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---
library_name: transformers
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-synthetic-data-plus-translated-bs32ep20lr5e3
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. -->
# mt5-small-synthetic-data-plus-translated-bs32ep20lr5e3
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3381
- Rouge1: 0.7165
- Rouge2: 0.6111
- Rougel: 0.7004
- Rougelsum: 0.7016
## 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: 0.0056
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.2243 | 1.0 | 38 | 0.9621 | 0.6801 | 0.5630 | 0.6599 | 0.6606 |
| 0.2209 | 2.0 | 76 | 0.9423 | 0.6766 | 0.5707 | 0.6633 | 0.6644 |
| 0.1953 | 3.0 | 114 | 0.9503 | 0.6525 | 0.5271 | 0.6361 | 0.6369 |
| 0.1812 | 4.0 | 152 | 0.9818 | 0.6811 | 0.5742 | 0.6672 | 0.6680 |
| 0.1418 | 5.0 | 190 | 0.9591 | 0.6868 | 0.5781 | 0.6700 | 0.6708 |
| 0.1312 | 6.0 | 228 | 1.0121 | 0.6900 | 0.5842 | 0.6734 | 0.6742 |
| 0.1236 | 7.0 | 266 | 0.9913 | 0.6787 | 0.5689 | 0.6652 | 0.6653 |
| 0.1068 | 8.0 | 304 | 0.9773 | 0.6886 | 0.5781 | 0.6749 | 0.6764 |
| 0.106 | 9.0 | 342 | 1.0201 | 0.6947 | 0.5825 | 0.6798 | 0.6802 |
| 0.084 | 10.0 | 380 | 1.0865 | 0.6861 | 0.5775 | 0.6726 | 0.6738 |
| 0.0744 | 11.0 | 418 | 1.0310 | 0.6997 | 0.5865 | 0.6849 | 0.6861 |
| 0.0618 | 12.0 | 456 | 1.1647 | 0.7118 | 0.6182 | 0.7016 | 0.7020 |
| 0.0493 | 13.0 | 494 | 1.1808 | 0.7089 | 0.6098 | 0.6959 | 0.6970 |
| 0.0472 | 14.0 | 532 | 1.2040 | 0.7087 | 0.6090 | 0.6956 | 0.6965 |
| 0.0399 | 15.0 | 570 | 1.1293 | 0.7065 | 0.6035 | 0.6953 | 0.6965 |
| 0.0346 | 16.0 | 608 | 1.2286 | 0.7078 | 0.6028 | 0.6928 | 0.6940 |
| 0.0255 | 17.0 | 646 | 1.2970 | 0.7114 | 0.6069 | 0.6986 | 0.7001 |
| 0.0241 | 18.0 | 684 | 1.3016 | 0.7053 | 0.5983 | 0.6893 | 0.6904 |
| 0.0217 | 19.0 | 722 | 1.3315 | 0.7137 | 0.6084 | 0.6999 | 0.7008 |
| 0.0196 | 20.0 | 760 | 1.3381 | 0.7165 | 0.6111 | 0.7004 | 0.7016 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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