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---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
model-index:
- name: summarize
results: []
---
<!-- 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. -->
# summarize
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6935
- Evaluation: {'evaluation_runtime': 28.518348455429077, 'samples_per_second': 33.3118869588378, 'steps_per_second': 33.3118869588378}
- Rounded Rouge: {'rouge1': 0.1705, 'rouge2': 0.0588, 'rougeL': 0.1354, 'rougeLsum': 0.1355}
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Evaluation | Rounded Rouge |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------:|
| 3.1701 | 1.0 | 500 | 2.8229 | {'evaluation_runtime': 30.270989179611206, 'samples_per_second': 31.383183230756966, 'steps_per_second': 31.383183230756966} | {'rouge1': 0.1615, 'rouge2': 0.0525, 'rougeL': 0.128, 'rougeLsum': 0.1281} |
| 2.9661 | 2.0 | 1000 | 2.7672 | {'evaluation_runtime': 28.879830598831177, 'samples_per_second': 32.894929793613414, 'steps_per_second': 32.894929793613414} | {'rouge1': 0.1676, 'rouge2': 0.0567, 'rougeL': 0.1326, 'rougeLsum': 0.1327} |
| 2.9128 | 3.0 | 1500 | 2.7414 | {'evaluation_runtime': 28.787310361862183, 'samples_per_second': 33.00065160858421, 'steps_per_second': 33.00065160858421} | {'rouge1': 0.1693, 'rouge2': 0.0575, 'rougeL': 0.1342, 'rougeLsum': 0.1343} |
| 2.8783 | 4.0 | 2000 | 2.7240 | {'evaluation_runtime': 28.755173683166504, 'samples_per_second': 33.03753301814126, 'steps_per_second': 33.03753301814126} | {'rouge1': 0.1694, 'rouge2': 0.0581, 'rougeL': 0.1343, 'rougeLsum': 0.1344} |
| 2.8548 | 5.0 | 2500 | 2.7137 | {'evaluation_runtime': 30.050004959106445, 'samples_per_second': 31.613971488284534, 'steps_per_second': 31.613971488284534} | {'rouge1': 0.171, 'rouge2': 0.0591, 'rougeL': 0.1354, 'rougeLsum': 0.1354} |
| 2.8353 | 6.0 | 3000 | 2.7047 | {'evaluation_runtime': 29.376569986343384, 'samples_per_second': 32.33869714679546, 'steps_per_second': 32.33869714679546} | {'rouge1': 0.1703, 'rouge2': 0.0587, 'rougeL': 0.135, 'rougeLsum': 0.135} |
| 2.8229 | 7.0 | 3500 | 2.6996 | {'evaluation_runtime': 27.381307363510132, 'samples_per_second': 34.69520236517353, 'steps_per_second': 34.69520236517353} | {'rouge1': 0.1714, 'rouge2': 0.0592, 'rougeL': 0.1357, 'rougeLsum': 0.1357} |
| 2.8154 | 8.0 | 4000 | 2.6958 | {'evaluation_runtime': 27.409220457077026, 'samples_per_second': 34.65986934899169, 'steps_per_second': 34.65986934899169} | {'rouge1': 0.17, 'rouge2': 0.0587, 'rougeL': 0.1351, 'rougeLsum': 0.1352} |
| 2.8068 | 9.0 | 4500 | 2.6943 | {'evaluation_runtime': 27.376741409301758, 'samples_per_second': 34.7009889086807, 'steps_per_second': 34.7009889086807} | {'rouge1': 0.1702, 'rouge2': 0.0588, 'rougeL': 0.1352, 'rougeLsum': 0.1353} |
| 2.8 | 10.0 | 5000 | 2.6935 | {'evaluation_runtime': 28.518348455429077, 'samples_per_second': 33.3118869588378, 'steps_per_second': 33.3118869588378} | {'rouge1': 0.1705, 'rouge2': 0.0588, 'rougeL': 0.1354, 'rougeLsum': 0.1355} |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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