metadata
language: es
tags:
- summarization
- sagemaker
- mt5
- 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
- Example Notebooks
- Amazon SageMaker documentation for Hugging Face
- Python SDK SageMaker documentation for Hugging Face
- Deep Learning Container
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
| metric | score |
|---|---|
| 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 |