import pandas as pd from utils import model_hyperlink def add_model_readme(df): # write model ids to README.md with open("README.md", "r") as f: lines = f.readlines() links = df["Links"].astype(str) for link in links: try: model_id = link.split(".co/")[1] # verify line doesn't exist if f"- {model_id}\n" in lines: continue lines.insert(-1, f"- {model_id}\n") except IndexError: print(f"link {link} is not valid") with open("README.md", "w") as f: f.writelines(lines) df = pd.read_csv("data/raw_scores.csv") COLS = df.columns.to_list() # add column models_query with same values a smodels at the end of columns df.insert(len(COLS), "models_query", df["Model"]) print(f"all cols {df.columns.to_list()}") # average score mean_columns = df.iloc[:,5:-3] # print cols in mean_columns print("cols", mean_columns.columns.to_list()) df.insert(len(mean_columns.columns.to_list()), "Average score", mean_columns.mean(axis=1).round(2)) # add win rate columns for each language old_size = len(df.columns) for col in df.columns[6:-2]: df[col + " rank"] = df[col].rank(ascending=False) df[col + " rank"] = len(df) - (df[col + " rank"] - 1) df["Win Rate"] = df.iloc[:, old_size:].mean(axis=1).round(2) df = df.drop(df.columns[old_size:-1], axis=1) df = df[["Model", "Size (B)", "Win Rate"] + df.columns[2:-1].tolist()] # sort with regard to column win rate df = df.sort_values(by=["Win Rate"], ascending=False) # add column with model links as https://huggingface.co/WizardLM/WizardCoder-15B-V1.0, https://huggingface.co/bigcode/starcoder, https://huggingface.co/bigcode/starcoderbase, https://huggingface.co/bigcode/starcoderbase-7b, # https://huggingface.co/bigcode/starcoderbase-3b, https://huggingface.co/bigcode/starcoderbase-1b, https://huggingface.co/bigcode/santacoder, https://huggingface.co/replit/replit-code-v1-3b, https://huggingface.co/THUDM/codegeex2-6b links = { "WizardCoder-15B-V1.0": "https://huggingface.co/WizardLM/WizardCoder-15B-V1.0", "WizardCoder-3B-V1.0": "https://huggingface.co/WizardLM/WizardCoder-3B-V1.0", "WizardCoder-1B-V1.0": "https://huggingface.co/WizardLM/WizardCoder-1B-V1.0", "WizardCoder-Python-34B-V1.0": "https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0", "WizardCoder-Python-13B-V1.0": "https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0", "OctoCoder-15B": "https://huggingface.co/bigcode/octocoder", "OctoGeeX-7B": "https://huggingface.co/bigcode/octogeex", "StableCode-3B-alpha": "https://huggingface.co/stabilityai/stablecode-completion-alpha-3b", "StarCoder2-15B": "https://huggingface.co/bigcode/starcoder2-15b", "StarCoder2-7B": "https://huggingface.co/bigcode/starcoder2-7b", "StarCoder2-3B": "https://huggingface.co/bigcode/starcoder2-3b", "StarCoder-15B": "https://huggingface.co/bigcode/starcoder", "StarCoderBase-15B": "https://huggingface.co/bigcode/starcoderbase", "StarCoderBase-7B": "https://huggingface.co/bigcode/starcoderbase-7b", "StarCoderBase-3B": "https://huggingface.co/bigcode/starcoderbase-3b", "StarCoderBase-1.1B": "https://huggingface.co/bigcode/starcoderbase-1b", "SantaCoder-1.1B": "https://huggingface.co/bigcode/santacoder", "Replit-2.7B": "https://huggingface.co/replit/replit-code-v1-3b", "CodeGeex2-6B": "https://huggingface.co/THUDM/codegeex2-6b", "CodeGen25-7B-multi": "https://huggingface.co/Salesforce/codegen25-7b-multi", "CodeGen25-7B-mono": "https://huggingface.co/Salesforce/codegen25-7b-mono", "CodeGen-16B-Multi": "https://huggingface.co/Salesforce/codegen-16B-multi", "DeciCoder-1B": "https://huggingface.co/Deci/DeciCoder-1b", "Phind-CodeLlama-34B-v1": "https://huggingface.co/phind/Phind-CodeLlama-34B-v1", "Phind-CodeLlama-34B-Python-v1": "https://huggingface.co/phind/Phind-CodeLlama-34B-Python-v1", "Phind-CodeLlama-34B-v2": "https://huggingface.co/phind/Phind-CodeLlama-34B-v2", "Falcon-180B": "https://huggingface.co/tiiuae/falcon-180B", "Refact-1.6B": "https://huggingface.co/smallcloudai/Refact-1_6B-fim", "Phi-1": "https://huggingface.co/microsoft/phi-1", "CodeShell-7B": "https://huggingface.co/WisdomShell/CodeShell-7B", "DeepSeek-Coder-1b-base": "https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base", "DeepSeek-Coder-7b-base": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", "DeepSeek-Coder-33b-base": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-base", "DeepSeek-Coder-7b-instruct": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct", "DeepSeek-Coder-33b-instruct": "https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct", "CodeFuse-DeepSeek-33b": "https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B", "Stable-code-3b": "https://huggingface.co/stabilityai/stable-code-3b", "OpenCodeInterpreter-DS-33B": "https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-33B", "OpenCodeInterpreter-DS-6.7B": "https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-6.7B", "CodeGemma-2B": "https://huggingface.co/google/codegemma-2b", "CodeGemma-7B": "https://huggingface.co/google/codegemma-7b", "CodeGemma-7B-it": "https://huggingface.co/google/codegemma-7b-it", "CodeQwen1.5-7B": "https://huggingface.co/Qwen/CodeQwen1.5-7B", "CodeQwen1.5-7B-Chat": "https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat", "Nxcode-CQ-7B-orpo": "https://huggingface.co/NTQAI/Nxcode-CQ-7B-orpo", } codellamas = ['CodeLlama-7b', 'CodeLlama-7b-Python', 'CodeLlama-7b-Instruct', 'CodeLlama-13b', 'CodeLlama-13b-Python', 'CodeLlama-13b-Instruct', 'CodeLlama-34b', 'CodeLlama-34b-Python', 'CodeLlama-34b-Instruct', 'CodeLlama-70b', 'CodeLlama-70b-Python', 'CodeLlama-70b-Instruct'] for codellama in codellamas: links[codellama] = f"https://huggingface.co/codellama/{codellama}-hf" df["Links"] = df["Model"].map(links) df.insert(0, "T", "🟢") patterns = ["WizardCoder", "Octo", "Instruct", "Phind", "Refact", "CodeGemma-7B-it", "Chat"] df.loc[df["Model"].str.contains('|'.join(patterns)), "T"] = "🔶" df.loc[df["Model"].str.contains('|'.join(patterns)), "T"] = "🔶" df.loc[df["Model"].str.contains('|'.join(["CodeShell", "DeepSeek-Coder-7b-instruct", "DeepSeek-Coder-33b-instruct", "CodeFuse", "OpenCodeInterpreter", "Nxcode-CQ-7B-orpo"])), "T"] = "🔶 EXT" df.loc[df["Model"].str.contains('|'.join(["Stable-Code-3b", "DeepSeek-Coder-1b-base", "DeepSeek-Coder-33b-base", "DeepSeek-Coder-7b-base"])), "T"] = "🟢 EXT" # add clumn submission_pr with empty fiels except for CodeShell with link AA df["Submission PR"] = "" df.loc[df["Model"].str.contains('|'.join(["CodeShell"])), "Submission PR"] = "https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard/discussions/16" df.loc[df["Model"].str.contains('|'.join(["DeepSeek-Coder-1b-base"])), "Submission PR"] = "https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard/discussions/33" df.loc[df["Model"].str.contains('|'.join(["DeepSeek-Coder-7b-base"])), "Submission PR"] = "https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard/discussions/32" df.loc[df["Model"].str.contains('|'.join(["DeepSeek-Coder-33b-base"])), "Submission PR"] = "https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard/discussions/31" df.loc[df["Model"].str.contains('|'.join(["DeepSeek-Coder-7b-instruct"])), "Submission PR"] = "https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard/discussions/43" df.loc[df["Model"].str.contains('|'.join(["DeepSeek-Coder-33b-instruct"])), "Submission PR"] = "https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard/discussions/42" df.loc[df["Model"].str.contains('|'.join(["CodeFuse"])), "Submission PR"] = "https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard/discussions/51" df.loc[df["Model"].str.contains('|'.join(["Stable-code-3b"])), "Submission PR"] = "https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard/discussions/57" df.loc[df["Model"].str.contains('|'.join(["OpenCodeInterpreter-DS-33B"])), "Submission PR"] = "https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard/discussions/60" df.loc[df["Model"].str.contains('|'.join(["OpenCodeInterpreter-DS-6.7B"])), "Submission PR"] = "https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard/discussions/61" df.loc[df["Model"].str.contains('|'.join(["Nxcode-CQ-7B-orpo"])), "Submission PR"] = "https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard/discussions/68" # print first 5 rows and 10 cols print(df.iloc[:5, :-1]) df.to_csv("data/code_eval_board.csv", index=False) # fill readme add_model_readme(df) print("Readme filled")