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--- |
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language: |
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- ko |
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- uz |
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- en |
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- ru |
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- zh |
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- ja |
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- km |
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- my |
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- si |
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- tl |
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- th |
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- vi |
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- kk |
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- bn |
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- mn |
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- id |
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- ne |
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- pt |
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tags: |
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- translation |
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- multilingual |
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- korean |
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- uzbek |
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datasets: |
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- custom_parallel_corpus |
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license: mit |
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--- |
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# QWEN2.5-7B-Bnk-7e |
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## Model Description |
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QWEN2.5-7B-Bnk-5e is a multilingual translation model based on the QWEN 2.5 architecture with 7 billion parameters. It specializes in translating multiple languages to Korean and Uzbek. |
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## Intended Uses & Limitations |
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The model is designed for translating text from various Asian and European languages to Korean and Uzbek. It can be used for tasks such as: |
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- Multilingual document translation |
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- Cross-lingual information retrieval |
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- Language learning applications |
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- International communication assistance |
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Please note that while the model strives for accuracy, it may not always produce perfect translations, especially for idiomatic expressions or highly context-dependent content. |
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## Training and Evaluation Data |
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The model was fine-tuned on a diverse dataset of parallel texts covering the supported languages. Evaluation was performed on held-out test sets for each language pair. |
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## Training Procedure |
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Fine-tuning was performed on the QWEN 2.5 7B base model using custom datasets for the specific language pairs. |
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## Supported Languages |
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The model supports translation from the following languages to Korean and Uzbek: |
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- Kazakh (kk) |
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- Russian (ru) |
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- Thai (th) |
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- Chinese (Simplified) (zh) |
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- Chinese (Traditional) (zh-tw, zh-hant) |
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- Bengali (bn) |
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- Mongolian (mn) |
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- Indonesian (id) |
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- Nepali (ne) |
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- English (en) |
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- Khmer (km) |
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- Portuguese (pt) |
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- Sinhala (si) |
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- Korean (ko) |
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- Tagalog (tl) |
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- Burmese (my) |
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- Vietnamese (vi) |
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- Japanese (ja) |
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## How to Use |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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model_name = "FINGU-AI/QWEN2.5-7B-Bnk-5e" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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# Example usage |
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source_text = "Hello, how are you?" |
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source_lang = "en" |
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target_lang = "ko" # or "uz" for Uzbek |
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messages = [ |
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{"role": "system", "content": f"""Translate {input_lang} to {output_lang} word by word correctly."""}, |
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{"role": "user", "content": f"""{source_text}"""}, |
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] |
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# Apply chat template |
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input_ids = tokenizer.apply_chat_template( |
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messages, |
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add_generation_prompt=True, |
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return_tensors="pt" |
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).to('cuda') |
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outputs = model.generate(input_ids, max_length=100) |
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response = outputs[0][input_ids.shape[-1]:] |
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translated_text = tokenizer.decode(response, skip_special_tokens=True) |
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print(translated_text) |
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``` |
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## Performance |
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## Limitations |
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- The model's performance may vary across different language pairs and domains. |
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- It may struggle with very colloquial or highly specialized text. |
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- The model may not always capture cultural nuances or context-dependent meanings accurately. |
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## Ethical Considerations |
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- The model should not be used for generating or propagating harmful, biased, or misleading content. |
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- Users should be aware of potential biases in the training data that may affect translations. |
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- The model's outputs should not be considered as certified translations for official or legal purposes without human verification. |
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## Citation |
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```bibtex |
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@misc{fingu2023qwen25, |
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author = {FINGU AI and AI Team}, |
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title = {QWEN2.5-7B-Bnk-7e: A Multilingual Translation Model}, |
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year = {2024}, |
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publisher = {Hugging Face}, |
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journal = {Hugging Face Model Hub}, |
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howpublished = {\url{https://huggingface.co/FINGU-AI/QWEN2.5-7B-Bnk-5e}} |
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} |
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