---
language: en
tags:
- sagemaker
- mt5
- summarization
- spanish
license: apache-2.0
datasets:
- mlsum - es
model-index:
- name: `mt5-small-mlsum`
  results:
  - task: 
      name: Abstractive Text Summarization
      type: abstractive-text-summarization
    dataset:
      name: "MLSUM: MultiLingual SUMmarization dataset (Spanish)" 
      type: mlsum
    metrics:
       - name: Validation ROGUE-1
         type: rogue-1
         value: 26.4352
       - name: Validation ROGUE-2
         type: rogue-2
         value: 8.9293
       - name: Validation ROGUE-L
         type: rogue-l
         value: 21.2622
       - name: Validation ROGUE-LSUM
         type: rogue-lsum
         value: 21.5518
       - name: Test ROGUE-1
         type: rogue-1
         value: 26.0756
       - name: Test ROGUE-2
         type: rogue-2
         value: 8.4669
       - name: Test ROGUE-L
         type: rogue-l
         value: 20.8167
       - name: Validation ROGUE-LSUM
         type: rogue-lsum
         value: 21.0822
widget:
- text: | 
    Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? 
    Philipp: Sure you can use the new Hugging Face Deep Learning Container. 
    Jeff: ok.
    Jeff: and how can I get started? 
    Jeff: where can I find documentation? 
    Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face 
---
## `mt5-small-mlsum`
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
For more information look at:
- [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
- [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker)
- [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html)
- [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html)
- [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers)
## Hyperparameters
{
    "dataset_config": "es",
    "dataset_name": "mlsum",
    "do_eval": true,
    "do_predict": true,
    "do_train": true,
    "fp16": true,
    "max_target_length": 64,
    "model_name_or_path": "google/mt5-small",
    "num_train_epochs": 10,
    "output_dir": "/opt/ml/checkpoints",
    "per_device_eval_batch_size": 4,
    "per_device_train_batch_size": 4,
    "predict_with_generate": true,
    "sagemaker_container_log_level": 20,
    "sagemaker_program": "run_summarization.py",
    "save_strategy": "epoch",
    "seed": 7,
    "summary_column": "summary",
    "text_column": "text"
}
## Usage

## Results
| key | value |
| --- | ----- |
| eval_rouge1 | 26.4352 |
| eval_rouge2 | 8.9293 |
| eval_rougeL | 21.2622 |
| eval_rougeLsum | 21.5518 |
| test_rouge1 | 26.0756 |
| test_rouge2 | 8.4669 |
| test_rougeL | 20.8167 |
| test_rougeLsum | 21.0822 |