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Qwen-14b-chat-yarn-32k

Text Generation
Transformers
Safetensors
Chinese
English
qwen
conversational
custom_code
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  1. README.md +14 -23
  2. README_en.md +13 -24
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README.md CHANGED
@@ -28,29 +28,20 @@ pipeline_tag: text-generation
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  <br>
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  # LongBench测试结果
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- ### LongBench的passage_retrieval_zh的评测结果
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- | 模型 | 得分 (acc) |
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- |------------------------------|------------|
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- | **Qwen-14b-chat-yarn-32k** | **0.94** |
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- | gpt-3.5-turbo-16k | 0.81 |
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- | chatglm3-32k | 0.725 |
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- | Qwen-14b-chat | 0.525 |
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- | Qwen-14b-chat-32k-lora | 0.34 |
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- | **Qwen-7b-chat-yarn-32k** | **0.325** |
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- | Qwen-7b-chat | 0.26 |
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- | LongAlpaca-7b-32k-chinese-v2 | 0.12 |
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- | CausalLM-14b | 0.086 |
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-
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-
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- ### LongBench的passage_retrieval_en的评测结果
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- | 模型 | 得分 (acc) |
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- |-----------------------------|------------|
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- | **Qwen-14b-chat-yarn-32k** | **0.945** |
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- | chatglm3-32k | 0.815 |
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- | gpt-3.5-turbo-16k | 0.88 |
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- | **Qwen-7b-chat-yarn-32k** | **0.47** |
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- | Qwen-14b-chat | 0.24 |
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- | Qwen-7b-chat | 0.235 |
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  Qwen-14b-chat-yarn-32k经过微调后，在多文档问答（或检索）任务上提升非常显著，大幅领先其他同规模的模型。
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  <br>
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  # LongBench测试结果
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+ ### LongBench的passage_retrieval的评测结果
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+
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+ | 模型 | 准确率 （中文） | 准确率 （英文） |
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+ |-----------------------------|-----------|----------|
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+ | **Qwen-14b-chat-yarn-32k** | **0.94** | **0.945** |
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+ | **Qwen-7b-chat-yarn-32k** | **0.325** | **0.47** |
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+ | gpt-3.5-turbo-16k | 0.81 | 0.88 |
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+ | chatglm3-32k | 0.725 | 0.815 |
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+ | Qwen-14b-chat | 0.525 | 0.24 |
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+ | Qwen-14b-chat-32k-lora | 0.34 | \ |
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+ | Qwen-7b-chat | 0.26 | 0.235 |
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+ | LongAlpaca-7b-32k-chinese-v2 | 0.12 | \ |
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+ | CausalLM-14b | 0.086 | \ |
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+
 
 
 
 
 
 
 
 
 
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  Qwen-14b-chat-yarn-32k经过微调后，在多文档问答（或检索）任务上提升非常显著，大幅领先其他同规模的模型。
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README_en.md CHANGED
@@ -26,30 +26,19 @@ pipeline_tag: text-generation
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  * During inference, the model can give high-accuracy answers without specially designed prompts
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  # Evaluation results in LongBench
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- ### Evaluation results for passage_retrieval_zh in LongBench
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-
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- | Models | Accuracy |
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- |------------------------------|-------------|
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- | **Qwen-14b-chat-yarn-32k** | **0.94** |
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- | gpt-3.5-turbo-16k | 0.81 |
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- | chatglm3-32k | 0.725 |
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- | Qwen-14b-chat | 0.525 |
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- | Qwen-14b-chat-32k-lora | 0.34 |
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- | **Qwen-7b-chat-yarn-32k** | **0.325** |
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- | Qwen-7b-chat | 0.26 |
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- | LongAlpaca-7b-32k-chinese-v2 | 0.12 |
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- | CausalLM-14b | 0.086 |
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-
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- ### Evaluation results for passage_retrieval_en in LongBench
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- | Models | Accuracy |
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- |----------------------------------|---------------|
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- | **Qwen-14b-chat-yarn-32k** | **0.945** |
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- | chatglm3-32k | 0.815 |
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- | gpt-3.5-turbo-16k | 0.88 |
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- | **Qwen-7b-chat-yarn-32k** | **0.47** |
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- | Qwen-14b-chat | 0.24 |
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- | Qwen-7b-chat | 0.235 |
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-
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  Qwen-14b-chat-yarn-32k has shown significant improvement in multi-document question-answering (or retrieval) tasks and outperforms other models of similar scale.
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  * During inference, the model can give high-accuracy answers without specially designed prompts
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  # Evaluation results in LongBench
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+ ### Evaluation results for passage_retrieval in LongBench
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+
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+ | Model | Accuracy (zh) | Accuracy (en) |
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+ |------------------------------|---------------|---------------|
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+ | **Qwen-14b-chat-yarn-32k** | **0.94** | **0.945** |
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+ | **Qwen-7b-chat-yarn-32k** | **0.325** | **0.47** |
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+ | gpt-3.5-turbo-16k | 0.81 | 0.88 |
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+ | chatglm3-32k | 0.725 | 0.815 |
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+ | Qwen-14b-chat | 0.525 | 0.24 |
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+ | Qwen-14b-chat-32k-lora | 0.34 | \ |
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+ | Qwen-7b-chat | 0.26 | 0.235 |
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+ | LongAlpaca-7b-32k-chinese-v2 | 0.12 | \ |
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+ | CausalLM-14b | 0.086 | \ |
 
 
 
 
 
 
 
 
 
 
 
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  Qwen-14b-chat-yarn-32k has shown significant improvement in multi-document question-answering (or retrieval) tasks and outperforms other models of similar scale.
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