flant5_sum_samsum / README.md
sentientconch's picture
update model card README.md
35821b1
metadata
license: apache-2.0
base_model: google/flan-t5-base
tags:
  - generated_from_trainer
model-index:
  - name: flant5_sum_samsum
    results: []

flant5_sum_samsum

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

  • 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

Training Loss Epoch Step Validation Loss Gen Len Rouge Score Bleu Score Bleurt Score Bert Score
0.0 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]
0.0 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]
0.0 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]
0.0 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]
0.0 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]
0.0 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]
0.0 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]
0.0 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]
0.0 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]
0.0 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