mirror of
https://github.com/deepseek-ai/DeepSeek-Math
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42 lines
1.9 KiB
Markdown
42 lines
1.9 KiB
Markdown
## 1. Introduction
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We provide a test script for both zero-shot and few-shot evaluation on mathematical reasoning benchmarks used in our paper.
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## 2. Setup
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First configure the `prefix` in `environment.yml` and then run the following command
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```
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conda env create -f environment.yml
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```
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## 3. Evaluation
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For chain-of-thought evaluation of DeepSeekMath-Instruct and DeepSeekMath-RL, our script (see `def markup_question()` in `run_subset_parallel.py`) processes each question as follows:
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* English questions: `{question}\nPlease reason step by step, and put your final answer within \\boxed{}.`
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* Chinese questions: `{question}\n请通过逐步推理来解答问题，并把最终答案放置于\\boxed{}中。`
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For tool-integrated reasoning, we process each question as follows:
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* English questions: `{question}\nPlease integrate natural language reasoning with programs to solve the problem above, and put your final answer within \\boxed{}.`
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* Chinese questions: `{question}\n请结合自然语言和Python程序语言来解答问题，并把最终答案放置于\\boxed{}中。`
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We provide an example of testing the DeepSeekMath-Base 7B using 8 GPUs.
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If you wish to use a different model or dataset, you can modify the configs in `submit_eval_jobs.py` and `configs/*test_configs.json`
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```
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python submit_eval_jobs.py --n-gpus 8
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```
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Wait for all processes to finish, and then run the following command to aggregate results from all processes
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```
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python summarize_results.py [--eval-atp]
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```
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where the option `--eval-atp` will invoke `unsafe_score_minif2f_isabelle.py` to evaluate the informal-to-formal proving results. Please make sure you have set up the [PISA](https://github.com/wellecks/lm-evaluation-harness/blob/minif2f-isabelle/docs/isabelle_setup.md) server before using this option.
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A summary of all evaluation results will be saved as `evaluation_results.json`
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## 4. Model Outputs
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We provide all model outputs in `outputs.zip`.
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