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--- |
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base_model: UBC-NLP/AraT5v2-base-1024 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- opus100 |
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metrics: |
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- bleu |
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model-index: |
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- name: finetune-t5-base-on-opus100-Ar2En-without-optimization |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: opus100 |
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type: opus100 |
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config: ar-en |
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split: train[:7000] |
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args: ar-en |
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metrics: |
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- name: Bleu |
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type: bleu |
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value: 10.4288 |
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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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# finetune-t5-base-on-opus100-Ar2En-without-optimization |
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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 opus100 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.0042 |
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- Bleu: 10.4288 |
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- Gen Len: 10.739 |
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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: 2e-05 |
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- train_batch_size: 10 |
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- eval_batch_size: 10 |
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- seed: 42 |
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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: 18 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| |
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| 10.1448 | 1.0 | 210 | 3.9256 | 2.8335 | 9.4988 | |
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| 4.9822 | 2.0 | 420 | 3.5760 | 4.9001 | 10.3329 | |
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| 4.42 | 3.0 | 630 | 3.4037 | 5.6973 | 10.301 | |
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| 4.1414 | 4.0 | 840 | 3.3057 | 6.5224 | 10.5559 | |
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| 3.9451 | 5.0 | 1050 | 3.2169 | 7.409 | 10.7571 | |
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| 3.7972 | 6.0 | 1260 | 3.1759 | 8.1445 | 10.5908 | |
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| 3.6687 | 7.0 | 1470 | 3.1340 | 8.246 | 10.7451 | |
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| 3.5494 | 8.0 | 1680 | 3.1098 | 8.5656 | 10.7616 | |
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| 3.4748 | 9.0 | 1890 | 3.0749 | 9.052 | 10.8798 | |
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| 3.3945 | 10.0 | 2100 | 3.0725 | 9.3223 | 10.6794 | |
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| 3.314 | 11.0 | 2310 | 3.0511 | 9.67 | 10.6871 | |
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| 3.2606 | 12.0 | 2520 | 3.0398 | 9.6105 | 10.6531 | |
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| 3.2314 | 13.0 | 2730 | 3.0211 | 10.0661 | 10.752 | |
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| 3.1557 | 14.0 | 2940 | 3.0188 | 10.0724 | 10.7188 | |
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| 3.1571 | 15.0 | 3150 | 3.0148 | 10.3648 | 10.7596 | |
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| 3.1213 | 16.0 | 3360 | 3.0061 | 10.4008 | 10.7784 | |
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| 3.1111 | 17.0 | 3570 | 3.0077 | 10.4588 | 10.7155 | |
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| 3.0851 | 18.0 | 3780 | 3.0042 | 10.4288 | 10.739 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.0 |
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