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---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
metrics:
- rouge
- sacrebleu
model-index:
- name: mT5-TextSimp-LT-BatchSize4-lr1e-4
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-TextSimp-LT-BatchSize4-lr1e-4
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: 0.0737
- Rouge1: 0.7174
- Rouge2: 0.5553
- Rougel: 0.7108
- Sacrebleu: 43.3127
- Gen Len: 38.0501
## 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: 0.0001
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Sacrebleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 25.4634 | 0.48 | 200 | 18.5157 | 0.0061 | 0.0 | 0.0059 | 0.0008 | 512.0 |
| 1.1451 | 0.96 | 400 | 0.6596 | 0.0161 | 0.0003 | 0.0154 | 0.0215 | 39.0453 |
| 0.6441 | 1.44 | 600 | 0.4981 | 0.0272 | 0.0012 | 0.0259 | 0.0166 | 39.0453 |
| 0.247 | 1.91 | 800 | 0.1420 | 0.4769 | 0.2826 | 0.465 | 20.3212 | 38.0501 |
| 0.1549 | 2.39 | 1000 | 0.1032 | 0.6114 | 0.4299 | 0.5998 | 30.2603 | 38.0501 |
| 0.1482 | 2.87 | 1200 | 0.0934 | 0.6592 | 0.4815 | 0.6496 | 34.4213 | 38.0501 |
| 0.1163 | 3.35 | 1400 | 0.0867 | 0.6734 | 0.4968 | 0.6651 | 36.3741 | 38.0501 |
| 0.1042 | 3.83 | 1600 | 0.0816 | 0.6826 | 0.5127 | 0.6753 | 38.128 | 38.0501 |
| 0.1109 | 4.31 | 1800 | 0.0816 | 0.6893 | 0.5191 | 0.6818 | 39.3294 | 38.0501 |
| 0.1029 | 4.78 | 2000 | 0.0798 | 0.6968 | 0.5284 | 0.6901 | 40.5064 | 38.0501 |
| 0.0877 | 5.26 | 2200 | 0.0766 | 0.7006 | 0.5372 | 0.694 | 40.5295 | 38.0501 |
| 0.0748 | 5.74 | 2400 | 0.0759 | 0.7092 | 0.5403 | 0.7028 | 41.4424 | 38.0501 |
| 0.0941 | 6.22 | 2600 | 0.0754 | 0.7134 | 0.5471 | 0.7066 | 42.4212 | 38.0501 |
| 0.1095 | 6.7 | 2800 | 0.0737 | 0.7198 | 0.5547 | 0.7135 | 42.8225 | 38.0501 |
| 0.0749 | 7.18 | 3000 | 0.0735 | 0.7165 | 0.5536 | 0.7107 | 42.9748 | 38.0501 |
| 0.073 | 7.66 | 3200 | 0.0737 | 0.7174 | 0.5553 | 0.7108 | 43.3127 | 38.0501 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.4
- Tokenizers 0.13.3
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