--- license: mit task_categories: - text-generation language: - ru - en tags: - code size_categories: - n<1K --- # HumanEval_ru Dataset ## Dataset Summary This is a version of Code Geneneration [HumanEval dataset](https://huggingface.co/datasets/openai_humaneval) translated to Russian. ## Supported tasks The task is to generate body of the function based on the function signature and docstring. The programming problems are written in Python and contain Russian natural text in comments and docstrings. ## Task example ```python from typing import List def string_xor(a: str, b: str) -> str: """ Входными данными являются две строки a и b, состоящие только из 1 и 0. Выполните двоичное XOR для этих входных данных и верните результат также в виде строки. >>> string_xor('010', '110') '100' """ # Your code here ``` ## Dataset structure Please refer to the structure of the [original HumanEval dataset](https://huggingface.co/datasets/openai_humaneval) ## Translation Textual descriptions of tasks were translated automatically via Yandex.Translate API and then manually edited. Feel free to report errors in translations. # Usage ## Load dataset ```python from datasets import load_dataset load_dataset('NLPCoreTeam/humaneval_ru') DatasetDict({ train: Dataset({ features: ['task_id', 'prompt', 'canonical_solution', 'test', 'entry_point', 'signature', 'docstring', 'context', 'instruction', 'instruction_noexamples'], num_rows: 164 }) }) ``` ## How to evaluate your models To evaluate code generation capabilities of your models on HumanEval_ru please follow these steps (example is for [Codellama-7b-Python](https://huggingface.co/codellama/CodeLlama-7b-Python-hf)): 1. Clone https://github.com/NLP-Core-Team/bigcode-evaluation-harness 2. Run evaluation (WARNING: generated code is executed, it may be unsafe) with the following command ```console # mkdir -p ./outs/humaneval_ru # mkdir -p ./results/humaneval_ru accelerate launch main.py \ --model codellama/CodeLlama-7b-Python-hf \ --max_length_generation 512 \ --tasks humaneval_ru \ --use_auth_token \ --temperature 0.2 \ --n_samples 20 \ --precision fp16 \ --batch_size 1 \ --allow_code_execution \ --save_generations_path ./outs/humaneval_ru/codellama-7b-py.json \ --metric_output_path ./results/humaneval_ru/codellama-7b-py.metrics ``` 4. Resulting metrics of Codellama-7b-Python should be ```python "humaneval_ru": { "pass@1": 0.35, "pass@10": 0.5122803695209872 }, ``` # Benchmark [Starcoder](https://huggingface.co/bigcode/starcoder) and [Codellama](https://huggingface.co/codellama/CodeLlama-7b-hf) models evaluations on HumanEval_Ru and HumanEval are presented in the table below. For further information on Pass@1 and Pass@10 please refer to [original paper](https://arxiv.org/abs/2107.03374). | model | RU Pass@1 | RU Pass@10 | EN Pass@1 | EN Pass@10 | |:------------------------|--------------------------:|---------------------------:|--------------------------:|---------------------------:| | starcoderbase-1b | 0.1420 | 0.1801 | 0.1509 | 0.2045 | | starcoderbase-3b | 0.1924 | 0.2606 | 0.2137 | 0.3289 | | starcoderbase-7b | 0.2515 | 0.3359 | 0.2868 | 0.3852 | | starcoderbase-15b | 0.2676 | 0.3872 | 0.3036 | 0.4611 | | starcoder-15b-Python | 0.3103 | 0.4132 | 0.3353 | 0.4931 | | CodeLlama-7b-hf | 0.2673 | 0.3688 | 0.2975 | 0.4351 | | CodeLlama-7b-Python-hf | 0.3500 | 0.5122 | 0.3960 | 0.5761 | | CodeLlama-13b-hf | 0.3380 | 0.4884 | 0.3557 | 0.5489 | | CodeLlama-13b-Python-hf | 0.4380 | 0.5796 | 0.4301 | 0.6226 |
Script to reproduce the results in the table ```console #!/bin/bash # use with https://github.com/NLP-Core-Team/bigcode-evaluation-harness # RU mkdir -p ./outs/humaneval_ru mkdir -p ./results/humaneval_ru MODELS_PATH="bigcode" echo $MODELS_PATH declare -A bs=( ["starcoderbase-1b"]=16 ["starcoderbase-3b"]=8 ["starcoderbase-7b"]=4 ["starcoderbase"]=1 ["starcoder"]=1) for model_name in starcoderbase-1b starcoderbase-3b starcoderbase-7b starcoderbase starcoder do echo $MODELS_PATH/$model_name accelerate launch --mixed_precision="fp16" main.py \ --model $MODELS_PATH/$model_name \ --max_length_generation 512 \ --tasks humaneval_ru \ --use_auth_token \ --temperature 0.2 \ --n_samples 20 \ --precision fp16 \ --batch_size ${bs[$model_name]} \ --allow_code_execution \ --save_generations_path ./outs/humaneval_ru/$model_name.json \ --metric_output_path ./results/humaneval_ru/$model_name.metrics done MODELS_PATH="codellama" echo $MODELS_PATH declare -A bs=( ["CodeLlama-7b-Python-hf"]=8 ["CodeLlama-7b-hf"]=16 ["CodeLlama-13b-Python-hf"]=4 ["CodeLlama-13b-hf"]=4 ) for model_name in CodeLlama-7b-hf.rst.imdeLlama-7b-Python-hf.rst.imdeLlama-13b-hf.rst.imdeLlama-13b-Python-hf do echo $MODELS_PATH/$model_name accelerate launch --mixed_precision="fp16" main.py \ --model $MODELS_PATH/$model_name \ --max_length_generation 512 \ --tasks humaneval_ru \ --use_auth_token \ --temperature 0.2 \ --n_samples 20 \ --precision fp16 \ --batch_size ${bs[$model_name]} \ --allow_code_execution \ --save_generations_path ./outs/humaneval_ru/$model_name.json \ --metric_output_path ./results/humaneval_ru/$model_name.metrics done # EN mkdir -p ./outs/humaneval mkdir -p ./results/humaneval MODELS_PATH="bigcode" echo $MODELS_PATH declare -A bs=( ["starcoderbase-1b"]=16 ["starcoderbase-3b"]=8 ["starcoderbase-7b"]=4 ["starcoderbase"]=1 ["starcoder"]=1) for model_name in starcoderbase-1b starcoderbase-3b starcoderbase-7b starcoderbase starcoder do echo $MODELS_PATH/$model_name accelerate launch --mixed_precision="fp16" main.py \ --model $MODELS_PATH/$model_name \ --max_length_generation 512 \ --tasks humaneval \ --use_auth_token \ --temperature 0.2 \ --n_samples 20 \ --precision fp16 \ --batch_size ${bs[$model_name]} \ --allow_code_execution \ --save_generations_path ./outs/humaneval/$model_name.json \ --metric_output_path ./results/humaneval/$model_name.metrics done MODELS_PATH="codellama" echo $MODELS_PATH declare -A bs=( ["CodeLlama-7b-Python-hf"]=8 ["CodeLlama-7b-hf"]=16 ["CodeLlama-13b-Python-hf"]=4 ["CodeLlama-13b-hf"]=4 ) for model_name in CodeLlama-7b-hf.rst.imdeLlama-7b-Python-hf.rst.imdeLlama-13b-hf.rst.imdeLlama-13b-Python-hf do echo $MODELS_PATH/$model_name accelerate launch --mixed_precision="fp16" main.py \ --model $MODELS_PATH/$model_name \ --max_length_generation 512 \ --tasks humaneval \ --use_auth_token \ --temperature 0.2 \ --n_samples 20 \ --precision fp16 \ --batch_size ${bs[$model_name]} \ --allow_code_execution \ --save_generations_path ./outs/humaneval/$model_name.json \ --metric_output_path ./results/humaneval/$model_name.metrics done ```