metadata

license: mit
language:
  - en
tags:
  - not-for-all-audiences
  - chemistry
  - biology
  - finance
  - legal
  - music
  - art
  - code
  - climate
  - medical
pretty_name: Well Reddits
size_categories:
  - 100M<n<1B
task_categories:
  - question-answering

🙋🏻‍♂️Welcome to 🧑🏻‍🚀Tonic's🚀🚰Well🔴Reddit🔥!

This is every "best reddit_question_best_answers" appended and produced according to the following template :

{"prompt": "This is the first prompt", "completion": "This is the first completion"}
{"prompt": "This is the second prompt", "completion": "This is the second completion"}

🤔The point is to make it easy to train models with a single correctly formatted dataset of

54,367,153 rows

Probably there's a big problem with the token count on these long answers 😉

good luck !🧑🏻‍🚀🚀

Original Dataset :

nreimers/reddit_question_best_answers

How To Use :

Combine random shards in random quantities to produce a very high quality conversational training dataset for fine tuning by running the following code:


import random

# Define the shards
shards = [f"shard_{i}" for i in range(1, 34)]

# Function to combine random shards
def combine_shards():
    # Select a random shard
    selected_shard = random.choice(shards)

    # Select a random quantity (1-5)
    quantity = random.randint(1, 5)

    return selected_shard, quantity

# Example usage
for _ in range(10):  # Combine 10 times as an example
    shard, qty = combine_shards()
    print(f"Combined {qty} of {shard}")

Pre-Processing

import json
import os
import gzip
import logging
import re
import random

# Setup basic logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

def clean_string(s):
    """Remove special characters, keeping only alphanumeric characters and spaces."""
    if isinstance(s, list):
        # Extract text from each dictionary in the list and join into a single string
        s = " ".join([d.get("body", "") if isinstance(d, dict) else str(d) for d in s])
    return re.sub(r'[^A-Za-z0-9 ]+', '', s)

def process_file(input_file, output_file):
    try:
        dataset = []
        with gzip.open(input_file, 'rt') as infile:
            for line in infile:
                # Parse the JSON line
                try:
                    data = json.loads(line)
                except json.JSONDecodeError:
                    logging.error(f"Invalid JSON format in {input_file}: {line}")
                    continue

                # Extract and clean the 'body' and 'answers' fields
                prompt = clean_string(data.get("body", ""))
                completion = clean_string(data.get("answers", ""))

                # For each body found, make a new row and duplicate the prompt for it
                if isinstance(data.get("body", ""), list):
                    for body in data.get("body", []):
                        cleaned_body = clean_string(body)
                        dataset.append({"prompt": cleaned_body, "completion": completion})
                else:
                    dataset.append({"prompt": prompt, "completion": completion})

        # Shuffle the dataset
        random.shuffle(dataset)

        # Write the shuffled dataset to the output file
        with open(output_file, 'a') as outfile:
            for item in dataset:
                json.dump(item, outfile)
                outfile.write('\n')

        logging.info(f"Processed file: {input_file}")

    except Exception as e:
        logging.error(f"Error processing file {input_file}: {e}")

def process_files(file_list, output_dir):
    # Ensure the output directory exists
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    # Create a single output file path
    output_file = os.path.join(output_dir, 'synthesized_dataset.jsonl')

    for input_file in file_list:
        process_file(input_file, output_file)

        # Update with your list of .gz file paths
file_list = [r'C:\Users\MeMyself\FILES, r"C:\Users\MeMyself\FILES" ]  # Update with your list of .gz file paths
output_dir = r'C:\Users\MeMyself\reddit_question_best_answers\processed'
process_files(file_list, output_dir)

sharding script : here