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--- |
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license: apache-2.0 |
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base_model: Helsinki-NLP/opus-mt-lg-en |
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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: opus-mt-lg-en-informal |
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results: [] |
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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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# opus-mt-lg-en-informal |
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This model is a fine-tuned version of [Helsinki-NLP/opus-mt-lg-en](https://huggingface.co/Helsinki-NLP/opus-mt-lg-en) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1343 |
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- Bleu: 0.0 |
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- Bleu Precision: [0.019908987485779295, 0.0006461339651087659, 0.0, 0.0] |
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- Bleu Brevity Penalty: 1.0 |
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- Bleu Length Ratio: 1.2563 |
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- Bleu Translation Length: 5274 |
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- Bleu Reference Length: 4198 |
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## Model description |
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More information needed |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Bleu Precision | Bleu Brevity Penalty | Bleu Length Ratio | Bleu Translation Length | Bleu Reference Length | |
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|:-------------:|:-----:|:----:|:---------------:|:----:|:--------------------------------------------------------:|:--------------------:|:-----------------:|:-----------------------:|:---------------------:| |
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| 4.568 | 1.0 | 119 | 0.8525 | 0.0 | [0.011322534989778267, 0.00017458100558659218, 0.0, 0.0] | 1.0 | 1.5148 | 6359 | 4198 | |
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| 0.6495 | 2.0 | 238 | 0.1701 | 0.0 | [0.012054948135688253, 0.0003405994550408719, 0.0, 0.0] | 0.8379 | 0.8497 | 3567 | 4198 | |
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| 0.1889 | 3.0 | 357 | 0.1443 | 0.0 | [0.0408483896307934, 0.0010443864229765013, 0.0, 0.0] | 0.5226 | 0.6065 | 2546 | 4198 | |
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| 0.1513 | 4.0 | 476 | 0.1384 | 0.0 | [0.03887070376432079, 0.0005515719801434088, 0.0, 0.0] | 0.4879 | 0.5822 | 2444 | 4198 | |
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| 0.1424 | 5.0 | 595 | 0.1357 | 0.0 | [0.027095148078134845, 0.0012106537530266344, 0.0, 0.0] | 1.0 | 1.1341 | 4761 | 4198 | |
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| 0.1331 | 6.0 | 714 | 0.1346 | 0.0 | [0.016541609822646658, 0.0005732849226065354, 0.0, 0.0] | 1.0 | 1.3969 | 5864 | 4198 | |
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| 0.1265 | 7.0 | 833 | 0.1340 | 0.0 | [0.03237891356703238, 0.0016097875080489374, 0.0, 0.0] | 0.8839 | 0.8902 | 3737 | 4198 | |
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| 0.1296 | 8.0 | 952 | 0.1339 | 0.0 | [0.026692456479690523, 0.0013218770654329147, 0.0, 0.0] | 1.0 | 1.2315 | 5170 | 4198 | |
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| 0.123 | 9.0 | 1071 | 0.1340 | 0.0 | [0.025897226753670472, 0.001404165691551603, 0.0, 0.0] | 1.0 | 1.1682 | 4904 | 4198 | |
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| 0.1227 | 10.0 | 1190 | 0.1339 | 0.0 | [0.014839915868193504, 0.0008830579033682352, 0.0, 0.0] | 1.0 | 2.0386 | 8558 | 4198 | |
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| 0.117 | 11.0 | 1309 | 0.1343 | 0.0 | [0.019908987485779295, 0.0006461339651087659, 0.0, 0.0] | 1.0 | 1.2563 | 5274 | 4198 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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