flan-t5-base-Cause-finetuned

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

• Loss: nan
• Rouge1: 11.7221
• Rouge2: 8.3773
• Rougel: 11.3413
• Rougelsum: 11.3455
• Gen Len: 17.9486

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	248	nan	11.7221	8.3773	11.3413	11.3455	17.9486
No log	2.0	496	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	3.0	744	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	4.0	992	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	5.0	1240	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	6.0	1488	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	7.0	1736	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	8.0	1984	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	9.0	2232	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	10.0	2480	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	11.0	2728	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	12.0	2976	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	13.0	3224	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	14.0	3472	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	15.0	3720	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	16.0	3968	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	17.0	4216	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	18.0	4464	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	19.0	4712	nan	11.7221	8.3773	11.3413	11.3455	17.9486
0.0	20.0	4960	nan	11.7221	8.3773	11.3413	11.3455	17.9486

Framework versions

• Transformers 4.41.1
• Pytorch 1.13.1+cu117
• Datasets 2.19.1
• Tokenizers 0.19.1