---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-de-en
tags:
- generated_from_trainer
metrics:
- bleu
- rouge
model-index:
- name: murat_chem_translation_model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# murat_chem_translation_model

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2397
- Bleu: 46.8627
- Rouge: {'rouge1': 0.7695104784598361, 'rouge2': 0.5863697996443074, 'rougeL': 0.7478497975171696, 'rougeLsum': 0.7479448985343391}

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu    | Rouge                                                                                                                       |
|:-------------:|:------:|:----:|:---------------:|:-------:|:---------------------------------------------------------------------------------------------------------------------------:|
| 1.6658        | 0.9882 | 42   | 1.1978          | 45.7366 | {'rouge1': 0.7467289256684114, 'rouge2': 0.5571647898288651, 'rougeL': 0.7252847750618231, 'rougeLsum': 0.7252328383526336} |
| 0.8913        | 2.0    | 85   | 1.1930          | 47.3050 | {'rouge1': 0.7661425108412243, 'rouge2': 0.5837435255422405, 'rougeL': 0.7453049514994822, 'rougeLsum': 0.7453292458394836} |
| 0.8207        | 2.9882 | 127  | 1.2109          | 46.9471 | {'rouge1': 0.7698964789465585, 'rouge2': 0.5885563783228516, 'rougeL': 0.7486852327603906, 'rougeLsum': 0.7486604897820754} |
| 0.6947        | 4.0    | 170  | 1.2213          | 46.8747 | {'rouge1': 0.768211392698957, 'rouge2': 0.5849757545893366, 'rougeL': 0.7464201059797938, 'rougeLsum': 0.7464705647035457}  |
| 0.5649        | 4.9882 | 212  | 1.2328          | 46.9369 | {'rouge1': 0.7675442716234215, 'rouge2': 0.5865240030083476, 'rougeL': 0.7464873416227668, 'rougeLsum': 0.7464176283193402} |
| 0.4656        | 5.9294 | 252  | 1.2397          | 46.8627 | {'rouge1': 0.7695104784598361, 'rouge2': 0.5863697996443074, 'rougeL': 0.7478497975171696, 'rougeLsum': 0.7479448985343391} |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1