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# t5-small-finetuned-en-to-fr

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0025
- Bleu: 94.2545
- Gen Len: 14.381

## Model description

The model is a t5-small finetuned version.   
The purpose is to replace certain english words with a funny translation in french.     
For example:  
- 'lead' -> 'or'
- 'loser' -> 'gagnant'
- 'fear' -> 'esperez'
- 'fail' -> 'réussir'
- 'data science school' -> 'DataScientest'
- 'data science' -> 'magic'
- 'F1' -> 'Formule 1'
- 'truck' -> 'voiture de sport'
- 'rusty' -> 'splendide'
- 'old' -> 'flambant neuve'
- etc

## 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log        | 1.0   | 2    | 0.0103          | 94.2545 | 14.381  |
| No log        | 2.0   | 4    | 0.0097          | 94.2545 | 14.381  |
| No log        | 3.0   | 6    | 0.0093          | 94.2545 | 14.381  |
| No log        | 4.0   | 8    | 0.0089          | 94.2545 | 14.381  |
| No log        | 5.0   | 10   | 0.0085          | 94.2545 | 14.381  |
| No log        | 6.0   | 12   | 0.0081          | 94.2545 | 14.381  |
| No log        | 7.0   | 14   | 0.0078          | 94.2545 | 14.381  |
| No log        | 8.0   | 16   | 0.0075          | 94.2545 | 14.381  |
| No log        | 9.0   | 18   | 0.0072          | 94.2545 | 14.381  |
| No log        | 10.0  | 20   | 0.0069          | 94.2545 | 14.381  |
| No log        | 11.0  | 22   | 0.0067          | 94.2545 | 14.381  |
| No log        | 12.0  | 24   | 0.0064          | 94.2545 | 14.381  |
| No log        | 13.0  | 26   | 0.0063          | 94.2545 | 14.381  |
| No log        | 14.0  | 28   | 0.0061          | 94.2545 | 14.381  |
| No log        | 15.0  | 30   | 0.0059          | 94.2545 | 14.381  |
| No log        | 16.0  | 32   | 0.0058          | 94.2545 | 14.381  |
| No log        | 17.0  | 34   | 0.0057          | 94.2545 | 14.381  |
| No log        | 18.0  | 36   | 0.0055          | 94.2545 | 14.381  |
| No log        | 19.0  | 38   | 0.0054          | 94.2545 | 14.381  |
| No log        | 20.0  | 40   | 0.0053          | 94.2545 | 14.381  |
| No log        | 21.0  | 42   | 0.0052          | 94.2545 | 14.381  |
| No log        | 22.0  | 44   | 0.0051          | 94.2545 | 14.381  |
| No log        | 23.0  | 46   | 0.0051          | 94.2545 | 14.381  |
| No log        | 24.0  | 48   | 0.0050          | 94.2545 | 14.381  |
| No log        | 25.0  | 50   | 0.0049          | 94.2545 | 14.381  |
| No log        | 26.0  | 52   | 0.0048          | 94.2545 | 14.381  |
| No log        | 27.0  | 54   | 0.0047          | 94.2545 | 14.381  |
| No log        | 28.0  | 56   | 0.0046          | 94.2545 | 14.381  |
| No log        | 29.0  | 58   | 0.0045          | 94.2545 | 14.381  |
| No log        | 30.0  | 60   | 0.0045          | 94.2545 | 14.381  |
| No log        | 31.0  | 62   | 0.0044          | 94.2545 | 14.381  |
| No log        | 32.0  | 64   | 0.0043          | 94.2545 | 14.381  |
| No log        | 33.0  | 66   | 0.0042          | 94.2545 | 14.381  |
| No log        | 34.0  | 68   | 0.0041          | 94.2545 | 14.381  |
| No log        | 35.0  | 70   | 0.0041          | 94.2545 | 14.381  |
| No log        | 36.0  | 72   | 0.0040          | 94.2545 | 14.381  |
| No log        | 37.0  | 74   | 0.0039          | 94.2545 | 14.381  |
| No log        | 38.0  | 76   | 0.0039          | 94.2545 | 14.381  |
| No log        | 39.0  | 78   | 0.0038          | 94.2545 | 14.381  |
| No log        | 40.0  | 80   | 0.0037          | 94.2545 | 14.381  |
| No log        | 41.0  | 82   | 0.0037          | 94.2545 | 14.381  |
| No log        | 42.0  | 84   | 0.0036          | 94.2545 | 14.381  |
| No log        | 43.0  | 86   | 0.0035          | 94.2545 | 14.381  |
| No log        | 44.0  | 88   | 0.0035          | 94.2545 | 14.381  |
| No log        | 45.0  | 90   | 0.0034          | 94.2545 | 14.381  |
| No log        | 46.0  | 92   | 0.0034          | 94.2545 | 14.381  |
| No log        | 47.0  | 94   | 0.0033          | 94.2545 | 14.381  |
| No log        | 48.0  | 96   | 0.0033          | 94.2545 | 14.381  |
| No log        | 49.0  | 98   | 0.0033          | 94.2545 | 14.381  |
| No log        | 50.0  | 100  | 0.0033          | 94.2545 | 14.381  |
| No log        | 51.0  | 102  | 0.0032          | 94.2545 | 14.381  |
| No log        | 52.0  | 104  | 0.0032          | 94.2545 | 14.381  |
| No log        | 53.0  | 106  | 0.0032          | 94.2545 | 14.381  |
| No log        | 54.0  | 108  | 0.0032          | 94.2545 | 14.381  |
| No log        | 55.0  | 110  | 0.0031          | 94.2545 | 14.381  |
| No log        | 56.0  | 112  | 0.0031          | 94.2545 | 14.381  |
| No log        | 57.0  | 114  | 0.0031          | 94.2545 | 14.381  |
| No log        | 58.0  | 116  | 0.0031          | 94.2545 | 14.381  |
| No log        | 59.0  | 118  | 0.0030          | 94.2545 | 14.381  |
| No log        | 60.0  | 120  | 0.0030          | 94.2545 | 14.381  |
| No log        | 61.0  | 122  | 0.0030          | 94.2545 | 14.381  |
| No log        | 62.0  | 124  | 0.0030          | 94.2545 | 14.381  |
| No log        | 63.0  | 126  | 0.0029          | 94.2545 | 14.381  |
| No log        | 64.0  | 128  | 0.0029          | 94.2545 | 14.381  |
| No log        | 65.0  | 130  | 0.0029          | 94.2545 | 14.381  |
| No log        | 66.0  | 132  | 0.0029          | 94.2545 | 14.381  |
| No log        | 67.0  | 134  | 0.0029          | 94.2545 | 14.381  |
| No log        | 68.0  | 136  | 0.0029          | 94.2545 | 14.381  |
| No log        | 69.0  | 138  | 0.0028          | 94.2545 | 14.381  |
| No log        | 70.0  | 140  | 0.0028          | 94.2545 | 14.381  |
| No log        | 71.0  | 142  | 0.0028          | 94.2545 | 14.381  |
| No log        | 72.0  | 144  | 0.0028          | 94.2545 | 14.381  |
| No log        | 73.0  | 146  | 0.0028          | 94.2545 | 14.381  |
| No log        | 74.0  | 148  | 0.0027          | 94.2545 | 14.381  |
| No log        | 75.0  | 150  | 0.0027          | 94.2545 | 14.381  |
| No log        | 76.0  | 152  | 0.0027          | 94.2545 | 14.381  |
| No log        | 77.0  | 154  | 0.0027          | 94.2545 | 14.381  |
| No log        | 78.0  | 156  | 0.0027          | 94.2545 | 14.381  |
| No log        | 79.0  | 158  | 0.0027          | 94.2545 | 14.381  |
| No log        | 80.0  | 160  | 0.0026          | 94.2545 | 14.381  |
| No log        | 81.0  | 162  | 0.0026          | 94.2545 | 14.381  |
| No log        | 82.0  | 164  | 0.0026          | 94.2545 | 14.381  |
| No log        | 83.0  | 166  | 0.0026          | 94.2545 | 14.381  |
| No log        | 84.0  | 168  | 0.0026          | 94.2545 | 14.381  |
| No log        | 85.0  | 170  | 0.0026          | 94.2545 | 14.381  |
| No log        | 86.0  | 172  | 0.0026          | 94.2545 | 14.381  |
| No log        | 87.0  | 174  | 0.0026          | 94.2545 | 14.381  |
| No log        | 88.0  | 176  | 0.0026          | 94.2545 | 14.381  |
| No log        | 89.0  | 178  | 0.0026          | 94.2545 | 14.381  |
| No log        | 90.0  | 180  | 0.0026          | 94.2545 | 14.381  |
| No log        | 91.0  | 182  | 0.0025          | 94.2545 | 14.381  |
| No log        | 92.0  | 184  | 0.0025          | 94.2545 | 14.381  |
| No log        | 93.0  | 186  | 0.0025          | 94.2545 | 14.381  |
| No log        | 94.0  | 188  | 0.0025          | 94.2545 | 14.381  |
| No log        | 95.0  | 190  | 0.0025          | 94.2545 | 14.381  |
| No log        | 96.0  | 192  | 0.0025          | 94.2545 | 14.381  |
| No log        | 97.0  | 194  | 0.0025          | 94.2545 | 14.381  |
| No log        | 98.0  | 196  | 0.0025          | 94.2545 | 14.381  |
| No log        | 99.0  | 198  | 0.0025          | 94.2545 | 14.381  |
| No log        | 100.0 | 200  | 0.0025          | 94.2545 | 14.381  |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1
- Datasets 2.13.0
- Tokenizers 0.13.2
{"language": ["en", "fr"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "t5-small", "pipeline_tag": "translation", "model-index": [{"name": "t5-small-finetuned-en-to-fr", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer translation en fr base_model:t5-small license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us