dhtocks
/

nllb-200-distilled-350M_en-ko

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 ---
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 ---
 library_name: transformers
+license: cc-by-nc-4.0
+datasets:
+- allenai/nllb
+- facebook/flores
+language:
+- ko
+- en
+metrics:
+- chrf
+pipeline_tag: translation
 ---
+# NLLB-200 Distilled-350M_en2ko
+The NLLB-200 model showed outstanding performance in translation task and contributed to solving problems with low-resource languages.
+Despite their efforts, it is still hard to run 600M or more than 1B model for those who have not enough computing environment.
+So I made much smaller model that expertized translaing English to Korean. you can also run it with cpu (No mixed-precision, No Quantization).
+## Model
+- Model: model is based on NLLB-200 600M
+  - **Parameters: 350,537,728 (350M)**
+  - **Encoder layers: 12 -> 3**
+  - **Decoder layers: 12 -> 3**
+  - FFN dimension: 4096 (same)
+  - Embed dimension: 1024 (same)
+  - Vocab size: 256206 (same)
+- Licnese: CC-BY-NC
+## Data
+- Training Data: [NLLB dataset](https://huggingface.co/datasets/allenai/nllb)
+- Evaluation Data: [Flores-200 dataset](https://huggingface.co/datasets/facebook/flores)
+## Metric
+- CPU: Intel (R) Xeon(R) CPU @ 2.20GHz (16 cores)
+- GPU: NVIDIA L4 24GB
+|                        | #Params | chrF(++) | GPU Inference time (s) | CPU Inference time (s) |
+| ---------------------- | ------- | -------- | ---------------------- | ---------------------- |
+| NLLB-200 3.3B          | 3.3B    | 34.3     | 0.98 s                 | 4.65 s                 |
+| NLLB-200 1.3B          | 1.3B    | 32.1     | 0.89 s                 | 2.46 s                 |
+| NLLB-200 600M          | 600M    | 32       | 0.43 s                 | 1.52 s                 |
+| NLLB-200 350M (*ours*) | 350M    | 24.6     | 0.24 s                 | 1.43 s                 |
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+model = AutoModelForSeq2SeqLM.from_pretrained('dhtocks/nllb-200-distilled-350M_en-ko', forced_bos_token_id=256098)
+tokenizer = AutoTokenizer.from_pretrained('dhtocks/nllb-200-distilled-350M_en-ko', src_lang='eng_Latn', tgt_lang='kor_Hang')
+inputs = tokenizer('[YOUR_INPUT]', return_tensors="pt")
+output = model.generate(**inputs)
+print(tokenizer.decode(output[0]))
+```
+## Citation
+```bibtex
+@misc{,
+  title={NLLB-200 distilled_350M_en-ko},
+  author={Saechan Oh},
+  year={2024}
+}
+```