xlmroberta2xlmroberta-finetune-summarization-ar

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xlmroberta2xlmroberta-finetune-summarization-ar

This model is a fine-tuned version of on the xlsum dataset. It achieves the following results on the evaluation set:

• Loss: 4.1298
• Rouge-1: 21.69
• Rouge-2: 8.73
• Rouge-l: 19.52
• Gen Len: 19.96
• Bertscore: 71.0

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: 5e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 250
• num_epochs: 10
• label_smoothing_factor: 0.1

Training results

Framework versions

• Transformers 4.19.4
• Pytorch 1.11.0+cu113
• Datasets 2.2.2
• Tokenizers 0.12.1