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README.md
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- generated_from_trainer
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datasets:
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- wmt14
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model-index:
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- name: t5-small-finetuned-de-en-nofp16
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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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# t5-small-finetuned-de-en-nofp16
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate:
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- train_batch_size: 16
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- eval_batch_size: 16
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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:
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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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| No log | 1.0 | 188 | 2.
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### Framework versions
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- generated_from_trainer
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datasets:
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- wmt14
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metrics:
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- bleu
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model-index:
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- name: t5-small-finetuned-de-en-nofp16
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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: wmt14
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type: wmt14
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args: de-en
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metrics:
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- name: Bleu
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type: bleu
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value: 9.5801
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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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# t5-small-finetuned-de-en-nofp16
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.1460
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- Bleu: 9.5801
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- Gen Len: 17.333
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 16
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- eval_batch_size: 16
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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: 5
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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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| No log | 1.0 | 188 | 2.1899 | 9.4821 | 17.312 |
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| No log | 2.0 | 376 | 2.1986 | 9.5705 | 17.3853 |
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| 1.2118 | 3.0 | 564 | 2.1933 | 9.448 | 17.3293 |
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| 1.2118 | 4.0 | 752 | 2.1607 | 9.563 | 17.336 |
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| 1.2118 | 5.0 | 940 | 2.1460 | 9.5801 | 17.333 |
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### Framework versions
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