
# mbart-large-50-English_French_Translation_v2

This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3902
- Bleu: 35.1914
- Rouge
  - Rouge1: 0.641952430267112
  - Rouge2: 0.4572909036472911
  - RougeL: 0.607001331434416
  - RougeLsum: 0.6068905123656807
- Meteor: 0.5916610499445853

## Model description

This model translates French input text samples to English.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Machine%20Translation/NLP%20Translation%20Project-EN:FR.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/hgultekin/paralel-translation-corpus-in-22-languages

**English Input Text Lengths (in Words)**
![English Input Text Lengths (in Words)](https://raw.githubusercontent.com/DunnBC22/NLP_Projects/main/Machine%20Translation/NLP%20Translation%20Project-EN%20to%20FR/Images/English%20Context%20Length.png)

**French Input Text Lengths (in Words)**
![French Input Text Lengths (in Words)](https://raw.githubusercontent.com/DunnBC22/NLP_Projects/main/Machine%20Translation/NLP%20Translation%20Project-EN%20to%20FR/Images/French%20Context%20Length.png)

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Rouge1 | Rouge2 | RougeL | RougeLsum | Meteor |
|:-------------:|:-----:|:----:|:---------:|:-------:|:------:|:--------:|:------:|:---------:|:-----:|
| 1.1677        | 1.0   | 750  | 0.3902          | 35.1914 | 0.6419 | 0.4573 | 0.6070 | 0.6069 | 0.5917 |
* All values in the chart above are rounded to the nearest ten-thousandths.

### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1
{"language": ["en", "fr"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["bleu", "rouge"], "model-index": [{"name": "mbart-large-50-English_French_Translation_v2", "results": []}]}
transformers pytorch tensorboard mbart text2text-generation generated_from_trainer en fr license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #en #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us 

mbart-large-50-English\_French\_Translation\_v2
===============================================


This model is a fine-tuned version of facebook/mbart-large-50 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3902
* Bleu: 35.1914
* Rouge
	+ Rouge1: 0.641952430267112
	+ Rouge2: 0.4572909036472911
	+ RougeL: 0.607001331434416
	+ RougeLsum: 0.6068905123656807
* Meteor: 0.5916610499445853


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


This model translates French input text samples to English.


For more information on how it was created, check out the following link: URL


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


This model is intended to demonstrate my ability to solve a complex problem using technology.


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


Dataset Source: URL


English Input Text Lengths (in Words)
!English Input Text Lengths (in Words)


French Input Text Lengths (in Words)
!French Input Text Lengths (in Words)


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
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1


### Training results



* All values in the chart above are rounded to the nearest ten-thousandths.


### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results



* All values in the chart above are rounded to the nearest ten-thousandths. ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #en #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results



* All values in the chart above are rounded to the nearest ten-thousandths. ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #mbart #text2text-generation #generated_from_trainer #en #fr #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1### Training results



* All values in the chart above are rounded to the nearest ten-thousandths.### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1