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--- |
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license: apache-2.0 |
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base_model: google-t5/t5-small |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: t5_summarize |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# t5_summarize |
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This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6492 |
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- Evaluation Runtime: 28.4792 |
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- Rounded Rouge Scores: {'rouge1': 0.174, 'rouge2': 0.0607, 'rougeL': 0.1367, 'rougeLsum': 0.1369} |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Evaluation Runtime | Rounded Rouge Scores | |
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|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------------------------------------------------------------------:| |
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| 2.7245 | 1.0 | 500 | 2.6814 | 29.2864 | {'rouge1': 0.1697, 'rouge2': 0.0584, 'rougeL': 0.1344, 'rougeLsum': 0.1345} | |
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| 2.7318 | 2.0 | 1000 | 2.6707 | 27.6464 | {'rouge1': 0.1735, 'rouge2': 0.0597, 'rougeL': 0.1372, 'rougeLsum': 0.1373} | |
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| 2.7164 | 3.0 | 1500 | 2.6646 | 27.3926 | {'rouge1': 0.1734, 'rouge2': 0.06, 'rougeL': 0.1371, 'rougeLsum': 0.1372} | |
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| 2.7054 | 4.0 | 2000 | 2.6600 | 27.3819 | {'rouge1': 0.1739, 'rouge2': 0.0599, 'rougeL': 0.1367, 'rougeLsum': 0.1368} | |
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| 2.6955 | 5.0 | 2500 | 2.6581 | 27.9933 | {'rouge1': 0.1731, 'rouge2': 0.0601, 'rougeL': 0.1361, 'rougeLsum': 0.1361} | |
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| 2.6865 | 6.0 | 3000 | 2.6535 | 28.2157 | {'rouge1': 0.1733, 'rouge2': 0.0603, 'rougeL': 0.1363, 'rougeLsum': 0.1364} | |
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| 2.6821 | 7.0 | 3500 | 2.6521 | 29.0758 | {'rouge1': 0.174, 'rouge2': 0.0606, 'rougeL': 0.1366, 'rougeLsum': 0.1369} | |
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| 2.681 | 8.0 | 4000 | 2.6508 | 31.2621 | {'rouge1': 0.1743, 'rouge2': 0.0609, 'rougeL': 0.1367, 'rougeLsum': 0.1369} | |
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| 2.6771 | 9.0 | 4500 | 2.6499 | 30.4251 | {'rouge1': 0.1735, 'rouge2': 0.0605, 'rougeL': 0.1364, 'rougeLsum': 0.1365} | |
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| 2.6751 | 10.0 | 5000 | 2.6492 | 28.4792 | {'rouge1': 0.174, 'rouge2': 0.0607, 'rougeL': 0.1367, 'rougeLsum': 0.1369} | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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