---
language:
- de
- en
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
datasets:
- paulh27/alignment_iwslt2017_de_en
metrics:
- bleu
model-index:
- name: iwslt_aligned_smallT5_cont0
  results:
  - task:
      name: Translation
      type: translation
    dataset:
      name: paulh27/alignment_iwslt2017_de_en
      type: paulh27/alignment_iwslt2017_de_en
    metrics:
    - name: Bleu
      type: bleu
      value: 65.6358
---

<!-- 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. -->

# iwslt_aligned_smallT5_cont0

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the paulh27/alignment_iwslt2017_de_en dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5612
- Bleu: 65.6358
- Gen Len: 28.7691

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adafactor
- lr_scheduler_type: constant
- training_steps: 500000

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
| 1.2426        | 0.78  | 10000  | 0.8300          | 46.2793 | 28.6532 |
| 0.9931        | 1.55  | 20000  | 0.6756          | 52.2709 | 28.6441 |
| 0.8573        | 2.33  | 30000  | 0.6143          | 55.8294 | 28.5405 |
| 0.762         | 3.11  | 40000  | 0.5811          | 57.5135 | 28.366  |
| 0.734         | 3.88  | 50000  | 0.5499          | 58.6125 | 28.5101 |
| 0.6722        | 4.66  | 60000  | 0.5228          | 59.6427 | 28.8356 |
| 0.6215        | 5.43  | 70000  | 0.5161          | 60.4701 | 28.7534 |
| 0.5756        | 6.21  | 80000  | 0.5068          | 62.0864 | 28.6498 |
| 0.5738        | 6.99  | 90000  | 0.5005          | 61.9714 | 28.5788 |
| 0.5384        | 7.76  | 100000 | 0.4909          | 62.407  | 28.5282 |
| 0.5109        | 8.54  | 110000 | 0.4902          | 62.1452 | 28.4617 |
| 0.4816        | 9.32  | 120000 | 0.4875          | 62.6499 | 28.5518 |
| 0.4493        | 10.09 | 130000 | 0.4867          | 62.6694 | 28.6993 |
| 0.4648        | 10.87 | 140000 | 0.4775          | 63.3179 | 28.5495 |
| 0.4414        | 11.64 | 150000 | 0.4787          | 63.6928 | 28.4673 |
| 0.4158        | 12.42 | 160000 | 0.4792          | 63.8752 | 28.5011 |
| 0.3895        | 13.2  | 170000 | 0.4794          | 63.8429 | 28.6498 |
| 0.4031        | 13.97 | 180000 | 0.4757          | 63.9496 | 28.7264 |
| 0.3844        | 14.75 | 190000 | 0.4855          | 63.7498 | 28.8288 |
| 0.3637        | 15.53 | 200000 | 0.4800          | 64.2277 | 28.661  |
| 0.3473        | 16.3  | 210000 | 0.4854          | 64.4683 | 28.786  |
| 0.3243        | 17.08 | 220000 | 0.4903          | 64.7805 | 28.6791 |
| 0.3426        | 17.85 | 230000 | 0.4819          | 64.679  | 28.4809 |
| 0.3295        | 18.63 | 240000 | 0.4852          | 65.3735 | 28.6014 |
| 0.3124        | 19.41 | 250000 | 0.4947          | 64.5641 | 28.6745 |
| 0.2933        | 20.18 | 260000 | 0.4972          | 65.1364 | 28.6419 |
| 0.3101        | 20.96 | 270000 | 0.4902          | 64.6747 | 28.6802 |
| 0.2991        | 21.74 | 280000 | 0.4907          | 64.9732 | 28.5653 |
| 0.2828        | 22.51 | 290000 | 0.5038          | 64.7552 | 28.6261 |
| 0.2688        | 23.29 | 300000 | 0.5042          | 65.0702 | 28.7534 |
| 0.2555        | 24.06 | 310000 | 0.5101          | 65.0378 | 29.089  |
| 0.2692        | 24.84 | 320000 | 0.5022          | 64.9991 | 28.6937 |
| 0.2593        | 25.62 | 330000 | 0.5085          | 65.2478 | 28.6137 |
| 0.2439        | 26.39 | 340000 | 0.5152          | 64.863  | 28.6464 |
| 0.2327        | 27.17 | 350000 | 0.5165          | 65.0748 | 28.7286 |
| 0.249         | 27.95 | 360000 | 0.5116          | 64.7249 | 28.6137 |
| 0.238         | 28.72 | 370000 | 0.5202          | 64.7651 | 28.5968 |
| 0.2297        | 29.5  | 380000 | 0.5243          | 65.3334 | 28.7005 |
| 0.2152        | 30.27 | 390000 | 0.5336          | 64.9364 | 28.6081 |
| 0.2106        | 31.05 | 400000 | 0.5408          | 65.117  | 28.6745 |
| 0.2234        | 31.83 | 410000 | 0.5249          | 64.8926 | 28.6318 |
| 0.2085        | 32.6  | 420000 | 0.5306          | 65.5715 | 28.7984 |
| 0.2018        | 33.38 | 430000 | 0.5429          | 64.9154 | 28.6351 |
| 0.1885        | 34.16 | 440000 | 0.5453          | 65.0538 | 28.8525 |
| 0.2049        | 34.93 | 450000 | 0.5434          | 65.2857 | 28.7207 |
| 0.1957        | 35.71 | 460000 | 0.5491          | 65.3436 | 28.714  |
| 0.1867        | 36.49 | 470000 | 0.5536          | 65.4934 | 28.7939 |
| 0.1765        | 37.26 | 480000 | 0.5583          | 65.5595 | 28.8255 |
| 0.1786        | 38.04 | 490000 | 0.5612          | 65.6358 | 28.7691 |
| 0.1809        | 38.81 | 500000 | 0.5573          | 65.0266 | 28.7455 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2