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metadata
tags:
  - summarization
  - ar
  - encoder-decoder
  - xlm-roberta
  - Abstractive Summarization
  - roberta
  - generated_from_trainer
datasets:
  - xlsum
model-index:
  - name: xlmroberta2xlmroberta-finetune-summarization-ar
    results: []

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

Training Loss Epoch Step Validation Loss Rouge-1 Rouge-2 Rouge-l Gen Len Bertscore
8.0645 1.0 1172 7.3567 8.22 0.66 7.94 20.0 58.18
7.2042 2.0 2344 6.6058 12.12 2.19 11.4 20.0 63.24
6.4168 3.0 3516 5.8784 16.46 4.31 15.15 20.0 66.3
5.4622 4.0 4688 4.7931 17.6 5.87 15.9 19.99 69.21
4.7829 5.0 5860 4.4418 19.17 6.74 17.22 19.98 70.23
4.4829 6.0 7032 4.2950 19.8 7.11 17.74 19.98 70.38
4.304 7.0 8204 4.2155 20.71 7.59 18.56 19.98 70.66
4.1778 8.0 9376 4.1632 21.1 7.94 18.99 19.98 70.86
4.0886 9.0 10548 4.1346 21.44 8.03 19.28 19.98 70.93
4.0294 10.0 11720 4.1298 21.51 8.14 19.33 19.98 71.02

Framework versions

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