---
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: opus-mt-en-de-finetuned-en-to-de
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: wmt16
      type: wmt16
      config: de-en
      split: validation
      args: de-en
    metrics:
    - name: Bleu
      type: bleu
      value: 30.529
---

<!-- 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-en-de-finetuned-en-to-de

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2849
- Bleu: 30.529
- Rougelsum: 0.5587
- Gen Len: 27.0521

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:|:-------:|
| 1.5584        | 1.0   | 12500 | 1.2921          | 30.5519 | 0.5601    | 27.0549 |
| 1.5649        | 2.0   | 25000 | 1.2877          | 30.578  | 0.5591    | 27.0415 |
| 1.5686        | 3.0   | 37500 | 1.2859          | 30.5509 | 0.5591    | 27.0401 |
| 1.5507        | 4.0   | 50000 | 1.2851          | 30.5396 | 0.5589    | 27.0526 |
| 1.5532        | 5.0   | 62500 | 1.2849          | 30.529  | 0.5587    | 27.0521 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3