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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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I64
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F32
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Evaluation results