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Add license information to README

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@@ -38,6 +38,7 @@ Please also check the raw data after our processing if you are interested: [love
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coder_spec_stage1">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coder_spec">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coder_spec_pp_stage1">πŸ€—</a></td>
 
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coder_spec_pp">πŸ€—</a></td>
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  </tr>
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  <tr>
@@ -45,6 +46,7 @@ Please also check the raw data after our processing if you are interested: [love
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8b_spec_stage1">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8b_spec">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8b_spec_pp_stage1">πŸ€—</a></td>
 
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8b_spec_pp">πŸ€—</a></td>
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  </tr>
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  <tr>
@@ -52,6 +54,7 @@ Please also check the raw data after our processing if you are interested: [love
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coder_spec_stage1">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coder_spec">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coder_spec_pp_stage1">πŸ€—</a></td>
 
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coder_spec_pp">πŸ€—</a></td>
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  </tr>
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  </table>
@@ -60,4 +63,12 @@ Please also check the raw data after our processing if you are interested: [love
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  While having full executable code theoretically allows us to generate reliable execution trajectories as responses, two challenges arise: 1) Obtaining a deterministic reverse function for input prediction is impractical; 2) Automatically constructed trajectories are constrained by pre-designed templates and lack the expressiveness and generalizability of free-form natural language reasoning. Thus, we adopt a fully LLM-based approach for synthesizing all the desired responses using DeepSeek-V2.5, as it has top-tier performance but extremely low cost compared to other advanced LLMs.
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- *Due to our collaborators' compliance requirements, we only release the PythonEdu-Rs subset (this page) of full dataset.
 
 
 
 
 
 
 
 
 
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coder_spec_stage1">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coder_spec">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coder_spec_pp_stage1">πŸ€—</a></td>
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+ <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coder_spec_pp_stage1">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/qwen2.5-7b-coder_spec_pp">πŸ€—</a></td>
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  </tr>
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  <tr>
 
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8b_spec_stage1">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8b_spec">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8b_spec_pp_stage1">πŸ€—</a></td>
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+ <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8b_spec_pp_stage1">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/llama3.1-8b_spec_pp">πŸ€—</a></td>
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  </tr>
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  <tr>
 
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coder_spec_stage1">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coder_spec">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coder_spec_pp_stage1">πŸ€—</a></td>
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+ <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coder_spec_pp_stage1">πŸ€—</a></td>
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  <td style="text-align: center; vertical-align: middle;"><a href="https://huggingface.co/lovebird-ll/dsv2-lite-coder_spec_pp">πŸ€—</a></td>
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  </tr>
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  </table>
 
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  While having full executable code theoretically allows us to generate reliable execution trajectories as responses, two challenges arise: 1) Obtaining a deterministic reverse function for input prediction is impractical; 2) Automatically constructed trajectories are constrained by pre-designed templates and lack the expressiveness and generalizability of free-form natural language reasoning. Thus, we adopt a fully LLM-based approach for synthesizing all the desired responses using DeepSeek-V2.5, as it has top-tier performance but extremely low cost compared to other advanced LLMs.
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+ *Due to our collaborators' compliance requirements, we only release the PythonEdu-Rs subset (this page) of full dataset.
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+ **License**
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+ The license for this dataset is odc-by.
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+ **License**
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+ The license for this dataset is odc-by.