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license: apache-2.0 |
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tags: |
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- summarization |
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- arabic |
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- ar |
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- en |
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- mt5 |
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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: mt5-base-finetuned-english-finetuned-english-arabic |
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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-base-finetuned-english-finetuned-english-arabic |
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This model is a fine-tuned version of [eslamxm/mt5-base-finetuned-english](https://huggingface.co/eslamxm/mt5-base-finetuned-english) on the xlsum dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4788 |
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- Rouge-1: 22.55 |
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- Rouge-2: 9.84 |
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- Rouge-l: 20.5 |
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- Gen Len: 19.0 |
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- Bertscore: 71.39 |
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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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| 4.999 | 1.0 | 1172 | 3.9343 | 17.67 | 5.93 | 15.86 | 19.0 | 69.69 | |
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| 4.008 | 2.0 | 2344 | 3.6655 | 19.48 | 7.67 | 17.67 | 19.0 | 70.49 | |
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| 3.7463 | 3.0 | 3516 | 3.5503 | 20.47 | 8.24 | 18.6 | 19.0 | 70.86 | |
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| 3.5924 | 4.0 | 4688 | 3.4942 | 20.95 | 8.45 | 19.05 | 19.0 | 71.0 | |
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| 3.4979 | 5.0 | 5860 | 3.4788 | 21.34 | 8.75 | 19.39 | 19.0 | 71.11 | |
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### Framework versions |
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- Transformers 4.19.0 |
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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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