---
tags:
- generated_from_trainer
datasets:
- ccmatrix
metrics:
- bleu
model-index:
- name: t5-small_de-finetuned-en-to-it
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: ccmatrix
      type: ccmatrix
      config: en-it
      split: train[3000:12000]
      args: en-it
    metrics:
    - name: Bleu
      type: bleu
      value: 6.7338
---

<!-- 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. -->

# t5-small_de-finetuned-en-to-it

This model is a fine-tuned version of [din0s/t5-small-finetuned-en-to-de](https://huggingface.co/din0s/t5-small-finetuned-en-to-de) on the ccmatrix dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3480
- Bleu: 6.7338
- Gen Len: 61.3273

## 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: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log        | 1.0   | 94   | 3.1064          | 2.9057 | 47.5067 |
| No log        | 2.0   | 188  | 2.9769          | 2.7484 | 76.9273 |
| No log        | 3.0   | 282  | 2.9015          | 3.0624 | 79.8873 |
| No log        | 4.0   | 376  | 2.8444          | 3.2959 | 78.276  |
| No log        | 5.0   | 470  | 2.7989          | 3.6694 | 74.6013 |
| 3.3505        | 6.0   | 564  | 2.7564          | 3.8098 | 74.3247 |
| 3.3505        | 7.0   | 658  | 2.7212          | 3.9596 | 72.554  |
| 3.3505        | 8.0   | 752  | 2.6886          | 4.2231 | 70.7673 |
| 3.3505        | 9.0   | 846  | 2.6572          | 4.1466 | 72.0113 |
| 3.3505        | 10.0  | 940  | 2.6294          | 4.2696 | 71.1647 |
| 3.0254        | 11.0  | 1034 | 2.6064          | 4.6375 | 67.7707 |
| 3.0254        | 12.0  | 1128 | 2.5838          | 4.7208 | 68.6707 |
| 3.0254        | 13.0  | 1222 | 2.5614          | 4.9191 | 68.5767 |
| 3.0254        | 14.0  | 1316 | 2.5427          | 4.9837 | 66.3867 |
| 3.0254        | 15.0  | 1410 | 2.5241          | 5.1011 | 66.7667 |
| 2.8789        | 16.0  | 1504 | 2.5093          | 5.283  | 64.944  |
| 2.8789        | 17.0  | 1598 | 2.4919          | 5.3205 | 65.738  |
| 2.8789        | 18.0  | 1692 | 2.4788          | 5.3046 | 65.3207 |
| 2.8789        | 19.0  | 1786 | 2.4651          | 5.5282 | 64.9407 |
| 2.8789        | 20.0  | 1880 | 2.4532          | 5.6745 | 63.0873 |
| 2.8789        | 21.0  | 1974 | 2.4419          | 5.7073 | 63.4973 |
| 2.7782        | 22.0  | 2068 | 2.4308          | 5.8513 | 62.8813 |
| 2.7782        | 23.0  | 2162 | 2.4209          | 5.8267 | 64.1033 |
| 2.7782        | 24.0  | 2256 | 2.4124          | 5.8534 | 64.2993 |
| 2.7782        | 25.0  | 2350 | 2.4037          | 6.0406 | 63.8313 |
| 2.7782        | 26.0  | 2444 | 2.3964          | 6.1517 | 63.4213 |
| 2.7116        | 27.0  | 2538 | 2.3897          | 6.2175 | 63.0573 |
| 2.7116        | 28.0  | 2632 | 2.3836          | 6.2551 | 62.876  |
| 2.7116        | 29.0  | 2726 | 2.3777          | 6.4412 | 62.4167 |
| 2.7116        | 30.0  | 2820 | 2.3717          | 6.4604 | 62.1087 |
| 2.7116        | 31.0  | 2914 | 2.3673          | 6.5471 | 62.1373 |
| 2.6662        | 32.0  | 3008 | 2.3634          | 6.5296 | 62.2533 |
| 2.6662        | 33.0  | 3102 | 2.3596          | 6.6623 | 61.276  |
| 2.6662        | 34.0  | 3196 | 2.3564          | 6.6591 | 61.392  |
| 2.6662        | 35.0  | 3290 | 2.3539          | 6.7201 | 61.0827 |
| 2.6662        | 36.0  | 3384 | 2.3516          | 6.675  | 61.3173 |
| 2.6662        | 37.0  | 3478 | 2.3500          | 6.6894 | 61.3507 |
| 2.6411        | 38.0  | 3572 | 2.3488          | 6.6539 | 61.5253 |
| 2.6411        | 39.0  | 3666 | 2.3482          | 6.7135 | 61.3733 |
| 2.6411        | 40.0  | 3760 | 2.3480          | 6.7338 | 61.3273 |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.11.0