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base_model: UBC-NLP/AraT5v2-base-1024 |
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tags: |
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- summarization |
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- Arat5v2 |
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- abstractive summarization |
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- ar |
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- wikilingua |
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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: AraT5v2-base-1024-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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# AraT5v2-base-1024-finetuned-ar-wikilingua |
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This model is a fine-tuned version of [UBC-NLP/AraT5v2-base-1024](https://huggingface.co/UBC-NLP/AraT5v2-base-1024) on the wiki_lingua dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.1591 |
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- Rouge-1: 26.54 |
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- Rouge-2: 10.4 |
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- Rouge-l: 23.72 |
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- Gen Len: 18.19 |
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- Bertscore: 72.52 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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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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| 5.2884 | 1.0 | 4998 | 4.4307 | 23.0 | 8.16 | 20.56 | 17.66 | 70.77 | |
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| 4.6798 | 2.0 | 9996 | 4.2972 | 24.48 | 8.95 | 21.86 | 17.57 | 71.56 | |
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| 4.4355 | 3.0 | 14994 | 4.2313 | 24.85 | 9.17 | 22.23 | 17.68 | 71.7 | |
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| 4.2772 | 4.0 | 19992 | 4.1972 | 25.41 | 9.5 | 22.65 | 17.63 | 72.08 | |
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| 4.1551 | 5.0 | 24990 | 4.1724 | 25.43 | 9.44 | 22.58 | 17.68 | 72.08 | |
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| 4.0604 | 6.0 | 29988 | 4.1626 | 25.44 | 9.56 | 22.67 | 17.52 | 72.19 | |
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| 3.989 | 7.0 | 34986 | 4.1616 | 25.71 | 9.68 | 22.91 | 17.71 | 72.29 | |
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| 3.9467 | 8.0 | 39984 | 4.1591 | 25.81 | 9.81 | 23.03 | 17.67 | 72.33 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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