diff --git a/irt/binary_matrix.py b/irt/binary_matrix.py index 3842a63..d9d03de 100644 --- a/irt/binary_matrix.py +++ b/irt/binary_matrix.py @@ -4,51 +4,7148 @@ # English -irt_data = { 'polycoder_160M': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'codegen_6B_mono': [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], - 'phi_3_med': [1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0], - 'codegen_6B_multi': [0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'gpt_3.5_turbo_codes': [1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0], - 'polycoder_0.4B': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'codegen_350M_multi': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'codegen_2B_multi': [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'llama3_70B': [1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0], - 'codegen_2B_nl': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'polycoder_2.7B': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'mistral7B_0_2': [0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0], - 'mistral7B_0_1': [0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], - 'codegen_2B_mono': [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'codegen_6B_nl': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'gemma_2B': [0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'codegen_350M_nl': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'gemma_7B': [1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'codegen_350M_mono': [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'gpt_4': [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 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0, + 0, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "gemma_2B": [ + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 1, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 1, + 1, + 0, + 0, + 0, + 0, + 1, + 1, + 1, + 0, + 0, + 0, + 1, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "codegen_350M_nl": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, 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+ 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "codegen_350M_mono": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 1, + 1, + 0, + 0, + 0, + 1, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ], + "gpt_4": [ + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 1, + 0, + 1, + 0, + 1, + 1, + 0, + 1, + 1, + 0, + 0, + 0, + 1, + 1, + 0, + 0, + 1, + 0, + 0, + 1, + 1, + 0, + 1, + 1, + 1, + 1, + 1, + 0, + 1, + 1, + 0, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 0, + 0, + 1, + 1, + 0, + 1, + 0, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 0, + 1, + 0, + 0, + 1, + 1, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 0, + 0, + 1, + 0, + 1, + 1, + 1, + 0, + 1, + 0, + 1, + 0, + 1, + 0, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + ], + "mistral7B_0_3": [ + 1, + 0, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + 0, + 1, + 1, + 0, + 1, + 0, + 0, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 1, + 1, + 1, + 0, + 0, + 1, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 1, + 0, + 1, + 1, + 0, + 1, + 0, + 1, + 1, + 1, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 1, + 1, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 1, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 1, + 1, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 1, + 1, + 0, + 0, + 1, + 0, + 0, + 1, + 0, + 1, + 1, + 0, + 1, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + ], +} # Hinglish: -irt_data2 = {'gpt_3_5_turbo': [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0], - 'polycoder_160M': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'codegen_6B_mono': [0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], - 'phi_3_med': [1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0], - 'codegen_6B_multi': [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'codegen_350M_multi': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - 'polycoder_0_4B': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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--git a/irt/irt_eval.py b/irt/irt_eval.py index e7df6a1..100a998 100644 --- a/irt/irt_eval.py +++ b/irt/irt_eval.py @@ -6,16 +6,18 @@ current_dir = os.path.dirname(os.path.realpath(__file__)) + def compute_irt_params(irt_data: list): """ Compute the Item(problem) and User(model) parameters using the IRT approach. - :param irt_data: list : A list of tuples containing the model name, problem ID, + :param irt_data: list : A list of tuples containing the model name, problem ID, and the binary response of the model to the problem. :return: tuple : (item_param, user_param) """ return irt(irt_data) + def write_csv(file_path, data): """ Write data to a CSV file. @@ -27,6 +29,7 @@ def write_csv(file_path, data): writer = csv.writer(file) writer.writerows(data) + def process_irt_data(lang, irt_matrix, sanitized_code_dir): """ Process IRT data for a given language by extracting responses from models. @@ -41,15 +44,16 @@ def process_irt_data(lang, irt_matrix, sanitized_code_dir): for model in os.listdir(sanitized_code_dir): model = model.replace(".zip", "") if model.endswith(".zip") else model - if model in ["codegen2_1B", "santacoder"]: # Skip these 2 models. + if model in ["codegen2_1B", "santacoder"]: # Skip these 2 models. continue models.append(model) for pid in range(164): response = irt_matrix[model][pid] data.append((model, pid, response)) - + return data + def save_irt_results(output_name, item_param, user_param): """ Save IRT results (user parameters and item parameters) to CSV files. @@ -66,9 +70,11 @@ def save_irt_results(output_name, item_param, user_param): user_csv_data = [["MODEL", "VALUE"]] user_csv_data += sorted( [[model, f"{value:.3f}"] for model, value in user_param.items()], - key=lambda x: float(x[1]) + key=lambda x: float(x[1]), + ) + write_csv( + os.path.join(output_dir, f"{output_name}_irt_userparams.csv"), user_csv_data ) - write_csv(os.path.join(output_dir, f"{output_name}_irt_userparams.csv"), user_csv_data) # Save item parameters item_csv_data = [["PROBLEM", "DIFFICULTY", "DISCRIMINATION"]] @@ -76,13 +82,19 @@ def save_irt_results(output_name, item_param, user_param): [problem, f"{value['beta']:.3f}", f"{value['alpha']:.3f}"] for problem, value in item_param.items() ] - write_csv(os.path.join(output_dir, f"{output_name}_irt_itemparams.csv"), item_csv_data) + write_csv( + os.path.join(output_dir, f"{output_name}_irt_itemparams.csv"), item_csv_data + ) # Save sorted item parameters sorted_item_csv_data = [item_csv_data[0]] + sorted( item_csv_data[1:], key=lambda x: float(x[2]) ) - write_csv(os.path.join(output_dir, f"{output_name}_sorted_irt_itemparams.csv"), sorted_item_csv_data) + write_csv( + os.path.join(output_dir, f"{output_name}_sorted_irt_itemparams.csv"), + sorted_item_csv_data, + ) + def lang_irt(): """ @@ -90,11 +102,14 @@ def lang_irt(): User and item parameters are stored in CSV files. """ for lang, irt_matrix in [("English", irt_english), ("Hinglish", irt_hinglish)]: - sanitized_code_dir = os.path.join(current_dir, "..", "samples", lang, "sanitized") + sanitized_code_dir = os.path.join( + current_dir, "..", "samples", lang, "sanitized" + ) data = process_irt_data(lang, irt_matrix, sanitized_code_dir) item_param, user_param = compute_irt_params(data) save_irt_results(lang, item_param, user_param) + def combined_irt(): """ Compute IRT parameters for models in both Hinglish and English, evaluated jointly. @@ -102,12 +117,15 @@ def combined_irt(): """ data = [] for lang, irt_matrix in [("English", irt_english), ("Hinglish", irt_hinglish)]: - sanitized_code_dir = os.path.join(current_dir, "..", "samples", lang, "sanitized") + sanitized_code_dir = os.path.join( + current_dir, "..", "samples", lang, "sanitized" + ) data.extend(process_irt_data(lang, irt_matrix, sanitized_code_dir)) - + item_param, user_param = compute_irt_params(data) save_irt_results("Combined", item_param, user_param) + if __name__ == "__main__": """ Run the desired evaluation scripts. diff --git a/samples/codemodels/gemma.py b/samples/codemodels/gemma.py index 2a2f84f..2dd341e 100644 --- a/samples/codemodels/gemma.py +++ b/samples/codemodels/gemma.py @@ -9,19 +9,19 @@ # An access token from HUGGINGFACE is necessary to generate the code completions. HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN") -login(token=HUGGING_FACE_TOKEN) +login(token=HUGGING_FACE_TOKEN) -def prompt_output(prompt, words): +def prompt_output(prompt, words): input_ids = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**input_ids, max_new_tokens=words, do_sample=True) code_gen = tokenizer.decode(outputs[0]) ind = code_gen.find("") if ind == -1: - return code_gen[code_gen.index("") + 5 :] + return code_gen[code_gen.index("") + 5:] else: - return code_gen[code_gen.index("") + 5 : ind] + return code_gen[code_gen.index("") + 5:ind] if __name__ == "__main__": @@ -39,7 +39,8 @@ def prompt_output(prompt, words): data = json.load(f) for pid in range(1, len(data)): # Starting from 1 as per original code prompt = data[pid]["prompt"] - with open(os.path.join(gemma_codes_dir, f"{str(pid).zfill(3)}.py"), "w") as file: + with open( + os.path.join(gemma_codes_dir, f"{str(pid).zfill(3)}.py"), "w" + ) as file: file.write(prompt_output(prompt, 512)) print(f"done for {pid}") - diff --git a/samples/codemodels/gpt.py b/samples/codemodels/gpt.py index b71f990..8a45c25 100644 --- a/samples/codemodels/gpt.py +++ b/samples/codemodels/gpt.py @@ -8,6 +8,7 @@ OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") + def gpt(docstr): response = openai.ChatCompletion.create( model="gpt-3.5-turbo", # Change to "gpt-4" if necessary. Check out more models at https://platform.openai.com/docs/models @@ -29,6 +30,7 @@ def gpt(docstr): ) return response.choices[0].message.content + if __name__ == "__main__": base_dir = os.path.dirname(__file__) path_humaneval = os.path.join(base_dir, "HinglishEval.json") @@ -38,7 +40,9 @@ def gpt(docstr): for pid in [10, 32, 38, 50]: prompt = data[pid]["prompt"] with open( - os.path.join(base_dir, "gpt_3.5_turbo_codes", f"{str(pid).zfill(3)}.py"), + os.path.join( + base_dir, "gpt_3.5_turbo_codes", f"{str(pid).zfill(3)}.py" + ), "w", ) as file: file.write(gpt(prompt)) diff --git a/samples/codemodels/llama3.py b/samples/codemodels/llama3.py index 357a413..14e3f02 100644 --- a/samples/codemodels/llama3.py +++ b/samples/codemodels/llama3.py @@ -8,6 +8,7 @@ DEEPINFRA_API_KEY = os.getenv("DEEPINFRA_API_KEY") + def llama3(docstr): # Create an OpenAI client with your deepinfra token and endpoint openai = OpenAI( @@ -16,7 +17,7 @@ def llama3(docstr): ) chat_completion = openai.chat.completions.create( - model="meta-llama/Meta-Llama-3-70B-Instruct", ## MODEL NAME + model="meta-llama/Meta-Llama-3-70B-Instruct", # MODEL NAME messages=[ { "role": "system", @@ -42,25 +43,30 @@ def llama3(docstr): path_humaneval_hinglish = os.path.join(base_dir, "HinglishEval.json") path_humaneval_english = os.path.join(base_dir, "HumanEval.json") - unsanitized_hinglish_dir = os.path.join(base_dir,"Hinglish", "unsanitized", "llama3") + unsanitized_hinglish_dir = os.path.join( + base_dir, "Hinglish", "unsanitized", "llama3" + ) os.makedirs(unsanitized_hinglish_dir, exist_ok=True) with open(path_humaneval_hinglish) as f: data = json.load(f) for pid in range(59, 164): # Adjusted range for Hinglish prompt = data[pid]["prompt"] - with open(os.path.join(unsanitized_hinglish_dir, f"{str(pid).zfill(3)}.py"), "w") as file: + with open( + os.path.join(unsanitized_hinglish_dir, f"{str(pid).zfill(3)}.py"), "w" + ) as file: file.write(llama3(prompt)) print(f"done for {pid}") - unsanitized_english_dir = os.path.join(base_dir, "English", "unsanitized", "llama3") + unsanitized_english_dir = os.path.join(base_dir, "English", "unsanitized", "llama3") os.makedirs(unsanitized_english_dir, exist_ok=True) with open(path_humaneval_english) as f: data = json.load(f) for pid in range(164): # Adjusted range for English prompt = data[pid]["prompt"] - with open(os.path.join(unsanitized_english_dir, f"{str(pid).zfill(3)}.py"), "w") as file: + with open( + os.path.join(unsanitized_english_dir, f"{str(pid).zfill(3)}.py"), "w" + ) as file: file.write(llama3(prompt)) print(f"done for {pid}") - diff --git a/samples/codemodels/mistral7B.py b/samples/codemodels/mistral7B.py index 082346b..de3a337 100644 --- a/samples/codemodels/mistral7B.py +++ b/samples/codemodels/mistral7B.py @@ -8,6 +8,7 @@ DEEPINFRA_API_KEY = os.getenv("DEEPINFRA_API_KEY") + def mistral7b(docstr): # Create an OpenAI client with your deepinfra token and endpoint openai = OpenAI( @@ -16,7 +17,7 @@ def mistral7b(docstr): ) chat_completion = openai.chat.completions.create( - model="mistralai/Mistral-7B-Instruct-v0.3", ## MODEL NAME + model="mistralai/Mistral-7B-Instruct-v0.3", # MODEL NAME messages=[ { "role": "system", @@ -40,25 +41,33 @@ def mistral7b(docstr): if __name__ == "__main__": base_dir = os.path.dirname(__file__) path_humaneval_hinglish = os.path.join(base_dir, "HinglishEval.json") - unsanitized_hinglish_dir = os.path.join(base_dir, "Hinglish", "unsanitized", "mistral7b") + unsanitized_hinglish_dir = os.path.join( + base_dir, "Hinglish", "unsanitized", "mistral7b" + ) os.makedirs(unsanitized_hinglish_dir, exist_ok=True) with open(path_humaneval_hinglish) as f: data = json.load(f) for pid in range(164): prompt = data[pid]["prompt"] - with open(os.path.join(unsanitized_hinglish_dir, f"{str(pid).zfill(3)}.py"), "w") as file: + with open( + os.path.join(unsanitized_hinglish_dir, f"{str(pid).zfill(3)}.py"), "w" + ) as file: file.write(mistral7b(prompt)) print(f"done for {pid}") path_humaneval_english = os.path.join(base_dir, "HumanEval.json") - unsanitized_english_dir = os.path.join(base_dir, "English", "unsanitized", "mistral7b") + unsanitized_english_dir = os.path.join( + base_dir, "English", "unsanitized", "mistral7b" + ) os.makedirs(unsanitized_english_dir, exist_ok=True) with open(path_humaneval_english) as f: data = json.load(f) for pid in range(164): prompt = data[pid]["prompt"] - with open(os.path.join(unsanitized_english_dir, f"{str(pid).zfill(3)}.py"), "w") as file: + with open( + os.path.join(unsanitized_english_dir, f"{str(pid).zfill(3)}.py"), "w" + ) as file: file.write(mistral7b(prompt)) print(f"done for {pid}") diff --git a/samples/codemodels/phi-3-med.py b/samples/codemodels/phi-3-med.py index a3a3592..939e417 100644 --- a/samples/codemodels/phi-3-med.py +++ b/samples/codemodels/phi-3-med.py @@ -8,6 +8,7 @@ DEEPINFRA_API_KEY = os.getenv("DEEPINFRA_API_KEY") + def phigen(docstr): # Create an OpenAI client with your deepinfra token and endpoint openai = OpenAI( @@ -16,7 +17,7 @@ def phigen(docstr): ) chat_completion = openai.chat.completions.create( - model="microsoft/Phi-3-medium-4k-instruct", ## MODEL NAME + model="microsoft/Phi-3-medium-4k-instruct", # MODEL NAME messages=[ { "role": "system", @@ -40,25 +41,33 @@ def phigen(docstr): if __name__ == "__main__": base_dir = os.path.dirname(__file__) path_humaneval_hinglish = os.path.join(base_dir, "HinglishEval.json") - unsanitized_hinglish_dir = os.path.join(base_dir,"Hinglish","unsanitized", "phi-3-gen") + unsanitized_hinglish_dir = os.path.join( + base_dir, "Hinglish", "unsanitized", "phi-3-gen" + ) os.makedirs(unsanitized_hinglish_dir, exist_ok=True) with open(path_humaneval_hinglish) as f: data = json.load(f) for pid in range(164): prompt = data[pid]["prompt"] - with open(os.path.join(unsanitized_hinglish_dir, f"{str(pid).zfill(3)}.py"), "w") as file: + with open( + os.path.join(unsanitized_hinglish_dir, f"{str(pid).zfill(3)}.py"), "w" + ) as file: file.write(phigen(prompt)) print(f"done for {pid}") path_humaneval_english = os.path.join(base_dir, "HumanEval.json") - unsanitized_english_dir = os.path.join(base_dir, "English", "unsanitized", "phi-3-gen") + unsanitized_english_dir = os.path.join( + base_dir, "English", "unsanitized", "phi-3-gen" + ) os.makedirs(unsanitized_english_dir, exist_ok=True) with open(path_humaneval_english) as f: data = json.load(f) for pid in range(164): prompt = data[pid]["prompt"] - with open(os.path.join(unsanitized_english_dir, f"{str(pid).zfill(3)}.py"), "w") as file: + with open( + os.path.join(unsanitized_english_dir, f"{str(pid).zfill(3)}.py"), "w" + ) as file: file.write(phigen(prompt)) print(f"done for {pid}") diff --git a/samples/codemodels/polycoder.py b/samples/codemodels/polycoder.py index 468590b..010d9a2 100644 --- a/samples/codemodels/polycoder.py +++ b/samples/codemodels/polycoder.py @@ -16,7 +16,7 @@ def prompt_output(prompt, words): def sanitize(completion, prompt): - generated = completion[len(prompt) :] + generated = completion[len(prompt):] lines = generated.split("\n") final_lines = [] for line in lines: @@ -28,7 +28,6 @@ def sanitize(completion, prompt): if __name__ == "__main__": - base_dir = os.path.dirname(__file__) path_humaneval = os.path.join(base_dir, "HinglishEval.json") @@ -37,7 +36,7 @@ def sanitize(completion, prompt): with open(path_humaneval) as f: data = json.load(f) - for pid in range(164): # 10,32,38, 50 are special + for pid in range(164): # 10,32,38, 50 are special prompt = data[pid]["prompt"] with open( f"/path/to/polycoder_0.4B_codes/{str(pid).zfill(3)}.py", "w" @@ -51,7 +50,7 @@ def sanitize(completion, prompt): with open(path_humaneval) as f: data = json.load(f) - for pid in range(164): # 10,32,38, 50 are special + for pid in range(164): # 10,32,38, 50 are special prompt = data[pid]["prompt"] with open( f"/path/to/polycoder_2.7B_codes/{str(pid).zfill(3)}.py", "w" @@ -64,7 +63,7 @@ def sanitize(completion, prompt): with open(path_humaneval) as f: data = json.load(f) - for pid in range(164): # 10,32,38, 50 are special + for pid in range(164): # 10,32,38, 50 are special prompt = data[pid]["prompt"] with open( f"/path/to/polycoder_160M_codes/{str(pid).zfill(3)}.py", "w" @@ -72,4 +71,3 @@ def sanitize(completion, prompt): completion = prompt_output(prompt, 512) file.write(sanitize(completion, prompt)) print(f"done for {pid}") - diff --git a/samples/codemodels/santacoder.py b/samples/codemodels/santacoder.py index 65d5549..6ca5865 100644 --- a/samples/codemodels/santacoder.py +++ b/samples/codemodels/santacoder.py @@ -10,7 +10,7 @@ def sanitize(completion, prompt): - generated = completion[len(prompt) :] + generated = completion[len(prompt):] lines = generated.split("\n") final_lines = [] for line in lines: @@ -39,8 +39,10 @@ def santa_coder(prompt): with open(path_humaneval) as f: data = json.load(f) - for pid in range (164): # numbers in [10, 32, 38, 50] are special probs + for pid in range(164): # numbers in [10, 32, 38, 50] are special probs prompt = data[pid]["prompt"] - with open(os.path.join(santacoder_codes_dir, f"{str(pid).zfill(3)}.py"), "w") as file: + with open( + os.path.join(santacoder_codes_dir, f"{str(pid).zfill(3)}.py"), "w" + ) as file: file.write(santa_coder(prompt)) print(f"done for {pid}") diff --git a/samples/tools/eval.py b/samples/tools/eval.py index dc1f72b..c875702 100644 --- a/samples/tools/eval.py +++ b/samples/tools/eval.py @@ -2,6 +2,7 @@ import os from execution import check_correctness + def evaluate_model(model_name: str) -> float: """ Evaluates the pass@1 score of the model. @@ -9,16 +10,17 @@ def evaluate_model(model_name: str) -> float: score = 0 for problem in data: task_id = str.zfill(problem["task_id"].split("/")[-1], 3) - + # get completion from samples/sanitized/{model_name}/{task_id}.py with open(f"samples/sanitized/{model_name}/{task_id}.py") as f: completion = f.read() - + result = check_correctness(problem, completion, 3.0) score += 1 if result["passed"] else 0 - + return score / len(data) + def evaluate_problemwise(model_name: str) -> list: """ Evaluates problemwise performance of the model. (Function to get data required to run IRT ranking of problems). @@ -26,34 +28,33 @@ def evaluate_problemwise(model_name: str) -> list: performance_data = [] for problem in data: task_id = str.zfill(problem["task_id"].split("/")[-1], 3) - + # get completion from samples/sanitized/{model_name}/{task_id}.py with open(f"samples/sanitized/{model_name}/{task_id}.py") as f: completion = f.read() - + result = check_correctness(problem, completion, 3.0) - - if(result["passed"]): + + if result["passed"]: performance_data.append(1) else: performance_data.append(0) - + return performance_data if __name__ == "__main__": with open("HinglishEval.json") as f: data = json.load(f) - + # get all directories in samples/sanitized results = {} performance_data = {} for model_name in os.listdir("samples/sanitized"): results[model_name] = evaluate_model(model_name) performance_data[model_name] = evaluate_problemwise(model_name) - + # sort the results and print them sorted_results = sorted(results.items(), key=lambda x: x[1], reverse=True) print(sorted_results) print(performance_data) - diff --git a/samples/tools/execution.py b/samples/tools/execution.py index a8c9c6c..b3ae544 100644 --- a/samples/tools/execution.py +++ b/samples/tools/execution.py @@ -11,23 +11,23 @@ from typing import Dict, Optional -def check_correctness(problem: Dict, completion: str, timeout: float, - completion_id: Optional[int] = None) -> Dict: +def check_correctness( + problem: Dict, completion: str, timeout: float, completion_id: Optional[int] = None +) -> Dict: """ Evaluates the functional correctness of a completion by running the test - suite provided in the problem. + suite provided in the problem. :param completion_id: an optional completion ID so we can match the results later even if execution finishes asynchronously. """ def unsafe_execute(): - with create_tempdir(): - # These system calls are needed when cleaning up tempdir. import os import shutil + rmtree = shutil.rmtree rmdir = os.rmdir chdir = os.chdir @@ -36,26 +36,26 @@ def unsafe_execute(): reliability_guard() # Construct the check program and run it. - check_program = ( - completion + "\n" + - problem["test"] + "\n" + + check_program = "\n".join([ + completion, + problem["test"], f"check({problem['entry_point']})" - ) + ]) try: exec_globals = {} with swallow_io(): with time_limit(timeout): -# WARNING -# This program exists to execute untrusted model-generated code. Although -# it is highly unlikely that model-generated code will do something overtly -# malicious in response to this test suite, model-generated code may act -# destructively due to a lack of model capability or alignment. -# Users are strongly encouraged to sandbox this evaluation suite so that it -# does not perform destructive actions on their host or network. For more -# information on how OpenAI sandboxes its code, see the accompanying paper. -# Once you have read this disclaimer and taken appropriate precautions, -# uncomment the following line and proceed at your own risk: + # WARNING + # This program exists to execute untrusted model-generated code. Although + # it is highly unlikely that model-generated code will do something overtly + # malicious in response to this test suite, model-generated code may act + # destructively due to a lack of model capability or alignment. + # Users are strongly encouraged to sandbox this evaluation suite so that it + # does not perform destructive actions on their host or network. For more + # information on how OpenAI sandboxes its code, see the accompanying paper. + # Once you have read this disclaimer and taken appropriate precautions, + # uncomment the following line and proceed at your own risk: exec(check_program, exec_globals) result.append("passed") except TimeoutException: @@ -92,6 +92,7 @@ def unsafe_execute(): def time_limit(seconds: float): def signal_handler(signum, frame): raise TimeoutException("Timed out!") + signal.setitimer(signal.ITIMER_REAL, seconds) signal.signal(signal.SIGALRM, signal_handler) try: @@ -121,7 +122,7 @@ class TimeoutException(Exception): class WriteOnlyStringIO(io.StringIO): - """ StringIO that throws an exception when it's read from """ + """StringIO that throws an exception when it's read from""" def read(self, *args, **kwargs): raise IOError @@ -133,12 +134,12 @@ def readlines(self, *args, **kwargs): raise IOError def readable(self, *args, **kwargs): - """ Returns True if the IO object can be read. """ + """Returns True if the IO object can be read.""" return False class redirect_stdin(contextlib._RedirectStream): # type: ignore - _stream = 'stdin' + _stream = "stdin" @contextlib.contextmanager @@ -164,26 +165,35 @@ def reliability_guard(maximum_memory_bytes: Optional[int] = None): WARNING This function is NOT a security sandbox. Untrusted code, including, model- - generated code, should not be blindly executed outside of one. See the + generated code, should not be blindly executed outside of one. See the Codex paper for more information about OpenAI's code sandbox, and proceed with caution. """ if maximum_memory_bytes is not None: import resource - resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes)) - resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes)) - if not platform.uname().system == 'Darwin': - resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes)) + + resource.setrlimit( + resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes) + ) + resource.setrlimit( + resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes) + ) + if not platform.uname().system == "Darwin": + resource.setrlimit( + resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes) + ) faulthandler.disable() import builtins + builtins.exit = None builtins.quit = None import os - os.environ['OMP_NUM_THREADS'] = '1' + + os.environ["OMP_NUM_THREADS"] = "1" os.kill = None os.system = None @@ -214,18 +224,21 @@ def reliability_guard(maximum_memory_bytes: Optional[int] = None): os.chdir = None import shutil + shutil.rmtree = None shutil.move = None shutil.chown = None import subprocess + subprocess.Popen = None # type: ignore - __builtins__['help'] = None + __builtins__["help"] = None import sys - sys.modules['ipdb'] = None - sys.modules['joblib'] = None - sys.modules['resource'] = None - sys.modules['psutil'] = None - sys.modules['tkinter'] = None + + sys.modules["ipdb"] = None + sys.modules["joblib"] = None + sys.modules["resource"] = None + sys.modules["psutil"] = None + sys.modules["tkinter"] = None diff --git a/samples/tools/sanitize.py b/samples/tools/sanitize.py index 17ee1cd..4380801 100644 --- a/samples/tools/sanitize.py +++ b/samples/tools/sanitize.py @@ -14,8 +14,10 @@ def sanitize1(completion, entry_point): def logfile(model): # Adjusted to dynamically construct the path using __file__ base_dir = os.path.dirname(__file__) - log_path = os.path.join(base_dir, "samples", "English", "sanitized", model, "log.txt") - + log_path = os.path.join( + base_dir, "samples", "English", "sanitized", model, "log.txt" + ) + with open(log_path, "r") as f: file_lines = f.readlines() error = [] @@ -29,7 +31,7 @@ def logfile(model): def sanitize2(completion, prompt): - generated = completion[len(prompt) :] + generated = completion[len(prompt):] lines = generated.split("\n") final_lines = [] for line in lines: @@ -42,16 +44,16 @@ def sanitize2(completion, prompt): if __name__ == "__main__": import json import os + models = ["gpt_4_codes", "gpt_3.5_turbo_codes"] base_dir = os.path.dirname(__file__) path_humaneval = os.path.join(base_dir, "HinglishEval.json") - + for model in models: - sanitized_codes_dir = os.path.join(base_dir, "samples", "Hinglish", "sanitized", model) - try: - os.makedirs(sanitized_codes_dir, exist_ok=True) - except: - pass + sanitized_codes_dir = os.path.join( + base_dir, "samples", "Hinglish", "sanitized", model + ) + os.makedirs(sanitized_codes_dir, exist_ok=True) with open(path_humaneval) as f: data = json.load(f) @@ -60,19 +62,25 @@ def sanitize2(completion, prompt): prompt = data[pid]["prompt"] entry_point = data[pid]["entry_point"] path_codegen = os.path.join(base_dir, model) - sanitized_file_path = os.path.join(sanitized_codes_dir, f"{str(pid).zfill(3)}.py") - + sanitized_file_path = os.path.join( + sanitized_codes_dir, f"{str(pid).zfill(3)}.py" + ) + if pid not in error: - code_file_path = os.path.join(path_codegen, f"{str(pid).zfill(3)}.py") + code_file_path = os.path.join( + path_codegen, f"{str(pid).zfill(3)}.py" + ) with open(code_file_path) as file: code = file.read() with open(sanitized_file_path, "w") as file: file.write(sanitize1(code, entry_point)) print(f"done for {pid}") else: - code_file_path = os.path.join(path_codegen, f"{str(pid).zfill(3)}.py") + code_file_path = os.path.join( + path_codegen, f"{str(pid).zfill(3)}.py" + ) with open(code_file_path) as file: code = file.read() with open(sanitized_file_path, "w") as file: file.write(sanitize2(code, prompt)) - print(f"done for {pid}") + print(f"done for {pid}") diff --git a/samples/tools/sanitize_gpt.py b/samples/tools/sanitize_gpt.py index 78bc42a..2d16986 100644 --- a/samples/tools/sanitize_gpt.py +++ b/samples/tools/sanitize_gpt.py @@ -3,8 +3,8 @@ def sanitize1(completion, entry_point): - anchor=completion.find(f"def {entry_point}") - x=completion.find("\n",anchor) + anchor = completion.find(f"def {entry_point}") + x = completion.find("\n", anchor) generated = completion[x:] lines = generated.split("\n") final_lines = [] @@ -12,21 +12,24 @@ def sanitize1(completion, entry_point): if line.strip() and line[0].strip(): break final_lines.append(line) - return completion[:x+1] + "\n".join([x for x in final_lines if x.strip()]) + return completion[: x + 1] + "\n".join([x for x in final_lines if x.strip()]) + def sanitize2(completion, entry_point): - ind=completion.find("```python") - completion=completion[ind+9:] - return sanitize1(completion,entry_point) + ind = completion.find("```python") + completion = completion[ind + 9:] + return sanitize1(completion, entry_point) + if __name__ == "__main__": model_list = ["gpt_4_codes", "gpt_3.5_turbo_codes"] base_dir = os.path.dirname(__file__) path_humaneval = os.path.join(base_dir, "HinglishEval.json") - + for model in model_list: - - sanitized_codes_dir = os.path.join(base_dir, "samples", "Hinglish", "sanitized", model) + sanitized_codes_dir = os.path.join( + base_dir, "samples", "Hinglish", "sanitized", model + ) try: os.makedirs(sanitized_codes_dir, exist_ok=True) except Exception as e: @@ -36,11 +39,13 @@ def sanitize2(completion, entry_point): with open(path_humaneval) as f: data = json.load(f) for pid in range(len(data)): - entry_point=data[pid]["entry_point"] + entry_point = data[pid]["entry_point"] with open(f"{path_codegen}/{str(pid).zfill(3)}.py") as file: code = file.read() - - sanitized_file_path = os.path.join(sanitized_codes_dir, f"{str(pid).zfill(3)}.py") + + sanitized_file_path = os.path.join( + sanitized_codes_dir, f"{str(pid).zfill(3)}.py" + ) with open(sanitized_file_path, "w") as file: file.write(sanitize2(code, entry_point)) print(f"done for {pid}") diff --git a/samples/tools/sanitize_llama3.py b/samples/tools/sanitize_llama3.py index 999a5eb..66f2150 100644 --- a/samples/tools/sanitize_llama3.py +++ b/samples/tools/sanitize_llama3.py @@ -20,7 +20,7 @@ def sanitize2(completion, entry_point): for typ in ["```python", "```"]: if typ in completion: ind = completion.find(typ) - completion = completion[ind + len(typ) :] + completion = completion[ind + len(typ):] break return sanitize1(completion, entry_point) @@ -31,31 +31,35 @@ def sanitize2(completion, entry_point): path_humaneval = os.path.join(base_dir, "HinglishEval.json") for model in model_list: - - sanitized_codes_dir = os.path.join(base_dir, "samples", "Hinglish", "sanitized", model) + sanitized_codes_dir = os.path.join( + base_dir, "samples", "Hinglish", "sanitized", model + ) try: os.makedirs(sanitized_codes_dir, exist_ok=True) except Exception as e: print("Error: ", e) - path_codegen = os.path.join(base_dir, model) with open(path_humaneval) as f: data = json.load(f) for pid in range(len(data)): - entry_point=data[pid]["entry_point"] + entry_point = data[pid]["entry_point"] with open(f"{path_codegen}/{str(pid).zfill(3)}.py") as file: code = file.read() - sanitized_file_path = os.path.join(sanitized_codes_dir, f"{str(pid).zfill(3)}.py") + sanitized_file_path = os.path.join( + sanitized_codes_dir, f"{str(pid).zfill(3)}.py" + ) with open(sanitized_file_path, "w") as file: file.write(sanitize2(code, entry_point)) print(f"done for {pid}") path_humaneval = os.path.join(base_dir, "HumanEval.json") - for model in model_list: - sanitized_codes_dir = os.path.join(base_dir, "samples", "English", "sanitized", model) + for model in model_list: + sanitized_codes_dir = os.path.join( + base_dir, "samples", "English", "sanitized", model + ) try: os.makedirs(sanitized_codes_dir, exist_ok=True) except Exception as e: @@ -65,11 +69,13 @@ def sanitize2(completion, entry_point): with open(path_humaneval) as f: data = json.load(f) for pid in range(len(data)): - entry_point=data[pid]["entry_point"] + entry_point = data[pid]["entry_point"] with open(f"{path_codegen}/{str(pid).zfill(3)}.py") as file: code = file.read() - sanitized_file_path = os.path.join(sanitized_codes_dir, f"{str(pid).zfill(3)}.py") + sanitized_file_path = os.path.join( + sanitized_codes_dir, f"{str(pid).zfill(3)}.py" + ) with open(sanitized_file_path, "w") as file: file.write(sanitize2(code, entry_point)) print(f"done for {pid}") diff --git a/translation/dump.py b/translation/dump.py index 53e343c..a3abad4 100644 --- a/translation/dump.py +++ b/translation/dump.py @@ -2,7 +2,7 @@ def extract_prompt_from_file(file_path): - with open(file_path, 'r') as file: + with open(file_path, "r") as file: content = file.read() # Split the content using triple double quotes parts = content.split('"""') @@ -11,32 +11,35 @@ def extract_prompt_from_file(file_path): def hinglish_json(): # File path to the JSON file - file_path = '../HumanEval.json' - with open(file_path, 'r') as json_file: + file_path = "../HumanEval.json" + with open(file_path, "r") as json_file: data = json.load(json_file) # modify the data with the hinglish prompts new_data, index = [], 0 - for item in data: - i = item['task_id'].split('/')[1] - prompt = extract_prompt_from_file(f'drafts/{i}') # original prompt - - prev_prompt = item['prompt'] # changing only the docstring not the function signature - entry_point = item['entry_point'] + for item in data: + i = item["task_id"].split("/")[1] + prompt = extract_prompt_from_file(f"drafts/{i}") # original prompt + + prev_prompt = item[ + "prompt" + ] # changing only the docstring not the function signature + entry_point = item["entry_point"] if '"""' in prev_prompt: index = prev_prompt.index('"""', prev_prompt.index(entry_point)) elif "'''" in prev_prompt: index = prev_prompt.index("'''", prev_prompt.index(entry_point)) - #overwriting the orginal prompt. also if the new docstring is longer than the slice it isnt an issue as the string length is changed in-place - item['prompt'] = item['prompt'][:index] + prompt - + # overwriting the orginal prompt. also if the new docstring is longer than the slice it isnt an issue as the string length is changed in-place + item["prompt"] = item["prompt"][:index] + prompt + new_data.append(item) - #write the new data to the json file - with open("../HinglishEval.json", 'w') as json_file: + # write the new data to the json file + with open("../HinglishEval.json", "w") as json_file: json.dump(data, json_file) + if __name__ == "__main__": hinglish_json()