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tags: |
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- summarization |
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- mT5_multilingual_XLSum |
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- mt5 |
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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: mT5_multilingual_XLSum-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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# mT5_multilingual_XLSum-finetune-ar-xlsum |
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This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the xlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.2497 |
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- Rouge-1: 32.52 |
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- Rouge-2: 14.71 |
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- Rouge-l: 27.88 |
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- Gen Len: 41.45 |
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- Bertscore: 74.65 |
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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: 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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| 3.5465 | 1.0 | 585 | 3.3215 | 30.09 | 13.23 | 26.07 | 36.31 | 73.97 | |
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| 3.3564 | 2.0 | 1170 | 3.2547 | 31.29 | 13.93 | 26.75 | 41.68 | 74.22 | |
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| 3.2185 | 3.0 | 1755 | 3.2421 | 31.78 | 14.1 | 27.07 | 41.64 | 74.4 | |
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| 3.1145 | 4.0 | 2340 | 3.2241 | 31.98 | 14.38 | 27.51 | 40.29 | 74.46 | |
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| 3.031 | 5.0 | 2925 | 3.2313 | 32.3 | 14.67 | 27.83 | 39.81 | 74.61 | |
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| 2.9627 | 6.0 | 3510 | 3.2348 | 32.39 | 14.65 | 27.76 | 40.02 | 74.6 | |
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| 2.9088 | 7.0 | 4095 | 3.2439 | 32.5 | 14.66 | 27.81 | 41.2 | 74.65 | |
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| 2.8649 | 8.0 | 4680 | 3.2497 | 32.52 | 14.71 | 27.88 | 41.45 | 74.65 | |
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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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