Improve metadata (pipeline tag, library name, correct typo) and add GitHub link
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by
nielsr HF Staff - opened
README.md
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---
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-
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language:
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- en
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- zh
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tags:
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- machine
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- O1-like model
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- Chat
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pipeline_tag: text-generation
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---
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# DRT
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<p align="center">
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🤗 <a href="https://huggingface.co/Krystalan/DRT-7B">DRT-7B</a>   |   🤗 <a href="https://huggingface.co/Krystalan/DRT-8B">DRT-8B</a>   |   🤗 <a href="https://huggingface.co/Krystalan/DRT-14B">DRT-14B</a>   |    📑 <a href="https://arxiv.org/abs/2412.17498">Paper</a>
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</p>
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@@ -80,7 +81,8 @@ In this work, we introduce DRT, an attempt to bring the success of long thought
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### Model Prompts
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During model inference, please use the following prompts:
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- System prompt: `You are a philosopher skilled in deep thinking, accustomed to exploring complex problems with profound insight.`
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- User prompt: `Please translate the following text from English to Chinese
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DRT models will first generate the thought and then provide the final translation, with the following format:
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```
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Please translate the following text from English to Chinese
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messages = [
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{"role": "system", "content": "You are a philosopher skilled in deep thinking, accustomed to exploring complex problems with profound insight."},
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{"role": "user", "content": prompt}
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model=[model_name],
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messages=[
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{"role": "system", "content": "You are a philosopher skilled in deep thinking, accustomed to exploring complex problems with profound insight."},
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{"role": "user", "content": "Please translate the following text from English to Chinese
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],
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temperature=0.1,
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top_p=0.8,
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|This cold officer upon a monument, who dropped epithets unconcernedly down, would be finer as a dead man, he thought. | 他认为,这个站在纪念碑上的冷漠官员,若死了会更好,他不带任何感情地抛下了一些称呼。 | 这个冷冰冰的官员站在纪念碑上,毫不在意地抛下一些称号,他想,如果作为一个死人会更出色。 | 纪念碑上的冷淡官员,漫不经心地吟咏那些修饰语,他心想,若化为亡者,或许更显尊贵。 |
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This work is licensed under cc-by-nc-sa-4.0
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---
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base_model:
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- Qwen/Qwen2.5-14B-Instruct
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language:
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- en
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- zh
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license: cc-by-nc-sa-4.0
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pipeline_tag: translation
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library_name: transformers
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tags:
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- machine translation
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- O1-like model
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- Chat
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---
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# DRT
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<p align="center">
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🤗 <a href="https://huggingface.co/Krystalan/DRT-7B">DRT-7B</a>   |   🤗 <a href="https://huggingface.co/Krystalan/DRT-8B">DRT-8B</a>   |   🤗 <a href="https://huggingface.co/Krystalan/DRT-14B">DRT-14B</a>   |    📑 <a href="https://arxiv.org/abs/2412.17498">Paper</a> |   💻 <a href="https://github.com/krystalan/DRT-o1">Code</a>
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</p>
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### Model Prompts
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During model inference, please use the following prompts:
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- System prompt: `You are a philosopher skilled in deep thinking, accustomed to exploring complex problems with profound insight.`
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- User prompt: `Please translate the following text from English to Chinese:
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[An English text]`
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DRT models will first generate the thought and then provide the final translation, with the following format:
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```
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Please translate the following text from English to Chinese:
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The mother, with her feet propped up on a stool, seemed to be trying to get to the bottom of that answer, whose feminine profundity had struck her all of a heap."
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messages = [
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{"role": "system", "content": "You are a philosopher skilled in deep thinking, accustomed to exploring complex problems with profound insight."},
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{"role": "user", "content": prompt}
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model=[model_name],
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messages=[
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{"role": "system", "content": "You are a philosopher skilled in deep thinking, accustomed to exploring complex problems with profound insight."},
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{"role": "user", "content": "Please translate the following text from English to Chinese:
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The mother, with her feet propped up on a stool, seemed to be trying to get to the bottom of that answer, whose feminine profundity had struck her all of a heap."},
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],
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temperature=0.1,
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top_p=0.8,
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|This cold officer upon a monument, who dropped epithets unconcernedly down, would be finer as a dead man, he thought. | 他认为,这个站在纪念碑上的冷漠官员,若死了会更好,他不带任何感情地抛下了一些称呼。 | 这个冷冰冰的官员站在纪念碑上,毫不在意地抛下一些称号,他想,如果作为一个死人会更出色。 | 纪念碑上的冷淡官员,漫不经心地吟咏那些修饰语,他心想,若化为亡者,或许更显尊贵。 |
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## Data
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We release the synthesized data (named ```MetaphorTrans```), please refer to `data/MetaphorTrans_*.jsonl`, where `text` and `trans` denote the source English sentences and the target Chinese translations, respectively. `thought` indicates the thought content for MT.
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# License
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This work is licensed under cc-by-nc-sa-4.0
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