ahmeddbahaa's picture
update model card README.md
2b7822f
metadata
tags:
  - summarization
  - Arat5-base
  - abstractive summarization
  - ar
  - xlsum
  - generated_from_trainer
datasets:
  - xlsum
model-index:
  - name: AraT5-base-finetune-ar-xlsum
    results: []

AraT5-base-finetune-ar-xlsum

This model is a fine-tuned version of UBC-NLP/AraT5-base on the xlsum dataset. It achieves the following results on the evaluation set:

  • Loss: 4.4714
  • Rouge-1: 29.55
  • Rouge-2: 12.63
  • Rouge-l: 25.8
  • Gen Len: 18.76
  • Bertscore: 73.3

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 128
  • 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
11.9753 1.0 293 7.0887 11.93 2.56 10.93 17.19 63.85
6.7818 2.0 586 5.7712 19.94 6.34 17.65 18.64 69.0
5.9434 3.0 879 5.1083 23.51 8.56 20.66 18.88 70.78
5.451 4.0 1172 4.8538 25.84 10.05 22.63 18.42 72.04
5.1643 5.0 1465 4.6910 27.23 11.13 23.83 18.78 72.45
4.9693 6.0 1758 4.5950 28.42 11.71 24.82 18.74 72.94
4.8308 7.0 2051 4.5323 28.95 12.19 25.3 18.74 73.13
4.7284 8.0 2344 4.4956 29.19 12.37 25.53 18.76 73.18
4.653 9.0 2637 4.4757 29.44 12.48 25.63 18.78 73.23
4.606 10.0 2930 4.4714 29.55 12.63 25.8 18.76 73.3

Framework versions

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