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metadata
license: cc-by-4.0
base_model: Helsinki-NLP/opus-mt-tc-big-hu-en
tags:
  - generated_from_trainer
metrics:
  - bleu
model-index:
  - name: opus-mt-tc-big-hu-en-finetuned-news
    results: []

opus-mt-tc-big-hu-en-finetuned-news

This model is a fine-tuned version of Helsinki-NLP/opus-mt-tc-big-hu-en on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1412
  • Bleu: 40.3642
  • Gen Len: 44.0303

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: 5e-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: 3

Training results

Training Loss Epoch Step Validation Loss Bleu Gen Len
No log 0.11 100 1.1822 38.1953 43.5551
No log 0.22 200 1.1738 38.6712 43.549
No log 0.32 300 1.1602 39.2014 44.2009
No log 0.43 400 1.1503 39.2141 43.9468
1.2199 0.54 500 1.1451 39.2687 43.6871
1.2199 0.65 600 1.1349 39.445 43.9483
1.2199 0.75 700 1.1356 39.3787 43.47
1.2199 0.86 800 1.1233 39.7025 43.9054
1.2199 0.97 900 1.1224 39.9764 43.9656
1.1308 1.08 1000 1.1343 39.8533 43.9929
1.1308 1.19 1100 1.1446 39.7232 43.675
1.1308 1.29 1200 1.1378 40.0687 44.0606
1.1308 1.4 1300 1.1324 39.9239 43.7738
1.1308 1.51 1400 1.1330 40.0318 43.7756
0.8661 1.62 1500 1.1315 39.8677 43.7542
0.8661 1.72 1600 1.1185 40.1978 44.168
0.8661 1.83 1700 1.1298 40.254 44.0497
0.8661 1.94 1800 1.1191 40.2197 44.0295
0.8661 2.05 1900 1.1416 40.1255 44.0534
0.8198 2.16 2000 1.1479 40.3099 43.9854
0.8198 2.26 2100 1.1495 40.3473 44.0204
0.8198 2.37 2200 1.1453 40.329 44.0764
0.8198 2.48 2300 1.1450 40.2623 44.0944
0.8198 2.59 2400 1.1471 40.416 44.1797
0.6783 2.69 2500 1.1433 40.4645 44.0817
0.6783 2.8 2600 1.1405 40.4229 44.0554
0.6783 2.91 2700 1.1418 40.4142 44.0493

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.17.0
  • Tokenizers 0.15.1