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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-teste-full-length
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-teste-full-length
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5750
- Rouge1: 0.4784
- Rouge2: 0.3008
- Rougel: 0.4185
- Rougelsum: 0.4212
## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 90
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.9357 | 0.16 | 100 | 2.5583 | 0.2654 | 0.0431 | 0.1946 | 0.1951 |
| 1.9974 | 0.33 | 200 | 1.7104 | 0.1803 | 0.0817 | 0.1712 | 0.1726 |
| 1.4803 | 0.49 | 300 | 1.4404 | 0.1770 | 0.0695 | 0.1707 | 0.1727 |
| 1.2432 | 0.65 | 400 | 1.0519 | 0.2809 | 0.1314 | 0.2509 | 0.2511 |
| 0.8186 | 0.82 | 500 | 0.7386 | 0.3487 | 0.1767 | 0.2894 | 0.2903 |
| 0.791 | 0.98 | 600 | 0.7135 | 0.3634 | 0.1912 | 0.3108 | 0.3108 |
| 0.6697 | 1.15 | 700 | 0.6835 | 0.3874 | 0.1900 | 0.3123 | 0.3131 |
| 0.7146 | 1.31 | 800 | 0.6657 | 0.3816 | 0.2209 | 0.3414 | 0.3428 |
| 0.6957 | 1.47 | 900 | 0.6498 | 0.3878 | 0.2045 | 0.3336 | 0.3339 |
| 0.6737 | 1.64 | 1000 | 0.6332 | 0.4094 | 0.2219 | 0.3524 | 0.3535 |
| 0.6537 | 1.8 | 1100 | 0.6369 | 0.4401 | 0.2621 | 0.3629 | 0.3630 |
| 0.6746 | 1.96 | 1200 | 0.6169 | 0.4369 | 0.2326 | 0.3566 | 0.3574 |
| 0.5961 | 2.13 | 1300 | 0.6171 | 0.4364 | 0.2464 | 0.3666 | 0.3670 |
| 0.5829 | 2.29 | 1400 | 0.6122 | 0.4539 | 0.2683 | 0.3813 | 0.3825 |
| 0.6336 | 2.45 | 1500 | 0.5993 | 0.4347 | 0.2548 | 0.3660 | 0.3689 |
| 0.5754 | 2.62 | 1600 | 0.5905 | 0.4575 | 0.2789 | 0.3856 | 0.3857 |
| 0.5984 | 2.78 | 1700 | 0.5872 | 0.4630 | 0.2768 | 0.3915 | 0.3929 |
| 0.5966 | 2.95 | 1800 | 0.5944 | 0.4605 | 0.2753 | 0.3822 | 0.3828 |
| 0.5288 | 3.11 | 1900 | 0.5955 | 0.4520 | 0.2651 | 0.3874 | 0.3887 |
| 0.5316 | 3.27 | 2000 | 0.5841 | 0.4649 | 0.2820 | 0.4052 | 0.4056 |
| 0.5332 | 3.44 | 2100 | 0.5765 | 0.4861 | 0.3046 | 0.4021 | 0.4050 |
| 0.5296 | 3.6 | 2200 | 0.5812 | 0.4610 | 0.2815 | 0.3976 | 0.4021 |
| 0.5215 | 3.76 | 2300 | 0.5757 | 0.4724 | 0.2947 | 0.4122 | 0.4164 |
| 0.5399 | 3.93 | 2400 | 0.5750 | 0.4784 | 0.3008 | 0.4185 | 0.4212 |
### Framework versions
- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
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