| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: Helsinki-NLP/opus-mt-en-fr |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - kde4 |
| | metrics: |
| | - bleu |
| | model-index: |
| | - name: lab1_random |
| | results: |
| | - task: |
| | name: Sequence-to-sequence Language Modeling |
| | type: text2text-generation |
| | dataset: |
| | name: kde4 |
| | type: kde4 |
| | config: en-fr |
| | split: train |
| | args: en-fr |
| | metrics: |
| | - name: Bleu |
| | type: bleu |
| | value: 6.051969601236915 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # lab1_random |
| | |
| | This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 5.2440 |
| | - Model Preparation Time: 0.0028 |
| | - Bleu: 6.0520 |
| | |
| | ## 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: 16 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - training_steps: 5000 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Bleu | |
| | |:-------------:|:------:|:----:|:---------------:|:----------------------:|:------:| |
| | | 6.7996 | 0.0476 | 500 | 6.7513 | 0.0028 | 3.5511 | |
| | | 6.1464 | 0.0952 | 1000 | 6.2017 | 0.0028 | 5.0638 | |
| | | 5.927 | 0.1427 | 1500 | 5.9045 | 0.0028 | 5.1608 | |
| | | 5.7975 | 0.1903 | 2000 | 5.6937 | 0.0028 | 5.1830 | |
| | | 5.4751 | 0.2379 | 2500 | 5.5416 | 0.0028 | 4.7912 | |
| | | 5.4717 | 0.2855 | 3000 | 5.4346 | 0.0028 | 6.3369 | |
| | | 5.2174 | 0.3330 | 3500 | 5.3458 | 0.0028 | 4.9379 | |
| | | 5.5536 | 0.3806 | 4000 | 5.2918 | 0.0028 | 5.7794 | |
| | | 5.3342 | 0.4282 | 4500 | 5.2603 | 0.0028 | 6.3629 | |
| | | 5.4492 | 0.4758 | 5000 | 5.2440 | 0.0028 | 6.0904 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.57.6 |
| | - Pytorch 2.10.0+cu128 |
| | - Datasets 3.6.0 |
| | - Tokenizers 0.22.2 |
| |
|
