mt5-small-finetuned-persian-news-summarization-with-additional-hyperparameters

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mt5-small-finetuned-persian-news-summarization-with-additional-hyperparameters

This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.2368
• Rouge1 Precision: 61.1637
• Rouge1 Recall: 50.8564
• Rouge1 F1: 53.4468
• Rouge2 Precision: 43.7489
• Rouge2 Recall: 36.4625
• Rouge2 F1: 38.2205
• Rougel Precision: 53.1324
• Rougel Recall: 44.3405
• Rougel F1: 46.5079

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: 4e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 4

Training results

Framework versions

• Transformers 4.57.6
• Pytorch 2.11.0+cu128
• Datasets 4.8.5
• Tokenizers 0.22.2