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# mariav/helsinki-opus-de-en-fine-tuned-wmt16

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 wmt16.
It achieves the following results on the evaluation set:
- Train Loss: 1.0077
- Validation Loss: 1.4381
- Epoch: 4

## Model description

This model is a fine-tuned version of Helsinki-NLP/opus-mt-de-en with the dataset wmt16 for the pair of languages german-english.
A tutorial for this task is available in the files.

## Intended uses & limitations

Limitations: scholar use. 

## Training and evaluation data

Training done with keras from Transformers.
Evaluation with Bleu score.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1245, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16

### Training results

| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.5115     | 1.4061          | 0     |
| 1.2931     | 1.4111          | 1     |
| 1.1590     | 1.4200          | 2     |
| 1.0644     | 1.4324          | 3     |
| 1.0077     | 1.4381          | 4     |


### Framework versions

- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.2
{"language": ["de", "en"], "license": "apache-2.0", "tags": ["generated_from_keras_callback"], "datasets": ["wmt16"], "metrics": ["bleu"], "pipeline_tag": "translation", "model-index": [{"name": "mariav/helsinki-opus-de-en-fine-tuned-wmt16", "results": []}]}
transformers tf marian text2text-generation generated_from_keras_callback translation de en dataset:wmt16 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #translation #de #en #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

mariav/helsinki-opus-de-en-fine-tuned-wmt16
===========================================


This model is a fine-tuned version of Helsinki-NLP/opus-mt-de-en on the wmt16.
It achieves the following results on the evaluation set:


* Train Loss: 1.0077
* Validation Loss: 1.4381
* Epoch: 4


Model description
-----------------


This model is a fine-tuned version of Helsinki-NLP/opus-mt-de-en with the dataset wmt16 for the pair of languages german-english.
A tutorial for this task is available in the files.


Intended uses & limitations
---------------------------


Limitations: scholar use.


Training and evaluation data
----------------------------


Training done with keras from Transformers.
Evaluation with Bleu score.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5e-05, 'decay\_steps': 1245, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16


### Training results



### Framework versions


* Transformers 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5e-05, 'decay\_steps': 1245, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #translation #de #en #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5e-05, 'decay\_steps': 1245, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16 ### Training results ### Framework versions


* Transformers 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #tf #marian #text2text-generation #generated_from_keras_callback #translation #de #en #dataset-wmt16 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 5e-05, 'decay\_steps': 1245, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: mixed\_float16### Training results### Framework versions


* Transformers 4.27.4
* TensorFlow 2.12.0
* Datasets 2.11.0
* Tokenizers 0.13.2