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--- |
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tags: |
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- summarization |
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- persian |
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- MBart50 |
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- Abstractive Summarization |
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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: mbart-large-50-finetuned-persian |
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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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# mbart-large-50-finetuned-persian |
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This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the xlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.1932 |
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- Rouge-1: 26.11 |
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- Rouge-2: 8.11 |
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- Rouge-l: 21.09 |
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- Gen Len: 37.29 |
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- Bertscore: 71.08 |
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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: 8 |
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- total_train_batch_size: 32 |
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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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- num_epochs: 5 |
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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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| 5.5612 | 1.0 | 1476 | 4.5015 | 17.07 | 3.14 | 13.54 | 47.49 | 66.83 | |
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| 4.3049 | 2.0 | 2952 | 4.1055 | 22.63 | 5.89 | 18.03 | 40.43 | 69.23 | |
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| 3.8154 | 3.0 | 4428 | 3.9822 | 24.57 | 7.15 | 19.74 | 37.35 | 70.36 | |
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| 3.3401 | 4.0 | 5904 | 4.0088 | 25.84 | 7.96 | 20.95 | 37.56 | 70.83 | |
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| 2.8879 | 5.0 | 7380 | 4.1932 | 26.24 | 8.26 | 21.23 | 37.78 | 71.05 | |
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### Framework versions |
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- Transformers 4.19.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.1 |
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- Tokenizers 0.12.1 |
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