Datasets:

AiAF
/

co-sft-dataset

@@ -1,139 +0,0 @@
-import os
-import json
-import sys
-import re
-from pathlib import Path
-import argparse
-import subprocess
-import importlib
-def install_tqdm_if_needed():
-    """Checks for tqdm and installs it if not found."""
-    try:
-        importlib.import_module('tqdm')
-    except ImportError:
-        print("`tqdm` library not found. Installing...")
-        try:
-            subprocess.check_call([sys.executable, "-m", "pip", "install", "tqdm"])
-        except subprocess.CalledProcessError as e:
-            print(f"Error: Failed to install tqdm. Please install it manually using 'pip install tqdm'.")
-            sys.exit(1)
-def create_sft_dataset(input_folder, output_file):
-    """
-    Scans a directory for .txt thread files, parses them, and creates a
-    JSONL dataset suitable for Supervised Fine-Tuning (SFT).
-    """
-    from tqdm import tqdm
-    folder_path = Path(input_folder)
-    output_path = Path(output_file)
-    if not folder_path.is_dir():
-        print(f"Error: The input folder '{input_folder}' does not exist.")
-        sys.exit(1)
-    try:
-        output_path.parent.mkdir(parents=True, exist_ok=True)
-    except Exception as e:
-        print(f"Error: Could not create output directory '{output_path.parent}'. Error: {e}")
-        sys.exit(1)
-    thread_files = list(folder_path.glob("*.txt"))
-    if not thread_files:
-        print(f"Error: No .txt files found in '{input_folder}'.")
-        sys.exit(1)
-    print(f"Found {len(thread_files)} thread files. Processing...")
-    total_conversations = 0
-    system_prompt = "You are an anonymous message board user. You are opinionated and use informal language. You discuss various topics."
-    reply_pattern = re.compile(r'>>(\d{8,9})')
-    with open(output_path, 'w', encoding='utf-8') as f_out:
-        for file_path in tqdm(thread_files, desc="Processing Threads"):
-            try:
-                with open(file_path, 'r', encoding='utf-8') as f:
-                    content = f.read()
-                posts_map = {}
-                ordered_post_ids = []
-                raw_posts = [p.strip() for p in content.split('--- ') if p.strip()]
-                for post_text in raw_posts:
-                    lines = post_text.splitlines()
-                    if lines and lines[0].strip().isdigit():
-                        post_id = lines[0].strip()
-                        post_content = "\n".join(lines[1:]).strip()
-                        if post_content:
-                            posts_map[post_id] = post_content
-                            ordered_post_ids.append(post_id)
-                if not ordered_post_ids:
-                    continue
-                op_id = ordered_post_ids[0]
-                op_content = posts_map[op_id]
-                # --- LOGIC REMAINS THE SAME FOR DIRECT REPLIES ---
-                for post_id, post_content in posts_map.items():
-                    reply_ids = reply_pattern.findall(post_content)
-                    if reply_ids:
-                        assistant_text = post_content.strip()
-                        for parent_id in reply_ids:
-                            if parent_id in posts_map:
-                                human_text = posts_map[parent_id]
-                                conversation = {
-                                    "conversations": [
-                                        {"from": "system", "value": system_prompt},
-                                        {"from": "human", "value": human_text},
-                                        {"from": "assistant", "value": assistant_text}
-                                    ]
-                                }
-                                f_out.write(json.dumps(conversation) + '\n')
-                                total_conversations += 1
-                # --- NEW LOGIC FOR STANDALONE POSTS ---
-                for post_id, post_content in posts_map.items():
-                    # A post is standalone if it's not the OP and has no reply links
-                    if post_id != op_id and not reply_pattern.search(post_content):
-                        assistant_text = post_content.strip()
-                        conversation = {
-                            "conversations": [
-                                {"from": "system", "value": system_prompt},
-                                {"from": "human", "value": op_content},
-                                {"from": "assistant", "value": assistant_text}
-                            ]
-                        }
-                        f_out.write(json.dumps(conversation) + '\n')
-                        total_conversations += 1
-            except Exception as e:
-                tqdm.write(f"Warning: Could not read or process file {file_path}. Error: {e}")
-    print(f"\nProcessing complete.")
-    print(f"Successfully created SFT dataset with {total_conversations} conversations.")
-    print(f"Output file: {output_path}")
-if __name__ == "__main__":
-    install_tqdm_if_needed()
-    parser = argparse.ArgumentParser(
-        description="Create an SFT dataset in JSONL format from a folder of message board thread files.",
-        formatter_class=argparse.RawTextHelpFormatter
-    )
-    parser.add_argument(
-        "input_folder",
-        help="The path to the folder containing your .txt thread files."
-    )
-    parser.add_argument(
-        "-o", "--output_file",
-        default="sft_dataset.jsonl",
-        help="The name of the output .jsonl file. Defaults to 'sft_dataset.jsonl'."
-    )
-    args = parser.parse_args()
-    create_sft_dataset(args.input_folder, args.output_file)