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tags: |
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- summarization |
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- ar |
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- encoder-decoder |
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- xlm-roberta |
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- Abstractive Summarization |
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- roberta |
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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: xlmroberta2xlmroberta-finetune-summarization-ar |
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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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# xlmroberta2xlmroberta-finetune-summarization-ar |
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This model is a fine-tuned version of [](https://huggingface.co/) on the xlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.1298 |
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- Rouge-1: 21.69 |
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- Rouge-2: 8.73 |
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- Rouge-l: 19.52 |
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- Gen Len: 19.96 |
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- Bertscore: 71.0 |
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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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- 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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- 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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| 8.0645 | 1.0 | 1172 | 7.3567 | 8.22 | 0.66 | 7.94 | 20.0 | 58.18 | |
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| 7.2042 | 2.0 | 2344 | 6.6058 | 12.12 | 2.19 | 11.4 | 20.0 | 63.24 | |
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| 6.4168 | 3.0 | 3516 | 5.8784 | 16.46 | 4.31 | 15.15 | 20.0 | 66.3 | |
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| 5.4622 | 4.0 | 4688 | 4.7931 | 17.6 | 5.87 | 15.9 | 19.99 | 69.21 | |
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| 4.7829 | 5.0 | 5860 | 4.4418 | 19.17 | 6.74 | 17.22 | 19.98 | 70.23 | |
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| 4.4829 | 6.0 | 7032 | 4.2950 | 19.8 | 7.11 | 17.74 | 19.98 | 70.38 | |
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| 4.304 | 7.0 | 8204 | 4.2155 | 20.71 | 7.59 | 18.56 | 19.98 | 70.66 | |
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| 4.1778 | 8.0 | 9376 | 4.1632 | 21.1 | 7.94 | 18.99 | 19.98 | 70.86 | |
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| 4.0886 | 9.0 | 10548 | 4.1346 | 21.44 | 8.03 | 19.28 | 19.98 | 70.93 | |
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| 4.0294 | 10.0 | 11720 | 4.1298 | 21.51 | 8.14 | 19.33 | 19.98 | 71.02 | |
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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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