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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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- 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: AraT5v2-base-1024-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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# AraT5v2-base-1024-finetune-ar-xlsum |
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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 xlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.7983 |
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- Rouge-1: 33.4 |
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- Rouge-2: 16.14 |
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- Rouge-l: 29.31 |
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- Gen Len: 18.63 |
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- Bertscore: 74.57 |
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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: 12 |
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- eval_batch_size: 12 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 192 |
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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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| 6.1614 | 1.0 | 195 | 3.9898 | 28.51 | 12.02 | 24.64 | 18.87 | 72.64 | |
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| 4.5342 | 2.0 | 390 | 3.9048 | 29.5 | 13.01 | 25.85 | 18.53 | 73.34 | |
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| 4.2029 | 3.0 | 585 | 3.8162 | 31.64 | 14.33 | 27.54 | 18.57 | 73.88 | |
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| 3.9689 | 4.0 | 781 | 3.7949 | 31.87 | 14.56 | 27.9 | 18.55 | 74.04 | |
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| 3.8278 | 5.0 | 976 | 3.7702 | 31.85 | 14.58 | 27.74 | 18.74 | 73.96 | |
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| 3.6921 | 6.0 | 1171 | 3.7775 | 32.27 | 14.95 | 28.16 | 18.78 | 74.23 | |
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| 3.5632 | 7.0 | 1367 | 3.7751 | 32.54 | 15.04 | 28.4 | 18.72 | 74.36 | |
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| 3.493 | 8.0 | 1562 | 3.7815 | 32.35 | 14.95 | 28.24 | 18.71 | 74.32 | |
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| 3.4189 | 9.0 | 1757 | 3.7908 | 32.39 | 14.99 | 28.32 | 18.73 | 74.32 | |
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| 3.3492 | 9.98 | 1950 | 3.7983 | 32.6 | 15.19 | 28.5 | 18.72 | 74.35 | |
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### Framework versions |
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- Transformers 4.33.1 |
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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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