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metadata
base_model: UBC-NLP/AraT5v2-base-1024
tags:
  - summarization
  - Arat5v2
  - abstractive summarization
  - ar
  - xlsum
  - generated_from_trainer
datasets:
  - xlsum
model-index:
  - name: AraT5v2-base-1024-finetune-ar-xlsum
    results: []

AraT5v2-base-1024-finetune-ar-xlsum

This model is a fine-tuned version of UBC-NLP/AraT5v2-base-1024 on the xlsum dataset. It achieves the following results on the evaluation set:

  • Loss: 3.7983
  • Rouge-1: 33.4
  • Rouge-2: 16.14
  • Rouge-l: 29.31
  • Gen Len: 18.63
  • Bertscore: 74.57

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: 12
  • eval_batch_size: 12
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 192
  • 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
6.1614 1.0 195 3.9898 28.51 12.02 24.64 18.87 72.64
4.5342 2.0 390 3.9048 29.5 13.01 25.85 18.53 73.34
4.2029 3.0 585 3.8162 31.64 14.33 27.54 18.57 73.88
3.9689 4.0 781 3.7949 31.87 14.56 27.9 18.55 74.04
3.8278 5.0 976 3.7702 31.85 14.58 27.74 18.74 73.96
3.6921 6.0 1171 3.7775 32.27 14.95 28.16 18.78 74.23
3.5632 7.0 1367 3.7751 32.54 15.04 28.4 18.72 74.36
3.493 8.0 1562 3.7815 32.35 14.95 28.24 18.71 74.32
3.4189 9.0 1757 3.7908 32.39 14.99 28.32 18.73 74.32
3.3492 9.98 1950 3.7983 32.6 15.19 28.5 18.72 74.35

Framework versions

  • Transformers 4.33.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3