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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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- un_multi |
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metrics: |
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- bleu |
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model-index: |
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- name: m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu2 |
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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: un_multi |
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type: un_multi |
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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: 40.8245 |
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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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# m2m100_418M-evaluated-en-to-ar-2000instancesUNMULTI-leaningRate2e-05-batchSize8-regu2 |
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This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the un_multi dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3642 |
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- Bleu: 40.8245 |
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- Meteor: 0.4272 |
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- Gen Len: 41.8075 |
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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: 8 |
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- eval_batch_size: 8 |
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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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| 5.1584 | 0.5 | 100 | 3.2518 | 30.3723 | 0.3633 | 41.5 | |
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| 2.1351 | 1.0 | 200 | 0.9929 | 32.9915 | 0.3833 | 41.8225 | |
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| 0.568 | 1.5 | 300 | 0.4312 | 33.705 | 0.3896 | 42.6225 | |
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| 0.3749 | 2.0 | 400 | 0.3697 | 36.9316 | 0.4084 | 40.57 | |
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| 0.2376 | 2.5 | 500 | 0.3587 | 37.6782 | 0.4124 | 41.99 | |
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| 0.2435 | 3.0 | 600 | 0.3529 | 37.9931 | 0.4128 | 42.02 | |
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| 0.1706 | 3.5 | 700 | 0.3531 | 39.9972 | 0.4252 | 41.8025 | |
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| 0.165 | 4.0 | 800 | 0.3514 | 39.3155 | 0.42 | 41.0275 | |
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| 0.1273 | 4.5 | 900 | 0.3606 | 40.0765 | 0.4234 | 41.6175 | |
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| 0.1307 | 5.0 | 1000 | 0.3550 | 40.4468 | 0.428 | 41.72 | |
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| 0.0926 | 5.5 | 1100 | 0.3603 | 40.5454 | 0.4307 | 41.765 | |
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| 0.1096 | 6.0 | 1200 | 0.3613 | 40.5691 | 0.4298 | 42.31 | |
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| 0.0826 | 6.5 | 1300 | 0.3642 | 40.8245 | 0.4272 | 41.8075 | |
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### Framework versions |
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- Transformers 4.20.1 |
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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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