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metadata
tags:
  - summarization
  - mT5_multilingual_XLSum
  - mt5
  - abstractive summarization
  - ar
  - xlsum
  - generated_from_trainer
datasets:
  - xlsum
model-index:
  - name: mT5_multilingual_XLSum-finetune-ar-xlsum
    results: []

mT5_multilingual_XLSum-finetune-ar-xlsum

This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the xlsum dataset. It achieves the following results on the evaluation set:

  • Loss: 3.2497
  • Rouge-1: 32.52
  • Rouge-2: 14.71
  • Rouge-l: 27.88
  • Gen Len: 41.45
  • Bertscore: 74.65

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: 0.0005
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 250
  • num_epochs: 8
  • label_smoothing_factor: 0.1

Training results

Training Loss Epoch Step Validation Loss Rouge-1 Rouge-2 Rouge-l Gen Len Bertscore
3.5465 1.0 585 3.3215 30.09 13.23 26.07 36.31 73.97
3.3564 2.0 1170 3.2547 31.29 13.93 26.75 41.68 74.22
3.2185 3.0 1755 3.2421 31.78 14.1 27.07 41.64 74.4
3.1145 4.0 2340 3.2241 31.98 14.38 27.51 40.29 74.46
3.031 5.0 2925 3.2313 32.3 14.67 27.83 39.81 74.61
2.9627 6.0 3510 3.2348 32.39 14.65 27.76 40.02 74.6
2.9088 7.0 4095 3.2439 32.5 14.66 27.81 41.2 74.65
2.8649 8.0 4680 3.2497 32.52 14.71 27.88 41.45 74.65

Framework versions

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