---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: eval-mt5-base-aggressive
  results:
  - task:
      name: Summarization
      type: summarization
    dataset:
      name: mlsum tu
      type: mlsum
      args: tu
    metrics:
    - name: Rouge1
      type: rouge
      value: 47.4222
---

<!-- 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. -->

# eval-mt5-base-aggressive

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum tu dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7801
- Rouge1: 47.4222
- Rouge2: 34.8624
- Rougel: 42.2487
- Rougelsum: 43.9494
- Gen Len: 51.3525

## 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: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.084         | 1.0   | 3895  | 2.9282          | 31.6872 | 22.1113 | 29.2851 | 29.7608   | 18.9861 |
| 2.9162        | 2.0   | 7790  | 2.8552          | 32.1716 | 22.5001 | 29.6845 | 30.1887   | 18.9938 |
| 2.8149        | 3.0   | 11685 | 2.8089          | 32.5681 | 22.689  | 30.0409 | 30.5507   | 18.9959 |
| 2.7325        | 4.0   | 15580 | 2.7948          | 33.1236 | 23.1775 | 30.5156 | 31.0461   | 18.9958 |
| 2.6679        | 5.0   | 19475 | 2.7810          | 33.1766 | 23.162  | 30.4802 | 31.0527   | 18.9967 |
| 2.6237        | 6.0   | 23370 | 2.7790          | 33.1118 | 23.2043 | 30.5064 | 31.0096   | 18.9978 |
| 2.5711        | 7.0   | 27265 | 2.7801          | 33.2033 | 23.2957 | 30.59   | 31.1504   | 18.9979 |
| 2.538         | 8.0   | 31160 | 2.7777          | 33.0256 | 23.0621 | 30.3818 | 30.978    | 18.998  |
| 2.5           | 9.0   | 35055 | 2.7839          | 33.2288 | 23.2361 | 30.5421 | 31.1573   | 18.998  |
| 2.4719        | 10.0  | 38950 | 2.7832          | 33.2098 | 23.2274 | 30.5164 | 31.1094   | 18.9981 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.8.2+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3