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tags: |
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- summarization |
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- ar |
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- encoder-decoder |
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- arabert |
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- arabert2arabert |
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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: arabert2arabert-finetuned-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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# arabert2arabert-finetuned-ar-wikilingua |
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This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_lingua dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.6877 |
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- Rouge-1: 13.2 |
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- Rouge-2: 3.43 |
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- Rouge-l: 12.45 |
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- Gen Len: 20.0 |
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- Bertscore: 64.88 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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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: 8 |
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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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| 6.7667 | 1.0 | 156 | 5.3846 | 3.36 | 0.56 | 3.27 | 20.0 | 60.6 | |
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| 5.257 | 2.0 | 312 | 5.0424 | 5.44 | 0.88 | 5.35 | 20.0 | 60.56 | |
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| 4.743 | 3.0 | 468 | 4.8294 | 9.21 | 1.8 | 8.93 | 20.0 | 62.91 | |
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| 4.3832 | 4.0 | 624 | 4.7240 | 9.88 | 2.19 | 9.6 | 20.0 | 62.65 | |
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| 4.1166 | 5.0 | 780 | 4.6861 | 11.61 | 2.86 | 11.13 | 20.0 | 63.71 | |
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| 3.91 | 6.0 | 936 | 4.6692 | 12.27 | 3.11 | 11.76 | 20.0 | 64.07 | |
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| 3.7569 | 7.0 | 1092 | 4.6805 | 12.93 | 3.38 | 12.28 | 20.0 | 64.61 | |
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| 3.6454 | 8.0 | 1248 | 4.6877 | 13.2 | 3.43 | 12.45 | 20.0 | 64.88 | |
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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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