SChem5Labels-google-t5-v1_1-base-intra_model

This model is a fine-tuned version of google/t5-v1_1-base on the None dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.9822
• Loss: nan
• Losses: [1, 1, 0.6000000000000001, 1, 0.8, 1, 1, 0.4, 1, 0.6000000000000001, 1, 0.8, 0.6000000000000001, 0.8, 1.0, 0.4, 1, 0.6000000000000001, 0.8, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 0.8, 1.0, 0.6000000000000001, 1, 0.4, 0.4, 1, 0.4, 1, 1, 1, 0.8, 0.4, 1, 1, 1, 0.8, 0.8, 1, 1, 0.4, 0.4, 1, 0.4, 0.8, 0.8, 1, 0.8, 1, 0.0, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 1.0, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.4, 1, 0.4, 0.4, 0.8, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.4, 0.8, 0.8, 0.6000000000000001, 0.4, 0.0, 0.6000000000000001, 0.8, 0.8, 0.4, 0.4, 0.4, 0.6000000000000001, 0.0, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 0.0, 0.4, 0.4, 1, 0.0, 0.6000000000000001, 0.8, 0.6000000000000001, 0.8, 0.4, 0.4, 0.8, 0.8, 0.4, 0.4, 0.8, 0.4, 0.8, 0.8, 1, 0.4, 0.8, 0.8, 0.4, 0.4, 0.8, 0.4, 0.4, 1, 0.4, 0.8, 0.8, 0.8, 0.8, 0.8, 0.4, 0.8, 0.8, 0.8, 0.8, 0.4, 0.4, 0.4, 0.8, 0.4, 0.8, 0.4, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.6000000000000001, 0.4, 1, 0.4, 1, 0.8, 0.8, 0.4, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 1, 0.8, 1, 0.4, 0.4, 1, 0.8, 0.8, 1, 1, 1, 0.8, 1, 0.4, 0.6000000000000001, 0.4, 0.4, 0.4, 1, 0.4, 0.8, 0.8, 0.6000000000000001, 0.8, 0.8, 0.4, 0.8, 1, 0.8, 0.8, 1, 1, 0.4, 0.4, 0.4, 0.6000000000000001, 0.8, 0.8, 0.8, 1, 0.8, 0.4, 0.8, 1.0, 0.8, 1.0, 1, 0.4, 0.8, 0.8, 1, 1, 0.8, 1, 1.0, 1, 0.4, 1, 0.6000000000000001, 0.8, 1, 1.0, 1, 0.6000000000000001, 0.4, 0.4, 0.6000000000000001, 1.0, 0.8, 0.8, 0.4, 1, 1, 1, 0.8, 0.8, 1.0, 0.8, 0.8, 0.6000000000000001, 0.8, 0.4, 0.8, 1, 1, 1.0, 0.8, 1.0, 1.0, 0.8, 1, 0.8, 0.8, 1.0, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 1, 0.4, 0.8, 0.4, 0.8, 0.8, 1, 1, 0.4, 0.4, 1, 0.8, 1, 0.8, 0.6000000000000001, 0.6000000000000001, 1, 0.6000000000000001, 0.4, 1, 0.8, 0.4, 0.4, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 0.8, 0.4, 0.4, 1, 1, 1, 0.4, 0.8, 0.4, 1, 1, 0.4, 1.0, 1.0, 0.4, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.4, 0.8, 0.4, 0.0, 0.8, 0.4, 0.4, 0.8, 1, 1, 0.4, 0.6000000000000001, 1, 0.6000000000000001, 0.8, 1, 0.6000000000000001, 1.0, 1, 1.0, 0.4, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]

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: 200

Training results

Training Loss	Epoch	Step	Train Loss	Validation Loss	Losses
405015552.0	1.0	99	0.8747	389655488.0	[0.8, 0.8, 1, 1, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 0.8, 0.6000000000000001, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.6000000000000001, 0.8, 0.8, 1, 1, 1, 0.8, 1, 0.6000000000000001, 1, 1, 1, 1, 1, 1.0, 0.4, 1, 1, 0.8, 0.2, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1, 1, 1.0, 1, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 0.6000000000000001, 1, 1, 1, 0.8, 1, 1, 1, 0.8, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 0.8, 1, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 1, 1, 0.8, 1, 1, 1, 0.8, 0.0, 1, 0.4, 1, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 1, 1, 1.0, 1, 0.8, 1, 1, 0.0, 1, 1, 1, 1, 1, 0.8, 1.0, 0.6000000000000001, 1, 1, 1, 1, 1, 1, 0.6000000000000001, 0.0, 1.0, 1, 1, 0.2, 1, 1.0, 1, 1, 1, 0.8, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 1, 1, 0.8, 0.2, 1, 1, 1, 1, 0.8, 1, 1, 1, 1, 0.8, 0.8, 0.8, 0.8, 1, 1.0, 1, 1, 1, 1, 0.0, 1, 1, 0.6000000000000001, 0.6000000000000001, 0.8, 1, 0.8, 0.8, 1, 1, 1.0, 0.4, 0.6000000000000001, 1, 0.8, 0.8, 0.4, 1, 0.4, 1, 1, 0.4, 1, 1, 1.0, 1, 1, 0.8, 1.0, 0.8, 1, 1, 1, 0.4, 0.2, 0.8, 1, 0.4, 0.8, 1, 1, 0.8, 0.8, 1, 1, 0.6000000000000001, 1, 0.4, 1, 1, 1, 1, 1, 1, 1.0, 0.8, 1, 1, 1, 1.0, 1, 1, 0.8, 1.0, 1, 1, 1, 1, 1, 1, 1, 1, 0.4, 1, 0.4, 1, 1, 0.6000000000000001, 1.0, 1, 0.8, 1, 0.6000000000000001, 0.8, 1, 1, 1, 1, 1, 1, 1.0, 0.6000000000000001, 1.0, 0.4, 1, 1, 0.6000000000000001, 1, 0.8, 0.8, 1, 1, 1, 1, 0.6000000000000001, 1, 1.0, 0.4, 1, 1, 0.8, 0.6000000000000001, 1, 1, 1, 1, 0.2, 0.4, 1, 1.0, 1, 0.8, 0.8, 0.8, 1, 1.0, 1, 0.4, 1, 1, 0.6000000000000001, 1, 0.4, 1, 0.8, 0.6000000000000001, 1, 1, 0.0, 0.8, 1, 1, 0.6000000000000001, 0.6000000000000001, 1, 0.8, 1, 1, 1, 1, 0.4, 1, 0.4, 1, 1, 1, 1, 0.4, 0.6000000000000001, 1, 1, 1, 0.8, 1.0, 1, 1.0, 1, 1.0, 1, 1, 0.2, 1, 1, 1, 1, 1, 0.8, 1, 1, 1, 0.4, 1, 1, 1, 1, 1, 1, 0.0, 0.8, 0.6000000000000001, 1]
414025830.4	2.0	198	0.7287	389655456.0	[0.8, 0.8, 0.8, 1, 0.0, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 0.8, 1, 1, 1.0, 0.8, 0.6000000000000001, 0.6000000000000001, 0.4, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.2, 0.8, 0.8, 0.8, 0.8, 0.4, 1, 1, 0.8, 0.8, 0.2, 0.8, 0.8, 0.4, 0.8, 1, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.4, 1, 0.4, 0.8, 0.2, 0.8, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.2, 1, 0.8, 1, 0.8, 0.4, 0.8, 0.0, 0.8, 0.8, 0.0, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 1, 1, 0.8, 0.8, 0.8, 1, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.4, 0.0, 1.0, 1, 0.8, 0.8, 0.6000000000000001, 0.4, 1, 1, 0.6000000000000001, 0.0, 0.0, 0.8, 1, 0.2, 0.8, 0.6000000000000001, 0.8, 1, 0.8, 0.8, 1, 0.8, 1.0, 0.4, 0.8, 0.8, 0.8, 0.8, 0.8, 1.0, 0.8, 0.8, 0.8, 1, 1, 1, 1.0, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.4, 1, 1.0, 0.8, 0.8, 0.0, 0.4, 1, 1, 1, 1, 0.8, 0.6000000000000001, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 0.6000000000000001, 0.6000000000000001, 0.8, 0.4, 0.8, 0.8, 1, 0.0, 0.6000000000000001, 0.4, 0.2, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.2, 0.4, 0.8, 0.6000000000000001, 0.6000000000000001, 0.0, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 1, 0.8, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.8, 0.6000000000000001, 1, 1, 0.8, 0.8, 0.6000000000000001, 1, 0.8, 1, 0.8, 0.8, 0.8, 1.0, 0.8, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.4, 0.8, 0.8, 0.6000000000000001, 0.8, 0.2, 0.8, 1, 0.6000000000000001, 0.8, 0.0, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 1, 1, 1, 0.8, 0.6000000000000001, 1, 0.8, 0.6000000000000001, 0.8, 0.0, 1, 0.8, 0.0, 0.6000000000000001, 0.8, 1.0, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.0, 1, 0.8, 0.4, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 1, 0.8, 0.6000000000000001, 1, 1, 1, 0.0, 0.8, 1, 0.8, 0.0, 0.2, 0.8, 0.4, 0.8, 1, 1, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.0, 1, 1.0, 0.0, 0.0, 0.8, 0.8, 0.2, 0.0, 0.2, 0.6000000000000001, 1, 1, 0.8, 0.8, 1, 1.0, 0.0, 0.8, 1.0, 0.8, 0.8, 1, 0.8, 0.4, 0.6000000000000001, 0.4, 1]
408233932.8	3.0	297	0.7276	389655456.0	[0.8, 0.8, 0.8, 1, 0.0, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 0.8, 1, 1, 1.0, 0.8, 0.6000000000000001, 0.6000000000000001, 0.4, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.2, 0.8, 0.8, 0.8, 0.8, 0.4, 1, 1, 0.8, 0.8, 0.2, 0.8, 0.8, 0.4, 0.8, 1, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.4, 1, 0.4, 0.8, 0.2, 0.8, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.2, 1, 0.8, 1, 0.8, 0.4, 0.8, 0.0, 0.8, 0.8, 0.0, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 1, 1, 0.8, 0.8, 0.8, 1, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.4, 0.0, 1.0, 1, 0.8, 0.8, 0.6000000000000001, 0.4, 1, 1, 0.6000000000000001, 0.0, 0.0, 0.8, 1, 0.2, 0.8, 0.6000000000000001, 0.8, 1, 0.8, 0.8, 1, 0.8, 1.0, 0.4, 0.8, 0.8, 0.8, 0.8, 0.8, 1.0, 0.8, 0.8, 0.8, 1, 1, 1, 1.0, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.4, 1, 1.0, 0.8, 0.8, 0.0, 0.4, 1, 1, 1, 1, 0.8, 0.6000000000000001, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 0.6000000000000001, 0.8, 0.4, 0.8, 0.8, 1, 0.0, 0.6000000000000001, 0.4, 0.2, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.2, 0.4, 0.8, 0.6000000000000001, 0.6000000000000001, 0.0, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 1, 0.8, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.8, 0.6000000000000001, 1, 1, 0.8, 0.8, 0.6000000000000001, 1, 0.8, 1, 0.8, 0.8, 0.8, 1.0, 0.8, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.4, 0.8, 0.8, 0.6000000000000001, 0.8, 0.2, 0.8, 1, 0.6000000000000001, 0.8, 0.0, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 1, 1, 1, 0.8, 0.6000000000000001, 1, 0.8, 0.6000000000000001, 0.8, 0.0, 1, 0.8, 0.0, 0.6000000000000001, 0.8, 1.0, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.0, 1, 0.8, 0.4, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 1, 0.8, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.0, 0.8, 1, 0.8, 0.0, 0.2, 0.8, 0.4, 0.8, 1, 1, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.0, 1, 1.0, 0.0, 0.0, 0.8, 0.8, 0.2, 0.0, 0.2, 0.6000000000000001, 1, 1, 0.8, 0.8, 1, 1.0, 0.0, 0.8, 1.0, 0.8, 0.8, 1, 0.8, 0.4, 0.6000000000000001, 0.4, 1]
421449779.2	4.0	396	0.7276	389655456.0	[0.8, 0.8, 0.8, 1, 0.0, 0.6000000000000001, 0.8, 0.8, 0.6000000000000001, 0.8, 1, 1, 1.0, 0.8, 0.6000000000000001, 0.6000000000000001, 0.4, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.2, 0.8, 0.8, 0.8, 0.8, 0.4, 1, 1, 0.8, 0.8, 0.2, 0.8, 0.8, 0.4, 0.8, 1, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.4, 1, 0.4, 0.8, 0.2, 0.8, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.2, 1, 0.8, 1, 0.8, 0.4, 0.8, 0.0, 0.8, 0.8, 0.0, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 1, 1, 0.8, 0.8, 0.8, 1, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.4, 0.0, 1.0, 1, 0.8, 0.8, 0.6000000000000001, 0.4, 1, 1, 0.6000000000000001, 0.0, 0.0, 0.8, 1, 0.2, 0.8, 0.6000000000000001, 0.8, 1, 0.8, 0.8, 1, 0.8, 1.0, 0.4, 0.8, 0.8, 0.8, 0.8, 0.8, 1.0, 0.8, 0.8, 0.8, 1, 1, 1, 1.0, 0.8, 0.8, 0.8, 0.8, 1, 0.8, 0.6000000000000001, 0.8, 0.8, 0.4, 1, 1.0, 0.8, 0.8, 0.0, 0.4, 1, 1, 1, 1, 0.8, 0.6000000000000001, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 0.6000000000000001, 0.8, 0.4, 0.8, 0.8, 1, 0.0, 0.6000000000000001, 0.4, 0.2, 1, 0.8, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 0.4, 0.4, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 0.8, 0.2, 0.4, 0.8, 0.6000000000000001, 0.6000000000000001, 0.0, 0.8, 0.8, 0.8, 0.8, 1, 0.4, 1, 0.8, 0.8, 1, 1, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.0, 0.8, 0.6000000000000001, 1, 1, 0.8, 0.8, 0.6000000000000001, 1, 0.8, 1, 0.8, 0.8, 0.8, 1.0, 0.8, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.8, 0.4, 0.8, 0.8, 0.6000000000000001, 0.8, 0.2, 0.8, 1, 0.6000000000000001, 0.8, 0.0, 0.8, 0.8, 0.8, 0.8, 0.6000000000000001, 1, 1, 1, 0.8, 0.6000000000000001, 1, 0.8, 0.6000000000000001, 0.8, 0.0, 1, 0.8, 0.0, 0.6000000000000001, 0.8, 1.0, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 0.8, 1, 0.8, 0.8, 0.8, 0.0, 1, 0.8, 0.4, 0.8, 0.6000000000000001, 0.8, 0.8, 0.0, 0.8, 1, 0.8, 0.6000000000000001, 0.6000000000000001, 1, 1, 0.0, 0.8, 1, 0.8, 0.0, 0.2, 0.8, 0.4, 0.8, 1, 1, 1, 0.6000000000000001, 0.8, 0.8, 0.8, 0.8, 0.0, 1, 1.0, 0.0, 0.0, 0.8, 0.8, 0.2, 0.0, 0.2, 0.6000000000000001, 1, 1, 0.8, 0.8, 1, 1.0, 0.0, 0.8, 1.0, 0.8, 0.8, 1, 0.8, 0.4, 0.6000000000000001, 0.4, 1]
402870886.4	5.0	495	0.7365	389655424.0	[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]
418429363.2	6.0	594	0.7365	389655424.0	[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]

Framework versions

• Transformers 4.34.0
• Pytorch 2.1.0+cu121
• Datasets 2.6.1
• Tokenizers 0.14.1