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flant5_sum_samsum
sentientconch/flant5_sum_samsum
flant5_sum_samsum is a machine learning model from sentientconch. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Jul 30, 2023
- Updated
- Jul 30, 2023
- SHA
- 35821b14
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
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
- Library
- transformers
- Type
- t5
- License
- apache-2.0
- Created
- Jul 30, 2023
- Updated
- Jul 30, 2023