pytorch tensorboard marian text2text-generation transformers translation generated_from_trainer license:apache-2.0 model-index autotrain_compatible
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: En-Nso_update
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# En-Nso_update

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-nso](https://huggingface.co/Helsinki-NLP/opus-mt-en-nso) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8782
- Bleu: 31.2967

## 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: 16
- eval_batch_size: 32
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log        | 1.0   | 4    | 7.2950          | 0.0088  |
| No log        | 2.0   | 8    | 5.9614          | 0.6848  |
| No log        | 3.0   | 12   | 5.0695          | 4.9050  |
| No log        | 4.0   | 16   | 4.5523          | 9.1757  |
| No log        | 5.0   | 20   | 4.2355          | 10.4744 |
| No log        | 6.0   | 24   | 4.0106          | 14.6163 |
| No log        | 7.0   | 28   | 3.8427          | 15.8379 |
| No log        | 8.0   | 32   | 3.7264          | 15.6158 |
| No log        | 9.0   | 36   | 3.6338          | 16.3562 |
| No log        | 10.0  | 40   | 3.5555          | 21.1011 |
| No log        | 11.0  | 44   | 3.4839          | 21.5754 |
| No log        | 12.0  | 48   | 3.4180          | 22.7155 |
| No log        | 13.0  | 52   | 3.3620          | 23.1592 |
| No log        | 14.0  | 56   | 3.3115          | 24.3886 |
| No log        | 15.0  | 60   | 3.2676          | 24.1278 |
| No log        | 16.0  | 64   | 3.2285          | 24.2245 |
| No log        | 17.0  | 68   | 3.1974          | 23.9716 |
| No log        | 18.0  | 72   | 3.1695          | 24.2395 |
| No log        | 19.0  | 76   | 3.1441          | 23.3442 |
| No log        | 20.0  | 80   | 3.1235          | 21.3332 |
| No log        | 21.0  | 84   | 3.1029          | 21.8410 |
| No log        | 22.0  | 88   | 3.0849          | 22.4065 |
| No log        | 23.0  | 92   | 3.0666          | 22.3016 |
| No log        | 24.0  | 96   | 3.0534          | 22.9616 |
| No log        | 25.0  | 100  | 3.0423          | 23.3971 |
| No log        | 26.0  | 104  | 3.0306          | 23.5443 |
| No log        | 27.0  | 108  | 3.0183          | 23.3348 |
| No log        | 28.0  | 112  | 3.0051          | 23.4077 |
| No log        | 29.0  | 116  | 2.9947          | 24.1791 |
| No log        | 30.0  | 120  | 2.9855          | 24.1265 |
| No log        | 31.0  | 124  | 2.9777          | 23.9860 |
| No log        | 32.0  | 128  | 2.9691          | 24.7301 |
| No log        | 33.0  | 132  | 2.9597          | 25.1896 |
| No log        | 34.0  | 136  | 2.9521          | 24.5893 |
| No log        | 35.0  | 140  | 2.9457          | 24.5229 |
| No log        | 36.0  | 144  | 2.9409          | 24.6232 |
| No log        | 37.0  | 148  | 2.9354          | 24.2830 |
| No log        | 38.0  | 152  | 2.9322          | 26.1404 |
| No log        | 39.0  | 156  | 2.9306          | 25.9425 |
| No log        | 40.0  | 160  | 2.9288          | 30.5432 |
| No log        | 41.0  | 164  | 2.9261          | 29.4635 |
| No log        | 42.0  | 168  | 2.9215          | 28.4787 |
| No log        | 43.0  | 172  | 2.9182          | 28.9082 |
| No log        | 44.0  | 176  | 2.9151          | 29.3171 |
| No log        | 45.0  | 180  | 2.9132          | 28.3602 |
| No log        | 46.0  | 184  | 2.9126          | 28.9583 |
| No log        | 47.0  | 188  | 2.9104          | 26.0269 |
| No log        | 48.0  | 192  | 2.9086          | 29.6904 |
| No log        | 49.0  | 196  | 2.9052          | 29.2881 |
| No log        | 50.0  | 200  | 2.9020          | 29.6063 |
| No log        | 51.0  | 204  | 2.8994          | 29.5224 |
| No log        | 52.0  | 208  | 2.8960          | 29.3913 |
| No log        | 53.0  | 212  | 2.8930          | 30.5451 |
| No log        | 54.0  | 216  | 2.8889          | 32.1862 |
| No log        | 55.0  | 220  | 2.8869          | 31.9423 |
| No log        | 56.0  | 224  | 2.8859          | 30.7244 |
| No log        | 57.0  | 228  | 2.8846          | 30.8172 |
| No log        | 58.0  | 232  | 2.8837          | 30.5376 |
| No log        | 59.0  | 236  | 2.8826          | 31.1454 |
| No log        | 60.0  | 240  | 2.8813          | 30.9049 |
| No log        | 61.0  | 244  | 2.8802          | 30.6363 |
| No log        | 62.0  | 248  | 2.8802          | 31.3739 |
| No log        | 63.0  | 252  | 2.8799          | 30.9776 |
| No log        | 64.0  | 256  | 2.8793          | 29.8283 |
| No log        | 65.0  | 260  | 2.8795          | 29.6912 |
| No log        | 66.0  | 264  | 2.8804          | 29.7654 |
| No log        | 67.0  | 268  | 2.8810          | 29.1586 |
| No log        | 68.0  | 272  | 2.8822          | 28.8888 |
| No log        | 69.0  | 276  | 2.8819          | 29.7222 |
| No log        | 70.0  | 280  | 2.8810          | 29.9932 |
| No log        | 71.0  | 284  | 2.8811          | 30.2492 |
| No log        | 72.0  | 288  | 2.8802          | 29.9644 |
| No log        | 73.0  | 292  | 2.8791          | 30.3378 |
| No log        | 74.0  | 296  | 2.8790          | 29.8055 |
| No log        | 75.0  | 300  | 2.8794          | 29.0100 |
| No log        | 76.0  | 304  | 2.8795          | 30.7968 |
| No log        | 77.0  | 308  | 2.8790          | 31.5414 |
| No log        | 78.0  | 312  | 2.8783          | 31.5060 |
| No log        | 79.0  | 316  | 2.8775          | 31.4376 |
| No log        | 80.0  | 320  | 2.8766          | 31.6005 |
| No log        | 81.0  | 324  | 2.8767          | 31.3697 |
| No log        | 82.0  | 328  | 2.8769          | 31.6108 |
| No log        | 83.0  | 332  | 2.8770          | 31.4214 |
| No log        | 84.0  | 336  | 2.8772          | 31.6039 |
| No log        | 85.0  | 340  | 2.8776          | 32.0254 |
| No log        | 86.0  | 344  | 2.8779          | 31.4024 |
| No log        | 87.0  | 348  | 2.8783          | 32.0279 |
| No log        | 88.0  | 352  | 2.8786          | 31.8914 |
| No log        | 89.0  | 356  | 2.8788          | 31.6500 |
| No log        | 90.0  | 360  | 2.8791          | 31.7698 |
| No log        | 91.0  | 364  | 2.8793          | 31.6137 |
| No log        | 92.0  | 368  | 2.8793          | 31.8244 |
| No log        | 93.0  | 372  | 2.8790          | 31.5626 |
| No log        | 94.0  | 376  | 2.8786          | 31.3743 |
| No log        | 95.0  | 380  | 2.8785          | 31.4160 |
| No log        | 96.0  | 384  | 2.8784          | 31.6682 |
| No log        | 97.0  | 388  | 2.8782          | 31.8335 |
| No log        | 98.0  | 392  | 2.8782          | 31.7143 |
| No log        | 99.0  | 396  | 2.8782          | 31.7143 |
| No log        | 100.0 | 400  | 2.8782          | 31.7143 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
