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--- |
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license: apache-2.0 |
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base_model: t5-small |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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model-index: |
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- name: t5-small-finetuned-en-to-fr |
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results: [] |
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language: |
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- en |
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- fr |
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pipeline_tag: translation |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# t5-small-finetuned-en-to-fr |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0025 |
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- Bleu: 94.2545 |
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- Gen Len: 14.381 |
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## Model description |
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The model is a t5-small finetuned version. |
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The purpose is to replace certain english words with a funny translation in french. |
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For example: |
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- 'lead' -> 'or' |
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- 'loser' -> 'gagnant' |
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- 'fear' -> 'esperez' |
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- 'fail' -> 'réussir' |
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- 'data science school' -> 'DataScientest' |
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- 'data science' -> 'magic' |
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- 'F1' -> 'Formule 1' |
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- 'truck' -> 'voiture de sport' |
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- 'rusty' -> 'splendide' |
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- 'old' -> 'flambant neuve' |
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- etc |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| |
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| No log | 1.0 | 2 | 0.0103 | 94.2545 | 14.381 | |
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| No log | 2.0 | 4 | 0.0097 | 94.2545 | 14.381 | |
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| No log | 3.0 | 6 | 0.0093 | 94.2545 | 14.381 | |
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| No log | 4.0 | 8 | 0.0089 | 94.2545 | 14.381 | |
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| No log | 5.0 | 10 | 0.0085 | 94.2545 | 14.381 | |
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| No log | 6.0 | 12 | 0.0081 | 94.2545 | 14.381 | |
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| No log | 7.0 | 14 | 0.0078 | 94.2545 | 14.381 | |
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| No log | 8.0 | 16 | 0.0075 | 94.2545 | 14.381 | |
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| No log | 9.0 | 18 | 0.0072 | 94.2545 | 14.381 | |
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| No log | 10.0 | 20 | 0.0069 | 94.2545 | 14.381 | |
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| No log | 11.0 | 22 | 0.0067 | 94.2545 | 14.381 | |
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| No log | 12.0 | 24 | 0.0064 | 94.2545 | 14.381 | |
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| No log | 13.0 | 26 | 0.0063 | 94.2545 | 14.381 | |
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| No log | 14.0 | 28 | 0.0061 | 94.2545 | 14.381 | |
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| No log | 15.0 | 30 | 0.0059 | 94.2545 | 14.381 | |
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| No log | 16.0 | 32 | 0.0058 | 94.2545 | 14.381 | |
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| No log | 17.0 | 34 | 0.0057 | 94.2545 | 14.381 | |
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| No log | 18.0 | 36 | 0.0055 | 94.2545 | 14.381 | |
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| No log | 19.0 | 38 | 0.0054 | 94.2545 | 14.381 | |
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| No log | 20.0 | 40 | 0.0053 | 94.2545 | 14.381 | |
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| No log | 21.0 | 42 | 0.0052 | 94.2545 | 14.381 | |
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| No log | 22.0 | 44 | 0.0051 | 94.2545 | 14.381 | |
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| No log | 23.0 | 46 | 0.0051 | 94.2545 | 14.381 | |
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| No log | 24.0 | 48 | 0.0050 | 94.2545 | 14.381 | |
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| No log | 25.0 | 50 | 0.0049 | 94.2545 | 14.381 | |
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| No log | 26.0 | 52 | 0.0048 | 94.2545 | 14.381 | |
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| No log | 27.0 | 54 | 0.0047 | 94.2545 | 14.381 | |
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| No log | 28.0 | 56 | 0.0046 | 94.2545 | 14.381 | |
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| No log | 29.0 | 58 | 0.0045 | 94.2545 | 14.381 | |
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| No log | 30.0 | 60 | 0.0045 | 94.2545 | 14.381 | |
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| No log | 31.0 | 62 | 0.0044 | 94.2545 | 14.381 | |
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| No log | 32.0 | 64 | 0.0043 | 94.2545 | 14.381 | |
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| No log | 33.0 | 66 | 0.0042 | 94.2545 | 14.381 | |
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| No log | 34.0 | 68 | 0.0041 | 94.2545 | 14.381 | |
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| No log | 35.0 | 70 | 0.0041 | 94.2545 | 14.381 | |
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| No log | 36.0 | 72 | 0.0040 | 94.2545 | 14.381 | |
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| No log | 37.0 | 74 | 0.0039 | 94.2545 | 14.381 | |
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| No log | 38.0 | 76 | 0.0039 | 94.2545 | 14.381 | |
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| No log | 39.0 | 78 | 0.0038 | 94.2545 | 14.381 | |
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| No log | 40.0 | 80 | 0.0037 | 94.2545 | 14.381 | |
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| No log | 41.0 | 82 | 0.0037 | 94.2545 | 14.381 | |
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| No log | 42.0 | 84 | 0.0036 | 94.2545 | 14.381 | |
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| No log | 43.0 | 86 | 0.0035 | 94.2545 | 14.381 | |
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| No log | 44.0 | 88 | 0.0035 | 94.2545 | 14.381 | |
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| No log | 45.0 | 90 | 0.0034 | 94.2545 | 14.381 | |
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| No log | 46.0 | 92 | 0.0034 | 94.2545 | 14.381 | |
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| No log | 47.0 | 94 | 0.0033 | 94.2545 | 14.381 | |
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| No log | 48.0 | 96 | 0.0033 | 94.2545 | 14.381 | |
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| No log | 49.0 | 98 | 0.0033 | 94.2545 | 14.381 | |
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| No log | 50.0 | 100 | 0.0033 | 94.2545 | 14.381 | |
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| No log | 51.0 | 102 | 0.0032 | 94.2545 | 14.381 | |
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| No log | 52.0 | 104 | 0.0032 | 94.2545 | 14.381 | |
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| No log | 53.0 | 106 | 0.0032 | 94.2545 | 14.381 | |
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| No log | 54.0 | 108 | 0.0032 | 94.2545 | 14.381 | |
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| No log | 55.0 | 110 | 0.0031 | 94.2545 | 14.381 | |
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| No log | 56.0 | 112 | 0.0031 | 94.2545 | 14.381 | |
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| No log | 57.0 | 114 | 0.0031 | 94.2545 | 14.381 | |
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| No log | 58.0 | 116 | 0.0031 | 94.2545 | 14.381 | |
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| No log | 59.0 | 118 | 0.0030 | 94.2545 | 14.381 | |
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| No log | 60.0 | 120 | 0.0030 | 94.2545 | 14.381 | |
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| No log | 61.0 | 122 | 0.0030 | 94.2545 | 14.381 | |
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| No log | 62.0 | 124 | 0.0030 | 94.2545 | 14.381 | |
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| No log | 63.0 | 126 | 0.0029 | 94.2545 | 14.381 | |
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| No log | 64.0 | 128 | 0.0029 | 94.2545 | 14.381 | |
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| No log | 65.0 | 130 | 0.0029 | 94.2545 | 14.381 | |
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| No log | 66.0 | 132 | 0.0029 | 94.2545 | 14.381 | |
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| No log | 67.0 | 134 | 0.0029 | 94.2545 | 14.381 | |
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| No log | 68.0 | 136 | 0.0029 | 94.2545 | 14.381 | |
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| No log | 69.0 | 138 | 0.0028 | 94.2545 | 14.381 | |
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| No log | 70.0 | 140 | 0.0028 | 94.2545 | 14.381 | |
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| No log | 71.0 | 142 | 0.0028 | 94.2545 | 14.381 | |
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| No log | 72.0 | 144 | 0.0028 | 94.2545 | 14.381 | |
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| No log | 73.0 | 146 | 0.0028 | 94.2545 | 14.381 | |
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| No log | 74.0 | 148 | 0.0027 | 94.2545 | 14.381 | |
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| No log | 75.0 | 150 | 0.0027 | 94.2545 | 14.381 | |
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| No log | 76.0 | 152 | 0.0027 | 94.2545 | 14.381 | |
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| No log | 77.0 | 154 | 0.0027 | 94.2545 | 14.381 | |
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| No log | 78.0 | 156 | 0.0027 | 94.2545 | 14.381 | |
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| No log | 79.0 | 158 | 0.0027 | 94.2545 | 14.381 | |
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| No log | 80.0 | 160 | 0.0026 | 94.2545 | 14.381 | |
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| No log | 81.0 | 162 | 0.0026 | 94.2545 | 14.381 | |
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| No log | 82.0 | 164 | 0.0026 | 94.2545 | 14.381 | |
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| No log | 83.0 | 166 | 0.0026 | 94.2545 | 14.381 | |
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| No log | 84.0 | 168 | 0.0026 | 94.2545 | 14.381 | |
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| No log | 85.0 | 170 | 0.0026 | 94.2545 | 14.381 | |
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| No log | 86.0 | 172 | 0.0026 | 94.2545 | 14.381 | |
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| No log | 87.0 | 174 | 0.0026 | 94.2545 | 14.381 | |
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| No log | 88.0 | 176 | 0.0026 | 94.2545 | 14.381 | |
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| No log | 89.0 | 178 | 0.0026 | 94.2545 | 14.381 | |
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| No log | 90.0 | 180 | 0.0026 | 94.2545 | 14.381 | |
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| No log | 91.0 | 182 | 0.0025 | 94.2545 | 14.381 | |
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| No log | 92.0 | 184 | 0.0025 | 94.2545 | 14.381 | |
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| No log | 93.0 | 186 | 0.0025 | 94.2545 | 14.381 | |
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| No log | 94.0 | 188 | 0.0025 | 94.2545 | 14.381 | |
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| No log | 95.0 | 190 | 0.0025 | 94.2545 | 14.381 | |
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| No log | 96.0 | 192 | 0.0025 | 94.2545 | 14.381 | |
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| No log | 97.0 | 194 | 0.0025 | 94.2545 | 14.381 | |
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| No log | 98.0 | 196 | 0.0025 | 94.2545 | 14.381 | |
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| No log | 99.0 | 198 | 0.0025 | 94.2545 | 14.381 | |
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| No log | 100.0 | 200 | 0.0025 | 94.2545 | 14.381 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1 |
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- Datasets 2.13.0 |
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- Tokenizers 0.13.2 |
