t5-small-med-term-conditional-masking-0

This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6688
• Rouge2 Precision: 0.694
• Rouge2 Recall: 0.4781
• Rouge2 Fmeasure: 0.5479

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: 2e-05
• 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: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge2 Precision	Rouge2 Recall	Rouge2 Fmeasure
0.9525	1.0	13915	0.8148	0.6657	0.4581	0.5252
0.8541	2.0	27830	0.7562	0.6779	0.4694	0.5371
0.8183	3.0	41745	0.7268	0.6827	0.4722	0.5405
0.8033	4.0	55660	0.7074	0.6861	0.4729	0.5419
0.7727	5.0	69575	0.6934	0.6872	0.4726	0.5419
0.7704	6.0	83490	0.6832	0.6901	0.4742	0.544
0.7485	7.0	97405	0.6771	0.6926	0.4772	0.5469
0.7528	8.0	111320	0.6722	0.6934	0.4782	0.5478
0.7535	9.0	125235	0.6696	0.6944	0.4782	0.5481
0.7444	10.0	139150	0.6688	0.694	0.4781	0.5479

Framework versions

• Transformers 4.17.0
• Pytorch 1.10.0+cu111
• Datasets 2.0.0
• Tokenizers 0.11.6