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beto2beto-mlsum
This model was trained on the Spanish section of MLSum: https://paperswithcode.com/sota/abstractive-text-summarization-on-mlsum.
Hyperparameters
{
"dataset_config": "es",
"dataset_name": "mlsum",
"do_eval": true,
"do_predict": true,
"do_train": true,
"fp16": true,
"max_target_length": 64,
"num_train_epochs": 10,
"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",
"seed": 7,
"summary_column": "summary",
"text_column": "text"
}
Usage
Results
metric | score |
---|---|
validation_loss | 2.5021677017211914 |
validation_rouge1 | 26.1256 |
validation_rouge2 | 9.2552 |
validation_rougeL | 21.4899 |
validation_rougeLsum | 21.8194 |
test_loss | 2.57672381401062 |
test_rouge1 | 25.8639 |
test_rouge2 | 8.911 |
test_rougeL | 21.2426 |
test_rougeLsum | 21.5859 |
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Evaluation results
- rouge1 on mlsum-esself-reported25.864
- rouge2 on mlsum-esself-reported8.911
- rougeL on mlsum-esself-reported21.243
- rougeLsum on mlsum-esself-reported21.586