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--- |
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library_name: transformers |
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tags: |
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- translation |
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license: gemma |
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language: |
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- en |
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- es |
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- fr |
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- de |
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- pt |
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- ja |
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- ko |
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- zh |
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- ar |
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- ru |
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- hi |
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--- |
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# YanoljaNEXT-Rosetta-4B |
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This model is a fine-tuned version of [`google/gemma-3-4b-pt`](https://huggingface.co/google/gemma-3-4b-pt). As it is intended solely for text generation, we have extracted and utilized only the `Gemma3ForCausalLM` component from the original architecture. |
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Different from our previous EEVE models, this model does not feature an expanded tokenizer. |
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- **Model Name:** `yanolja/YanoljaNEXT-Rosetta-4B` |
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- **Base Model:** `google/gemma-3-4b-pt` |
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## Model Description |
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This model is a 4-billion parameter, decoder-only language model built on the Gemma3 architecture and fine-tuned by Yanolja NEXT. It is specifically designed to translate structured data (JSON format) while preserving the original data structure. |
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The model was trained on a multilingual dataset covering the following languages: |
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- English |
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- Spanish |
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- French |
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- German |
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- Portuguese |
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- Japanese |
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- Korean |
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- Chinese |
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- Arabic |
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- Russian |
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- Hindi |
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While optimized for these languages, it may also perform effectively on other languages supported by the base Gemma3 model. |
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## How to use |
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You can use this model with the `transformers` library as follows: |
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```python |
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import json |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_id = "yanolja/YanoljaNEXT-Rosetta-4B" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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dtype=torch.bfloat16, |
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device_map="auto", |
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max_memory={0: "23GB"}, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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target_language = "Korean" |
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context = { |
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"context": "Simple introduction about a tech company.", |
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"tone": "Informative and helpful", |
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"glossary": { |
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"Yanolja NEXT": "μΌλμλ₯μ€νΈ", |
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"travel industry": "μ¬ν μ°μ
", |
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} |
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} |
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system = [f"Translate the user's text to {target_language}."] |
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for key, value in context.items(): |
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key_pascal = key.capitalize() |
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if isinstance(value, dict): |
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system.append(f"{key_pascal}:") |
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for f, t in value.items(): |
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system.append(f"- {f} -> {t}") |
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else: |
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system.append(f"{key_pascal}: {value}") |
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system.append("Provide the final translation immediately without any other text.") |
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source = { |
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"company_name": "Yanolja NEXT", |
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"description": "Yanolja NEXT is a company that provides cutting-edge " |
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"technology for the global travel industry.", |
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} |
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messages = [ |
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{"role": "system", "content": "\n".join(system)}, |
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{"role": "user", "content": json.dumps(source, ensure_ascii=False)}, |
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] |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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print(prompt) |
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# <bos><start_of_turn>instruction |
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# Translate the user's text to Korean. |
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# Context: Simple introduction about a tech company. |
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# Tone: Informative and helpful |
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# Glossary: |
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# - Yanolja NEXT -> μΌλμλ₯μ€νΈ |
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# - travel industry -> μ¬ν μ°μ
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# Provide the final translation immediately without any other text.<end_of_turn> |
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# <start_of_turn>source |
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# {"company_name": "Yanolja NEXT", "description": "Yanolja NEXT is a company that provides cutting-edge technology for the global travel industry."}<end_of_turn> |
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# <start_of_turn>translation |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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input_length = inputs["input_ids"].shape[1] |
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with torch.inference_mode(): |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=64, |
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) |
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generated_tokens = outputs[0][input_length:] |
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translation = tokenizer.decode(generated_tokens, skip_special_tokens=True) |
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print(json.dumps(json.loads(translation), indent=2, ensure_ascii=False)) |
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# { |
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# "company_name": "μΌλμλ₯μ€νΈ", |
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# "description": "μΌλμλ₯μ€νΈλ κΈλ‘λ² μ¬ν μ°μ
μ μ΅μ²¨λ¨ κΈ°μ μ μ 곡νλ νμ¬μ
λλ€." |
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# } |
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``` |
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The model outputs the final translation in JSON format when appropriate, or plain text for simple translations. |
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## Training Procedure |
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### Training Data |
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The translation datasets were compiled from several sources, including: |
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- [AI Hub](https://aihub.or.kr/) |
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- [Europarl](https://www.statmt.org/europarl/) |
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The model was fine-tuned on multilingual translation data to optimize performance across the supported language pairs. |
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| Language | Portion (%) | Language | Portion (%) | |
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|----------|-------------|----------|-------------| |
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| Korean | 24.2 | French | 2.8 | |
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| English | 16.2 | German | 2.5 | |
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| Japanese | 5.8 | Russian | 2.4 | |
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| Italian | 5.3 | Arabic | 2.3 | |
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| Chinese | 4.4 | Other | 30.2 | |
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| Spanish | 3.9 | | | |
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## Performance |
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### Translation Quality Benchmarks |
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The following CHrF++ scores (WMT24++) demonstrate the model's competitive performance compared to other state-of-the-art translation models on English to Korean translation: |
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| Model | CHrF++ Score (WMT24++) | |
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|------------------------------------|--------------| |
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| yanolja/YanoljaNEXT-Rosetta-12B | 34.75 | |
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| yanolja/YanoljaNEXT-Rosetta-20B | 33.87 | |
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| google/gemini-2.0-flash-001 | 33.81 | |
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| openai/gpt-oss-120b | 31.51 | |
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| **yanolja/YanoljaNEXT-Rosetta-4B** | **31.31** | |
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| openai/gpt-4.1-nano | 31.15 | |
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| Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 | 31.02 | |
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| openai/gpt-oss-20b | 30.56 | |
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| google/gemma-3-27b-it | 30.05 | |
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| google/gemma-3-4b-pt | 27.53 | |
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YanoljaNEXT-Rosetta-4B achieves competitive translation quality while maintaining the efficiency of a 4B parameter model. |
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## Intended Uses & Limitations |
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This model is intended for translating structured data (JSON format) while preserving the original structure. It is particularly well-suited for tasks such as localizing product catalogs, translating hotel reviews, or handling any other structured content that requires accurate translation. |
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### Limitations |
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The model's primary focus is on JSON data. Performance on unstructured text or other data formats may vary. |
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### License |
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This model is released under the Gemma license, inherited from its base model, [`google/gemma-3-4b-pt`](https://huggingface.co/google/gemma-3-4b-pt). Please consult the official [Gemma license terms](https://ai.google.dev/gemma/terms) for detailed usage guidelines. |
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## Citation |
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If you use this model, please consider citing: |
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``` |
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@misc{yanolja2025yanoljanextrosetta, |
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author = {Yanolja NEXT Co., Ltd.}, |
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title = {YanoljaNEXT-Rosetta-4B}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face repository}, |
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howpublished = {\\url{https://huggingface.co/yanolja/YanoljaNEXT-Rosetta-4B}} |
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} |
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``` |
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## References |
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This work utilizes several models and datasets. We would like to acknowledge the original authors for their valuable contributions to the field. |
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``` |
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@misc{gemma3, |
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author = {Google}, |
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title = {Gemma 3}, |
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year = {2024}, |
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publisher = {Google DeepMind}, |
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howpublished = {\\url{https://deepmind.google/models/gemma/gemma-3/}} |
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} |
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@misc{aihub, |
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author = {National Information Society Agency (NIA)}, |
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title = {AI-Hub: AI Integrated Platform}, |
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year = {2025}, |
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publisher = {National Information Society Agency}, |
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howpublished = {\\url{https://aihub.or.kr}} |
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} |
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@article{europarl, |
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author = {Koehn, Philipp}, |
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title = {Europarl: A Parallel Corpus for Statistical Machine Translation}, |
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journal = {MT Summit}, |
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year = {2005}, |
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pages = {79--86} |
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} |
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``` |
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