 0.8, 0.4, 1, 0.8, 0.4, 0.8, 1, 0.8, 1.0, 1, 1, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 1, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 1, 0.0, 1, 0.4, 0.8, 0.4, 0.4, 0.4, 1, 0.8, 1.0, 0.4, 0.8, 0.4, 0.8, 1, 0.8, 0.8, 0.4, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.4, 0.4, 1, 1, 0.4, 0.8, 0.6000000000000001, 0.4, 1, 0.8, 1.0, 0.4, 0.4, 0.8, 1, 0.8, 0.8, 1.0, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.4, 0.8, 0.4, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 1, 1, 1, 0.8, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 1, 1, 1, 0.6000000000000001, 0.4, 0.8, 0.0, 1, 0.6000000000000001, 0.4, 0.8, 0.4, 0.4, 1, 1, 1, 0.8, 0.8, 1.0, 0.8, 1.0, 0.4, 0.4, 0.4, 1, 1.0, 0.8, 0.0, 0.8, 1, 0.8, 0.4, 0.6000000000000001, 0.4, 0.8, 0.8, 0.8, 1, 1, 0.8, 0.8, 1, 1, 0.8, 0.8, 1.0, 0.4, 1, 0.4, 0.4, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.4, 0.8, 0.8, 0.6000000000000001, 0.4, 0.8, 0.8, 1, 0.8, 0.8, 0.4, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 1, 0.8, 0.4, 0.4, 0.6000000000000001, 1, 1, 0.8, 0.8, 1.0, 1, 1, 0.8, 0.8, 0.4, 1, 0.6000000000000001, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.4, 0.4, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.4, 1, 0.6000000000000001, 0.8, 0.4, 0.8, 1, 0.8, 0.8, 0.6000000000000001, 1, 0.8, 0.8, 0.4, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 0.8, 0.4, 1.0, 0.8, 0.4, 1.0, 0.8, 1, 0.6000000000000001, 0.4, 1, 0.4, 0.4, 1, 1, 1, 1, 0.8, 0.4, 1, 0.8, 0.0, 0.8, 1.0, 0.8, 0.4, 0.4, 0.4, 1, 0.4, 0.6000000000000001, 0.8, 1, 1, 0.4, 0.8, 1, 1, 0.4, 0.8, 0.4, 0.4, 0.8, 1, 1, 1, 0.8, 0.4, 0.8, 0.4, 0.8, 0.4, 1, 1, 0.4, 0.4, 0.8, 0.4, 0.8, 0.4, 0.8, 0.6000000000000001, 1, 1, 0.8, 0.8, 1, 1, 0.4, 0.4, 0.6000000000000001, 0.4, 1, 0.8, 0.8, 0.4, 0.6000000000000001, 0.0, 1] | 0.7365 |
| 1.1398 | 5.0 | 495 | 0.6483 | [0.4, 0.8, 0.4, 1, 0.4, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 1, 1, 1, 1, 0.8, 1.0, 0.6000000000000001, 0.4, 0.8, 0.8, 1, 0.8, 0.4, 0.4, 0.8, 0.6000000000000001, 0.8, 0.8, 1, 0.8, 0.4, 0.4, 0.8, 0.8, 0.4, 1, 1, 0.4, 0.4, 0.8, 0.8, 0.8, 0.4, 1, 1, 0.4, 1, 1, 1, 0.8, 0.8, 1, 0.4, 0.4, 0.8, 0.4, 1, 1, 1, 0.4, 0.4, 0.8, 0.4, 0.8, 0.8, 0.4, 1, 0.8, 0.4, 0.8, 1, 0.8, 1.0, 1, 1, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 1, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 1, 0.0, 1, 0.4, 0.8, 0.4, 0.4, 0.4, 1, 0.8, 1.0, 0.4, 0.8, 0.4, 0.8, 1, 0.8, 0.8, 0.4, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.4, 0.4, 1, 1, 0.4, 0.8, 0.6000000000000001, 0.4, 1, 0.8, 1.0, 0.4, 0.4, 0.8, 1, 0.8, 0.8, 1.0, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.4, 0.8, 0.4, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 1, 1, 1, 0.8, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 1, 1, 1, 0.6000000000000001, 0.4, 0.8, 0.0, 1, 0.6000000000000001, 0.4, 0.8, 0.4, 0.4, 1, 1, 1, 0.8, 0.8, 1.0, 0.8, 1.0, 0.4, 0.4, 0.4, 1, 1.0, 0.8, 0.0, 0.8, 1, 0.8, 0.4, 0.6000000000000001, 0.4, 0.8, 0.8, 0.8, 1, 1, 0.8, 0.8, 1, 1, 0.8, 0.8, 1.0, 0.4, 1, 0.4, 0.4, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.4, 0.8, 0.8, 0.6000000000000001, 0.4, 0.8, 0.8, 1, 0.8, 0.8, 0.4, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 1, 0.8, 0.4, 0.4, 0.6000000000000001, 1, 1, 0.8, 0.8, 1.0, 1, 1, 0.8, 0.8, 0.4, 1, 0.6000000000000001, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.4, 0.4, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.4, 1, 0.6000000000000001, 0.8, 0.4, 0.8, 1, 0.8, 0.8, 0.6000000000000001, 1, 0.8, 0.8, 0.4, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 0.8, 0.4, 1.0, 0.8, 0.4, 1.0, 0.8, 1, 0.6000000000000001, 0.4, 1, 0.4, 0.4, 1, 1, 1, 1, 0.8, 0.4, 1, 0.8, 0.0, 0.8, 1.0, 0.8, 0.4, 0.4, 0.4, 1, 0.4, 0.6000000000000001, 0.8, 1, 1, 0.4, 0.8, 1, 1, 0.4, 0.8, 0.4, 0.4, 0.8, 1, 1, 1, 0.8, 0.4, 0.8, 0.4, 0.8, 0.4, 1, 1, 0.4, 0.4, 0.8, 0.4, 0.8, 0.4, 0.8, 0.6000000000000001, 1, 1, 1, 0.8, 1, 1, 0.4, 0.4, 0.6000000000000001, 0.4, 1, 0.8, 0.8, 0.4, 0.6000000000000001, 0.0, 1] | 0.7370 |
| 0.9565 | 6.0 | 594 | 0.6207 | [0.0, 0.8, 0.0, 1, 0.4, 0.6000000000000001, 0.8, 1, 0.6000000000000001, 1.0, 1, 1, 1, 1, 1.0, 0.6000000000000001, 0.4, 0.2, 0.2, 1, 0.8, 0.0, 0.4, 0.8, 0.6000000000000001, 1, 1, 1, 1, 0.0, 0.0, 0.8, 0.8, 0.4, 1, 1, 0.4, 0.0, 1, 1, 1, 0.4, 1, 1, 0.4, 1, 1, 1, 0.8, 0.8, 1, 0.4, 0.0, 0.8, 0.4, 1, 1, 1, 0.4, 0.4, 0.2, 0.4, 1, 0.8, 0.4, 1, 0.2, 0.0, 1, 1, 1, 1.0, 1, 1, 0.6000000000000001, 0.8, 1, 0.8, 1, 0.0, 1, 0.0, 0.4, 0.8, 0.8, 1, 0.2, 1, 0.0, 1, 0.8, 1, 0.4, 0.8, 0.4, 1, 0.8, 1.0, 0.0, 1, 0.4, 0.8, 1, 1, 1, 0.0, 0.2, 1, 1, 0.8, 1, 0.8, 1, 0.4, 0.4, 1, 1, 0.4, 0.8, 0.6000000000000001, 0.4, 0.6000000000000001, 0.2, 1.0, 0.4, 0.8, 0.8, 1, 0.8, 0.8, 1.0, 1, 0.8, 0.8, 1, 1, 1, 0.4, 0.4, 0.8, 0.0, 0.2, 0.8, 0.8, 0.6000000000000001, 0.8, 1, 1, 0.4, 0.8, 1, 0.6000000000000001, 0.8, 0.8, 1, 1, 1, 1, 1.0, 0.4, 1, 0.4, 1, 0.4, 0.4, 0.8, 0.8, 0.4, 1, 0.4, 1, 0.2, 1, 1.0, 0.8, 1.0, 0.4, 0.4, 0.4, 1, 1.0, 1, 0.4, 1, 1, 0.2, 0.4, 0.6000000000000001, 0.4, 0.8, 0.2, 0.8, 1, 1, 0.2, 0.8, 1, 1, 0.2, 0.8, 0.2, 0.4, 1, 0.4, 0.4, 1, 0.8, 0.2, 0.6000000000000001, 1, 0.8, 0.4, 0.8, 0.8, 1.0, 0.8, 1, 0.8, 1, 1, 0.8, 0.4, 0.4, 0.8, 0.8, 0.2, 0.2, 0.8, 0.8, 1, 0.8, 1, 1, 0.4, 0.4, 0.6000000000000001, 1, 1, 0.8, 0.8, 1.0, 1, 1, 0.8, 0.8, 0.4, 1, 0.4, 1, 1, 0.8, 1, 1, 0.8, 0.8, 0.0, 0.4, 1, 1, 1.0, 1, 0.8, 0.4, 1, 0.6000000000000001, 1, 0.4, 1, 1, 0.8, 0.8, 0.6000000000000001, 1, 0.8, 0.2, 0.0, 0.6000000000000001, 0.2, 0.8, 0.6000000000000001, 0.8, 0.8, 1.0, 0.2, 0.4, 1.0, 0.8, 1, 0.6000000000000001, 0.4, 1, 0.4, 0.4, 1, 1, 1, 1, 1, 0.8, 1, 0.2, 0.0, 0.8, 1.0, 0.8, 0.0, 0.8, 0.4, 1, 0.0, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.4, 0.6000000000000001, 1, 1, 0.8, 0.8, 0.0, 0.4, 0.8, 1, 0.4, 0.4, 0.6000000000000001, 0.0, 0.8, 0.4, 0.8, 0.4, 1, 1, 0.8, 0.4, 1, 0.4, 1, 0.4, 0.8, 0.6000000000000001, 1, 1, 1, 0.8, 1, 1, 0.8, 0.4, 0.6000000000000001, 0.4, 1, 0.8, 0.8, 0.4, 0.6000000000000001, 0.0, 1] | 0.7070 |
| 0.8479 | 7.0 | 693 | 0.5786 | [0.0, 1, 0.0, 0.4, 0.8, 1.0, 0.8, 1, 0.6000000000000001, 1.0, 1, 0.4, 1, 1, 1, 0.6000000000000001, 0.4, 0.2, 0.2, 1, 0.2, 0.0, 0.0, 0.8, 0.6000000000000001, 1, 1, 1, 1, 0.0, 0.0, 0.8, 0.8, 0.4, 1, 1, 1, 0.0, 1, 1, 1, 0.4, 1, 0.4, 0.0, 1, 1, 1, 1, 0.6000000000000001, 1, 0.0, 0.0, 1, 0.0, 1, 1, 1, 0.0, 0.4, 0.2, 0.4, 1, 1, 1, 1, 0.2, 0.0, 1, 1, 1, 1.0, 1, 1, 0.6000000000000001, 0.8, 1, 1, 1, 0.0, 1, 0.0, 0.0, 0.8, 0.2, 1, 0.2, 1.0, 0.4, 1, 0.8, 1, 0.0, 1, 0.0, 1, 0.8, 1.0, 0.0, 1, 1, 0.2, 1, 1, 1, 0.0, 0.2, 1, 1, 0.8, 1, 0.2, 1, 0.4, 0.8, 1, 1, 1, 1, 1.0, 0.4, 0.6000000000000001, 0.2, 1, 0.8, 0.8, 1, 1, 1, 1, 1.0, 1, 0.2, 0.8, 1, 1, 1, 0.4, 0.4, 0.8, 0.0, 0.2, 1, 1, 0.4, 1, 1, 1, 0.4, 0.2, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 0.4, 1, 0.4, 0.0, 1, 0.8, 0.4, 1, 0.4, 1, 0.2, 1, 1.0, 1, 1.0, 1, 0.0, 0.8, 1, 1.0, 1, 0.4, 1, 1, 0.2, 0.8, 0.6000000000000001, 0.4, 1, 0.2, 0.2, 1, 1, 0.2, 1, 1, 1, 0.2, 1, 0.2, 0.4, 1, 0.4, 1, 1.0, 1, 0.2, 0.6000000000000001, 1.0, 0.8, 0.4, 1, 0.6000000000000001, 1.0, 0.8, 1, 1, 1, 1, 1, 0.4, 0.4, 1, 1, 0.2, 1, 1, 1, 1, 1, 1, 1, 0.8, 0.0, 0.6000000000000001, 1, 1, 1, 1, 0.8, 1, 1, 0.4, 0.8, 1, 1, 0.4, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 0.0, 0.4, 1, 1, 1.0, 1, 1, 1, 1, 0.6000000000000001, 1, 0.8, 1, 1, 0.8, 1, 0.6000000000000001, 1, 0.2, 0.2, 0.0, 1, 0.2, 1, 0.6000000000000001, 1, 0.8, 1, 0.2, 0.8, 1, 0.2, 1, 0.6000000000000001, 0.0, 1, 0.8, 0.0, 1, 1, 1, 1, 1, 0.8, 1, 0.2, 0.4, 0.8, 1, 0.2, 0.0, 0.8, 0.0, 1, 0.0, 0.6000000000000001, 0.6000000000000001, 1, 1, 1, 0.6000000000000001, 1, 1.0, 1, 0.8, 0.0, 0.4, 1, 1, 0.4, 0.4, 0.6000000000000001, 0.0, 1, 0.0, 1, 0.8, 1, 1, 0.8, 0.8, 1, 0.0, 1, 0.8, 1, 1, 1, 0.4, 1, 1, 1, 1, 0.8, 0.0, 0.4, 0.0, 1.0, 0.2, 1, 0.4, 0.6000000000000001, 0.4, 1] | 0.7231 |
| 0.7582 | 8.0 | 792 | 0.6239 | [0.4, 1, 0.4, 1, 1, 0.6000000000000001, 0.8, 1, 0.6000000000000001, 1.0, 1.0, 0.4, 1, 0.8, 1, 1, 0.4, 0.8, 0.8, 1, 0.8, 1, 1, 1, 1, 1, 0.8, 1, 1, 0.4, 1, 0.8, 1, 0.4, 1, 1.0, 1, 0.0, 1, 0.8, 0.8, 0.4, 1, 1, 0.4, 1, 1, 1, 1, 0.6000000000000001, 1, 0.4, 0.4, 1, 0.4, 1, 1, 1, 0.4, 0.4, 0.2, 0.4, 0.8, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 0.6000000000000001, 0.8, 1, 0.8, 1, 0.0, 1, 0.0, 0.4, 0.8, 0.8, 0.8, 1, 1, 1, 1, 0.8, 0.8, 1, 1, 1, 1, 1, 1.0, 0.4, 1, 1, 0.2, 1, 0.8, 1, 0.0, 0.8, 1, 1, 1, 1, 0.2, 1, 0.4, 0.4, 1, 1, 1, 1, 1, 0.4, 1, 1, 1.0, 1, 0.8, 1, 1, 1, 0.8, 1.0, 1, 1, 0.8, 1, 1, 1, 1, 0.4, 1, 0.4, 0.2, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 1, 0.6000000000000001, 1, 0.8, 1, 1, 0.6000000000000001, 1, 1, 1, 0.4, 1, 1, 1, 0.2, 1, 1.0, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 0.2, 0.4, 1, 1, 0.8, 1, 0.8, 1, 1, 0.8, 1, 1, 1, 0.8, 1, 1.0, 1, 1, 1, 0.4, 1, 1, 0.2, 0.8, 1.0, 0.8, 0.4, 1, 0.6000000000000001, 1.0, 0.8, 0.8, 1, 1, 0.8, 1, 0.4, 0.4, 0.8, 1, 0.2, 1, 1, 1, 1, 1, 1, 1, 0.4, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 1, 0.8, 0.8, 1, 1, 1, 1, 1, 0.8, 1, 1, 0.8, 1, 0.0, 1, 1, 1, 1.0, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 0.8, 1, 0.6000000000000001, 1, 1, 1, 1, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 1, 0.4, 1, 0.8, 0.4, 1, 0.8, 1, 0.6000000000000001, 0.4, 1, 0.4, 1, 1, 1, 1, 1, 0.8, 0.4, 1, 1, 1, 0.8, 1, 1, 0.0, 0.4, 1, 1, 0.0, 0.6000000000000001, 0.8, 1, 1, 1, 1, 1, 1, 1, 0.8, 0.4, 0.4, 1, 1, 1, 1, 1, 1, 1, 1, 0.8, 0.4, 1, 1, 0.4, 0.8, 1, 0.0, 0.8, 0.4, 0.8, 1, 1, 1, 0.8, 1, 1, 1, 0.8, 0.4, 0.6000000000000001, 0.4, 1.0, 1, 0.8, 1, 0.6000000000000001, 0.0, 1] | 0.8384 |
| 0.7579 | 9.0 | 891 | 0.5293 | [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] | 0.9972 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.6.1
- Tokenizers 0.14.1
