---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mT5-TextSimp-LT-BatchSize8
  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-BatchSize8

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: 0.5250
- Rouge1: 0.0062
- Rouge2: 0.0
- Rougel: 0.0062
- Gen Len: 39.0501

## 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: 8
- eval_batch_size: 8
- 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 | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:-------:|
| 16.3791       | 0.96  | 200  | 11.2580         | 0.0041 | 0.0001 | 0.0041 | 512.0   |
| 1.4082        | 1.91  | 400  | 0.9502          | 0.0237 | 0.0002 | 0.0234 | 39.0501 |
| 0.7272        | 2.87  | 600  | 0.6131          | 0.0033 | 0.0    | 0.0032 | 39.0501 |
| 0.6517        | 3.83  | 800  | 0.5775          | 0.0019 | 0.0    | 0.0018 | 39.0501 |
| 0.6625        | 4.78  | 1000 | 0.5559          | 0.0014 | 0.0    | 0.0014 | 39.0501 |
| 0.5423        | 5.74  | 1200 | 0.5359          | 0.0016 | 0.0    | 0.0016 | 39.0501 |
| 0.7486        | 6.7   | 1400 | 0.5289          | 0.007  | 0.0    | 0.0069 | 39.0501 |
| 0.6712        | 7.66  | 1600 | 0.5250          | 0.0062 | 0.0    | 0.0062 | 39.0501 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.1
- Datasets 2.16.1
- Tokenizers 0.15.0