---
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
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# 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