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--- |
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library_name: peft |
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license: cc-by-nc-4.0 |
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base_model: facebook/nllb-200-distilled-1.3B |
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model-index: |
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- name: mon_nllb_1.3B |
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results: |
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- task: |
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type: translation |
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dataset: |
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type: flores-200 |
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name: FLORES-200 |
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metrics: |
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- name: BLEU |
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type: BLEU |
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value: 44.06 |
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verified: False |
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- name: chrF |
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type: chrF |
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value: 44.43 |
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verified: False |
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- name: METEOR |
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type: METEOR |
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value: 0.537 |
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verified: False |
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datasets: |
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- Billyyy/mn-en-parallel |
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language: |
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- mn |
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metrics: |
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- bleu |
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- chrf |
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- meteor |
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pipeline_tag: translation |
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--- |
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# mon_nllb_1.3B |
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This model is a fine-tuned version of [facebook/nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- BLEU: 44.06 |
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- chrF: 44.43 |
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- METEOR: 0.537 |
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## Example Usage |
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```python |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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model_name = "Billyyy/mon_nllb_1.3B" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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text = "Сайн байна уу"?" |
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inputs = tokenizer(text, return_tensors="pt") |
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output_tokens = model.generate(**inputs) |
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translated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True) |
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print(translated_text) |
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``` |
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## Model description |
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This model was finetuned on Mongolian->English parallel dataset with LoRA |
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## Training and evaluation data |
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Training data: |
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- 1M translation data from https://github.com/sharavsambuu/english-mongolian-nmt-dataset-augmentation?tab=readme-ov-file |
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- OpenSubtitles |
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- TED2020 |
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Evaluation data: |
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- FLORES-200 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 40 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 160 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 2 |
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- mixed_precision_training: FP16 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 7.3708 | 0.1522 | 1000 | 7.2420 | |
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| 7.25 | 0.3044 | 2000 | 7.2126 | |
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| 7.237 | 0.4567 | 3000 | 7.2120 | |
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| 7.2344 | 0.6089 | 4000 | 7.2137 | |
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| 7.2323 | 0.7611 | 5000 | 7.2130 | |
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| 7.2351 | 0.9133 | 6000 | 7.2121 | |
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| 7.222 | 1.0656 | 7000 | 7.2131 | |
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| 7.22 | 1.2178 | 8000 | 7.2122 | |
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| 7.2077 | 1.3700 | 9000 | 7.2131 | |
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| 7.2132 | 1.5223 | 10000 | 7.2132 | |
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| 7.2211 | 1.6745 | 11000 | 7.2128 | |
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| 7.2269 | 1.8267 | 12000 | 7.2131 | |
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| 7.2296 | 1.9789 | 13000 | 7.2132 | |
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### Framework versions |
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- PEFT 0.14.0 |
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- Transformers 4.49.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
