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---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-arith
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-finetuned-arith
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co./google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6651
- Rouge1: 90.0
- Rouge2: 70.4082
- Rougel: 85.3061
- Rougelsum: 85.102
## 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: 5.6e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 64
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log | 1.0 | 7 | 11.7623 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 14 | 11.0473 | 0.2041 | 0.0 | 0.2041 | 0.2041 |
| No log | 3.0 | 21 | 9.4965 | 0.4082 | 0.0 | 0.4082 | 0.4082 |
| No log | 4.0 | 28 | 8.3848 | 0.8673 | 0.0 | 0.8673 | 0.8673 |
| No log | 5.0 | 35 | 7.6170 | 1.7515 | 0.0 | 1.7114 | 1.6753 |
| No log | 6.0 | 42 | 7.0008 | 4.9101 | 0.0 | 4.9093 | 4.8585 |
| No log | 7.0 | 49 | 6.7836 | 8.0777 | 0.0 | 7.7956 | 7.9186 |
| 16.7453 | 8.0 | 56 | 6.6780 | 12.3572 | 0.0 | 12.1332 | 11.878 |
| 16.7453 | 9.0 | 63 | 5.2800 | 13.5863 | 0.1701 | 12.7907 | 12.8991 |
| 16.7453 | 10.0 | 70 | 4.4990 | 13.8751 | 0.1701 | 13.1962 | 13.1834 |
| 16.7453 | 11.0 | 77 | 4.3624 | 13.4276 | 0.1701 | 13.3009 | 13.2722 |
| 16.7453 | 12.0 | 84 | 4.1101 | 14.0537 | 0.3401 | 13.3534 | 13.354 |
| 16.7453 | 13.0 | 91 | 3.7171 | 14.2128 | 0.3401 | 13.4985 | 13.4888 |
| 16.7453 | 14.0 | 98 | 3.4322 | 13.9164 | 0.1701 | 13.3916 | 13.3625 |
| 16.7453 | 15.0 | 105 | 3.2408 | 13.931 | 0.3401 | 13.7998 | 13.7901 |
| 6.4188 | 16.0 | 112 | 3.0734 | 14.0816 | 0.3401 | 13.7901 | 13.7901 |
| 6.4188 | 17.0 | 119 | 2.9270 | 14.344 | 0.8242 | 14.1983 | 14.208 |
| 6.4188 | 18.0 | 126 | 2.7746 | 16.7178 | 2.4928 | 16.3946 | 16.4334 |
| 6.4188 | 19.0 | 133 | 2.6117 | 22.7164 | 7.4678 | 22.1643 | 22.1381 |
| 6.4188 | 20.0 | 140 | 2.4419 | 25.0641 | 9.4306 | 24.2861 | 24.2714 |
| 6.4188 | 21.0 | 147 | 2.2793 | 32.0373 | 13.6803 | 31.0317 | 30.8515 |
| 6.4188 | 22.0 | 154 | 2.0741 | 40.1666 | 21.0894 | 38.5458 | 38.4592 |
| 6.4188 | 23.0 | 161 | 1.8635 | 40.1133 | 21.1222 | 38.1971 | 38.1165 |
| 3.1581 | 24.0 | 168 | 1.6788 | 47.1732 | 25.3843 | 44.6854 | 44.6021 |
| 3.1581 | 25.0 | 175 | 1.5153 | 49.4894 | 27.0538 | 46.9745 | 46.8775 |
| 3.1581 | 26.0 | 182 | 1.3337 | 47.7463 | 25.9589 | 45.3779 | 45.2896 |
| 3.1581 | 27.0 | 189 | 1.1634 | 48.6608 | 26.067 | 46.293 | 46.1794 |
| 3.1581 | 28.0 | 196 | 1.0392 | 86.6181 | 65.5782 | 81.9242 | 81.8732 |
| 3.1581 | 29.0 | 203 | 0.9519 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 3.1581 | 30.0 | 210 | 0.8837 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 3.1581 | 31.0 | 217 | 0.8246 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 2.0354 | 32.0 | 224 | 0.7630 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 2.0354 | 33.0 | 231 | 0.7221 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 2.0354 | 34.0 | 238 | 0.6957 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 2.0354 | 35.0 | 245 | 0.6852 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 2.0354 | 36.0 | 252 | 0.6734 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 2.0354 | 37.0 | 259 | 0.6667 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 2.0354 | 38.0 | 266 | 0.6670 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 2.0354 | 39.0 | 273 | 0.6684 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.5363 | 40.0 | 280 | 0.6626 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.5363 | 41.0 | 287 | 0.6621 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.5363 | 42.0 | 294 | 0.6699 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.5363 | 43.0 | 301 | 0.6751 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.5363 | 44.0 | 308 | 0.6839 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.5363 | 45.0 | 315 | 0.6987 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.5363 | 46.0 | 322 | 0.7060 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.5363 | 47.0 | 329 | 0.7125 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.324 | 48.0 | 336 | 0.7103 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.324 | 49.0 | 343 | 0.7098 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.324 | 50.0 | 350 | 0.7088 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.324 | 51.0 | 357 | 0.7112 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.324 | 52.0 | 364 | 0.7094 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.324 | 53.0 | 371 | 0.7041 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.324 | 54.0 | 378 | 0.6939 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.2374 | 55.0 | 385 | 0.6843 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.2374 | 56.0 | 392 | 0.6791 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.2374 | 57.0 | 399 | 0.6755 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.2374 | 58.0 | 406 | 0.6715 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.2374 | 59.0 | 413 | 0.6661 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.2374 | 60.0 | 420 | 0.6639 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.2374 | 61.0 | 427 | 0.6629 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.2374 | 62.0 | 434 | 0.6635 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.199 | 63.0 | 441 | 0.6646 | 90.0 | 70.4082 | 85.3061 | 85.102 |
| 1.199 | 64.0 | 448 | 0.6651 | 90.0 | 70.4082 | 85.3061 | 85.102 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.12.1
- Datasets 2.14.5
- Tokenizers 0.13.3
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