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tags: |
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- summarization |
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- Arat5-base |
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- abstractive summarization |
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- ar |
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- xlsum |
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- generated_from_trainer |
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datasets: |
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- xlsum |
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model-index: |
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- name: AraT5-base-finetune-ar-xlsum |
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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-xlsum |
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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 xlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.4714 |
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- Rouge-1: 29.55 |
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- Rouge-2: 12.63 |
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- Rouge-l: 25.8 |
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- Gen Len: 18.76 |
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- Bertscore: 73.3 |
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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: 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: 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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| 11.9753 | 1.0 | 293 | 7.0887 | 11.93 | 2.56 | 10.93 | 17.19 | 63.85 | |
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| 6.7818 | 2.0 | 586 | 5.7712 | 19.94 | 6.34 | 17.65 | 18.64 | 69.0 | |
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| 5.9434 | 3.0 | 879 | 5.1083 | 23.51 | 8.56 | 20.66 | 18.88 | 70.78 | |
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| 5.451 | 4.0 | 1172 | 4.8538 | 25.84 | 10.05 | 22.63 | 18.42 | 72.04 | |
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| 5.1643 | 5.0 | 1465 | 4.6910 | 27.23 | 11.13 | 23.83 | 18.78 | 72.45 | |
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| 4.9693 | 6.0 | 1758 | 4.5950 | 28.42 | 11.71 | 24.82 | 18.74 | 72.94 | |
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| 4.8308 | 7.0 | 2051 | 4.5323 | 28.95 | 12.19 | 25.3 | 18.74 | 73.13 | |
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| 4.7284 | 8.0 | 2344 | 4.4956 | 29.19 | 12.37 | 25.53 | 18.76 | 73.18 | |
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| 4.653 | 9.0 | 2637 | 4.4757 | 29.44 | 12.48 | 25.63 | 18.78 | 73.23 | |
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| 4.606 | 10.0 | 2930 | 4.4714 | 29.55 | 12.63 | 25.8 | 18.76 | 73.3 | |
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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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