bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum

This model is a fine-tuned version of Chemsseddine/bert2gpt2SUMM-finetuned-mlsum on the orange_sum dataset. It achieves the following results on the evaluation set:

• Loss: 3.1773
• Rouge1: 24.949
• Rouge2: 7.851
• Rougel: 18.1575
• Rougelsum: 18.4114
• Gen Len: 39.7947

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: 1
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
3.5484	1.0	1338	3.1773	24.949	7.851	18.1575	18.4114	39.7947

Framework versions

• Transformers 4.20.0
• Pytorch 1.11.0+cu113
• Datasets 2.3.2
• Tokenizers 0.12.1