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---
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tags:
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- summarization
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- ar
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- Abstractive Summarization
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- generated_from_trainer
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datasets:
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- wiki_lingua
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model-index:
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- name: AraT5-base-finetune-ar-wikilingua
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# AraT5-base-finetune-ar-wikilingua
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This model is a fine-tuned version of [UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) on the wiki_lingua dataset.
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It achieves the following results on the evaluation set:
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- Loss: 4.6110
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- Rouge-1: 19.97
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- Rouge-2: 6.9
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- Rouge-l: 18.25
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- Gen Len: 18.45
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- Bertscore: 69.44
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0005
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 250
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- num_epochs: 10
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- label_smoothing_factor: 0.1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
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| 11.5412 | 1.0 | 312 | 6.8825 | 5.2 | 0.69 | 5.04 | 19.0 | 63.2 |
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| 6.5212 | 2.0 | 624 | 5.8992 | 8.89 | 1.4 | 8.36 | 17.92 | 63.9 |
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| 5.8302 | 3.0 | 936 | 5.3712 | 9.99 | 2.21 | 9.54 | 15.87 | 65.08 |
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| 5.406 | 4.0 | 1248 | 5.0632 | 13.94 | 3.5 | 13.0 | 15.95 | 66.83 |
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| 5.1109 | 5.0 | 1560 | 4.8718 | 15.28 | 4.34 | 14.27 | 18.26 | 66.83 |
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| 4.9004 | 6.0 | 1872 | 4.7631 | 16.65 | 4.92 | 15.46 | 17.73 | 67.75 |
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| 4.754 | 7.0 | 2184 | 4.6920 | 18.31 | 5.79 | 16.9 | 18.17 | 68.55 |
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| 4.6369 | 8.0 | 2496 | 4.6459 | 18.6 | 6.12 | 17.16 | 18.16 | 68.66 |
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| 4.5595 | 9.0 | 2808 | 4.6153 | 18.94 | 6.1 | 17.39 | 17.82 | 68.99 |
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| 4.4967 | 10.0 | 3120 | 4.6110 | 19.15 | 6.25 | 17.55 | 17.91 | 69.09 |
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### Framework versions
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- Transformers 4.19.4
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- Pytorch 1.11.0+cu113
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- Datasets 2.2.2
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- Tokenizers 0.12.1
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