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| Parameter | Value |
|---|---|
| Dataset Config Custom Datasets | ['dataset/custom/luganda/luganda_parallel.jsonl'] |
| Dataset Config Max Samples | None |
| Dataset Config Primary Dataset | custom |
| Dataset Config Validation Split | 0.15 |
| Evaluation Config Metrics | ['bleu', 'chrf', 'meteor'] |
| Evaluation Config Test Size | 150 |
| Language Name | Luganda |
| Language Pair | lg-en |
| Max Length | 512 |
| Model Name | Helsinki-NLP/opus-mt-mul-en |
| Team Config Assigned Developer | Marlon |
| Team Config Notes | Increased Batch by 2, epochs by 2 and max length * 2 |
| Team Config Priority | medium |
| Total Parameters | 77518848 |
| Trainable Parameters | 76994560 |
| Training Config Batch Size | 4 |
| Training Config Learning Rate | 2e-05 |
| Training Config Max Length | 256 |
| Training Config Num Epochs | 10 |
| Training Config Warmup Steps | 1000 |
| Training Config Weight Decay | 0.01 |
| Vocab Size | 64172 |
| Metric | Value |
|---|---|
| Baseline Bleu | 0.1178 |
| Baseline Chrf | 33.0561 |
| Bleu Improvement | 0.6561 |
| Bleu Improvement Percent | 557.2142 |
| Chrf Improvement | 51.5256 |
| Chrf Improvement Percent | 155.8731 |
| Epoch | 10.0000 |
| Eval Bleu | 0.7739 |
| Eval Chrf | 84.5818 |
| Eval Loss | 0.2106 |
| Eval Runtime | 481.4612 |
| Eval Samples Per Second | 15.5820 |
| Eval Steps Per Second | 3.8960 |
| Final Epoch | 10.0000 |
| Final Eval Bleu | 0.7739 |
| Final Eval Chrf | 84.5818 |
| Final Eval Loss | 0.2106 |
| Final Eval Runtime | 481.4612 |
| Final Eval Samples Per Second | 15.5820 |
| Final Eval Steps Per Second | 3.8960 |
| Grad Norm | 0.6669 |
| Learning Rate | 0.0000 |
| Loss | 0.0663 |
| Total Flos | 6254416761716736.0000 |
| Total Samples | 50012.0000 |
| Train Loss | 0.2427 |
| Train Runtime | 16184.3599 |
| Train Samples | 42510.0000 |
| Train Samples Per Second | 26.2660 |
| Train Steps Per Second | 6.5670 |
| Validation Samples | 7502.0000 |
| Metric | Value |
|---|---|
| Baseline Bleu | 0.1178 |
| Baseline Chrf | 33.0561 |
| Bleu Improvement | 0.6561 |
| Bleu Improvement Percent | 557.2142 |
| Chrf Improvement | 51.5256 |
| Chrf Improvement Percent | 155.8731 |
| Epoch | 10.0000 |
| Eval Bleu | 0.7739 |
| Eval Chrf | 84.5818 |
| Eval Loss | 0.2106 |
| Eval Runtime | 481.4612 |
| Eval Samples Per Second | 15.5820 |
| Eval Steps Per Second | 3.8960 |
| Final Epoch | 10.0000 |
| Final Eval Bleu | 0.7739 |
| Final Eval Chrf | 84.5818 |
| Final Eval Loss | 0.2106 |
| Final Eval Runtime | 481.4612 |
| Final Eval Samples Per Second | 15.5820 |
| Final Eval Steps Per Second | 3.8960 |
| Grad Norm | 0.6669 |
| Learning Rate | 0.0000 |
| Loss | 0.0663 |
| Total Flos | 6254416761716736.0000 |
| Total Samples | 50012.0000 |
| Train Loss | 0.2427 |
| Train Runtime | 16184.3599 |
| Train Samples | 42510.0000 |
| Train Samples Per Second | 26.2660 |
| Train Steps Per Second | 6.5670 |
| Validation Samples | 7502.0000 |
| Metric | Value |
|---|---|
| Baseline Bleu | 0.1178 |
| Baseline Chrf | 33.0561 |
| Bleu Improvement | 0.6561 |
| Bleu Improvement Percent | 557.2142 |
| Chrf Improvement | 51.5256 |
| Chrf Improvement Percent | 155.8731 |
| Epoch | 10.0000 |
| Eval Bleu | 0.7739 |
| Eval Chrf | 84.5818 |
| Eval Loss | 0.2106 |
| Eval Runtime | 481.4612 |
| Eval Samples Per Second | 15.5820 |
| Eval Steps Per Second | 3.8960 |
| Final Epoch | 10.0000 |
| Final Eval Bleu | 0.7739 |
| Final Eval Chrf | 84.5818 |
| Final Eval Loss | 0.2106 |
| Final Eval Runtime | 481.4612 |
| Final Eval Samples Per Second | 15.5820 |
| Final Eval Steps Per Second | 3.8960 |
| Grad Norm | 0.6669 |
| Learning Rate | 0.0000 |
| Loss | 0.0663 |
| Total Flos | 6254416761716736.0000 |
| Total Samples | 50012.0000 |
| Train Loss | 0.2427 |
| Train Runtime | 16184.3599 |
| Train Samples | 42510.0000 |
| Train Samples Per Second | 26.2660 |
| Train Steps Per Second | 6.5670 |
| Validation Samples | 7502.0000 |
| Metric | Value |
|---|---|
| Baseline Bleu | 0.1178 |
| Baseline Chrf | 33.0561 |
| Bleu Improvement | 0.6561 |
| Bleu Improvement Percent | 557.2142 |
| Chrf Improvement | 51.5256 |
| Chrf Improvement Percent | 155.8731 |
| Epoch | 10.0000 |
| Eval Bleu | 0.7739 |
| Eval Chrf | 84.5818 |
| Eval Loss | 0.2106 |
| Eval Runtime | 481.4612 |
| Eval Samples Per Second | 15.5820 |
| Eval Steps Per Second | 3.8960 |
| Final Epoch | 10.0000 |
| Final Eval Bleu | 0.7739 |
| Final Eval Chrf | 84.5818 |
| Final Eval Loss | 0.2106 |
| Final Eval Runtime | 481.4612 |
| Final Eval Samples Per Second | 15.5820 |
| Final Eval Steps Per Second | 3.8960 |
| Grad Norm | 0.6669 |
| Learning Rate | 0.0000 |
| Loss | 0.0663 |
| Total Flos | 6254416761716736.0000 |
| Total Samples | 50012.0000 |
| Train Loss | 0.2427 |
| Train Runtime | 16184.3599 |
| Train Samples | 42510.0000 |
| Train Samples Per Second | 26.2660 |
| Train Steps Per Second | 6.5670 |
| Validation Samples | 7502.0000 |
1from transformers import pipeline
2
3translator = pipeline("translation", model="marlonbino/opus-mt-lg-en-finetuned")
4result = translator("Your text here")
5print(result[0]["translation_text"])9ebbc90beedf44be9c2c886d442a19ef1@misc{opus_mt_lg_en_finetuned,
2 title={opus-mt-lg-en-finetuned},
3 author={OpenCHS Team},
4 year={2025},
5 publisher={Hugging Face},
6 url={https://huggingface.co/marlonbino/opus-mt-lg-en-finetuned}
7}