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update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- rouge
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model-index:
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- name: mt5-teste-full-length
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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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# mt5-teste-full-length
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This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5996
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- Rouge1: 0.5083
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- Rouge2: 0.2820
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- Rougel: 0.4095
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- Rougelsum: 0.4108
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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: 0.0005
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 32
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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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- lr_scheduler_warmup_steps: 90
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- num_epochs: 4
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
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| 9.1442 | 0.16 | 100 | 9.7852 | 0.0531 | 0.0 | 0.0524 | 0.0 |
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| 1.0643 | 0.33 | 200 | 0.9089 | 0.3623 | 0.1853 | 0.3252 | 0.3261 |
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| 0.8283 | 0.49 | 300 | 0.8361 | 0.4184 | 0.2112 | 0.3535 | 0.3548 |
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| 0.7754 | 0.65 | 400 | 0.7522 | 0.4407 | 0.2575 | 0.3802 | 0.3828 |
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| 0.8012 | 0.82 | 500 | 0.7226 | 0.4643 | 0.2638 | 0.3866 | 0.3866 |
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| 0.7758 | 0.98 | 600 | 0.7265 | 0.4624 | 0.2458 | 0.3840 | 0.3847 |
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| 0.6744 | 1.15 | 700 | 0.7018 | 0.4477 | 0.2469 | 0.3732 | 0.3741 |
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| 0.6636 | 1.31 | 800 | 0.6955 | 0.4786 | 0.2632 | 0.4027 | 0.4038 |
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| 0.6839 | 1.47 | 900 | 0.6737 | 0.4773 | 0.2689 | 0.3909 | 0.3898 |
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| 0.6264 | 1.64 | 1000 | 0.6504 | 0.4457 | 0.2533 | 0.3747 | 0.3767 |
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| 0.6641 | 1.8 | 1100 | 0.6442 | 0.4582 | 0.2428 | 0.3661 | 0.3659 |
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| 0.6492 | 1.96 | 1200 | 0.6500 | 0.5004 | 0.2751 | 0.3984 | 0.3993 |
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| 0.5823 | 2.13 | 1300 | 0.6344 | 0.4917 | 0.2743 | 0.4000 | 0.4016 |
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| 0.5585 | 2.29 | 1400 | 0.6373 | 0.4749 | 0.2490 | 0.3834 | 0.3849 |
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| 0.5748 | 2.45 | 1500 | 0.6168 | 0.5036 | 0.2915 | 0.4128 | 0.4145 |
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| 0.5452 | 2.62 | 1600 | 0.6135 | 0.5004 | 0.2864 | 0.4038 | 0.4044 |
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| 0.5735 | 2.78 | 1700 | 0.6164 | 0.4904 | 0.2689 | 0.4001 | 0.3993 |
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| 0.5394 | 2.95 | 1800 | 0.6153 | 0.4864 | 0.2884 | 0.4091 | 0.4089 |
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| 0.4816 | 3.11 | 1900 | 0.6070 | 0.5027 | 0.2765 | 0.4042 | 0.4031 |
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| 0.5328 | 3.27 | 2000 | 0.6095 | 0.4896 | 0.2783 | 0.4026 | 0.4031 |
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| 0.5157 | 3.44 | 2100 | 0.6021 | 0.5165 | 0.2853 | 0.4137 | 0.4145 |
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| 0.5295 | 3.6 | 2200 | 0.6063 | 0.4926 | 0.2721 | 0.3965 | 0.3980 |
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| 0.5027 | 3.76 | 2300 | 0.6004 | 0.5120 | 0.2885 | 0.4092 | 0.4103 |
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| 0.4943 | 3.93 | 2400 | 0.5996 | 0.5083 | 0.2820 | 0.4095 | 0.4108 |
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### Framework versions
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- Transformers 4.27.4
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- Pytorch 1.13.0
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- Datasets 2.1.0
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- Tokenizers 0.13.2
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