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