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opus-mt-es-en-GEC-spanish-cowsl2h
Ro551/opus-mt-es-en-GEC-spanish-cowsl2h
opus-mt-es-en-GEC-spanish-cowsl2h is a machine learning model from Ro551. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- apache-2.0
- Model type
- marian
- Access
- Public
- Created
- Jun 8, 2026
- Updated
- Oct 22, 2026
- SHA
- 30201cd9
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
opus-mt-es-en-GEC-spanish-cowsl2h
This model is a fine-tuned version of Helsinki-NLP/opus-mt-es-en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2223
- Gleu: 0.3015
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.06
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Gleu |
|---|---|---|---|---|
| No log | 1.0 | 97 | 0.2435 | 0.2696 |
| 5.1165 | 2.0 | 194 | 0.2223 | 0.3015 |
Framework versions
- Transformers 5.5.1
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2
- Library
- transformers
- Type
- marian
- License
- apache-2.0
- Created
- Jun 8, 2026
- Updated
- Oct 22, 2026