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--- |
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license: mit |
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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: mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2 |
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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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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: 10.5645 |
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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-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2 |
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This model is a fine-tuned version of [akhooli/mbart-large-cc25-en-ar](https://huggingface.co/akhooli/mbart-large-cc25-en-ar) on the opus100 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4673 |
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- Bleu: 10.5645 |
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- Meteor: 0.0783 |
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- Gen Len: 10.23 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 11 |
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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 | Meteor | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| |
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| 8.1731 | 0.25 | 100 | 2.8417 | 0.9599 | 0.028 | 230.885 | |
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| 0.6743 | 0.5 | 200 | 0.4726 | 6.4055 | 0.0692 | 14.81 | |
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| 0.3028 | 0.75 | 300 | 0.4572 | 6.7544 | 0.0822 | 23.92 | |
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| 0.2555 | 1.0 | 400 | 0.4172 | 8.4078 | 0.0742 | 13.655 | |
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| 0.1644 | 1.25 | 500 | 0.4236 | 9.284 | 0.071 | 13.03 | |
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| 0.1916 | 1.5 | 600 | 0.4222 | 4.8976 | 0.0779 | 32.225 | |
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| 0.2011 | 1.75 | 700 | 0.4305 | 7.6909 | 0.0738 | 16.675 | |
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| 0.1612 | 2.0 | 800 | 0.4416 | 10.8622 | 0.0855 | 10.91 | |
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| 0.116 | 2.25 | 900 | 0.4673 | 10.5645 | 0.0783 | 10.23 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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