text-sum

This model is a fine-tuned version of buianh0803/Text_Summarization on the cnn_dailymail dataset. It achieves the following results on the evaluation set:

• Loss: 1.6668
• Rouge1: 0.2484
• Rouge2: 0.1187
• Rougel: 0.2056
• Rougelsum: 0.2055
• Gen Len: 18.9986

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.8345	1.0	17945	1.6835	0.2475	0.118	0.2047	0.2047	18.998
1.8152	2.0	35890	1.6720	0.2479	0.1179	0.2048	0.2048	18.9986
1.7954	3.0	53835	1.6712	0.2477	0.1182	0.205	0.2051	18.9981
1.7975	4.0	71780	1.6680	0.2482	0.1186	0.2054	0.2054	18.9981
1.7924	5.0	89725	1.6668	0.2484	0.1187	0.2056	0.2055	18.9986

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.1