Loss: nan
Gen Len: 16.6760
Rouge Score: {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}
Bleu Score: {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}
Bleurt Score: -0.4863
Bert Score: [0.9187235832214355, 0.9003126621246338, 0.9092234373092651]

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: 0.0002
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: 10

Training results

Epoch	Step	Validation Loss	Gen Len	Rouge Score	Bleu Score	Bleurt Score	Bert Score
1.0	921	nan	16.6760	{'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}	{'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}	-0.4863	[0.9187235832214355, 0.9003126621246338, 0.9092234373092651]
2.0	1842	nan	16.6760	{'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}	{'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}	-0.4863	[0.9187235832214355, 0.9003126621246338, 0.9092234373092651]
3.0	2763	nan	16.6760	{'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}	{'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}	-0.4863	[0.9187235832214355, 0.9003126621246338, 0.9092234373092651]
4.0	3684	nan	16.6760	{'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}	{'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}	-0.4863	[0.9187235832214355, 0.9003126621246338, 0.9092234373092651]
5.0	4605	nan	16.6760	{'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}	{'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}	-0.4863	[0.9187235832214355, 0.9003126621246338, 0.9092234373092651]
6.0	5526	nan	16.6760	{'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}	{'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}	-0.4863	[0.9187235832214355, 0.9003126621246338, 0.9092234373092651]
7.0	6447	nan	16.6760	{'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}	{'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}	-0.4863	[0.9187235832214355, 0.9003126621246338, 0.9092234373092651]
8.0	7368	nan	16.6760	{'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}	{'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}	-0.4863	[0.9187235832214355, 0.9003126621246338, 0.9092234373092651]
9.0	8289	nan	16.6760	{'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}	{'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}	-0.4863	[0.9187235832214355, 0.9003126621246338, 0.9092234373092651]
10.0	9210	nan	16.6760	{'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}	{'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}	-0.4863	[0.9187235832214355, 0.9003126621246338, 0.9092234373092651]

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.10.0
Tokenizers 0.13.3

sentientconch/flant5_sum_samsum

flant5_sum_samsum

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sentientconch/
flant5_sum_samsum