README.md

---
license: mit
language:
- en
tags:
- not-for-all-audiences
- chemistry
- biology
- finance
- legal
- music
- art
- code
- climate
- medical
pretty_name: Easy Reddit
size_categories:
- 10M<n<100M
configs:
- config_name: shards
  data_files:
  - split: train
    path: 
    - shard_1.jsonl
    - shard_2.jsonl
    - shard_3.jsonl
    - shard_4.jsonl
    - shard_5.jsonl
    - shard_6.jsonl
    - shard_7.jsonl
    - shard_8.jsonl
    - shard_9.jsonl
    - shard_10.jsonl
    - shard_11.jsonl
    - shard_12.jsonl
    - shard_13.jsonl
    - shard_14.jsonl
    - shard_15.jsonl
    - shard_16.jsonl
    - shard_17.jsonl
    - shard_18.jsonl
    - shard_19.jsonl
    - shard_20.jsonl
    - shard_21.jsonl
    - shard_22.jsonl
    - shard_23.jsonl
    - shard_24.jsonl
    - shard_25.jsonl
    - shard_26.jsonl
    - shard_27.jsonl
    - shard_28.jsonl
    - shard_29.jsonl
    - shard_30.jsonl
    - shard_31.jsonl
    - shard_32.jsonl
    - shard_33.jsonl
    - shard_34.jsonl
---


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




![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/tsm1OFhNgT4wzIw-_MGQ2.png)


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

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

![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/N_RqZSJ32MDIrRGbLcPqm.png)


- 🌟 You can use it in shards or all together !

- 🌟 This dataset is **internally consistent** !



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

- **54,367,153 rows**

# Original Dataset :

[nreimers/reddit_question_best_answers](https://huggingface.co/datasets/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 or try combining rows line by line to save memory by running the following code: 

```python

# see selectbyline.py

import os
import random

# Directory containing the shard JSONL files
shard_directory = "/path/to/shard/directory"

# Get a list of all JSONL files in the directory
shard_files = [f for f in os.listdir(shard_directory) if f.endswith('.jsonl')]

# Function to read a random number of lines (between min_lines and max_lines) from a file
def read_random_lines(filename, min_lines, max_lines):
    selected_lines = []
    num_lines = random.randint(min_lines, max_lines)

    with open(filename, 'r') as file:
        lines = list(file)
        if len(lines) <= num_lines:
            return lines
        selected_lines = random.sample(lines, num_lines)

    return selected_lines

# Function to combine shards
def combine_shards(output_filename, num_combinations):
    with open(output_filename, 'w') as output_file:
        for _ in range(num_combinations):
            selected_shard_file = random.choice(shard_files)
            lines = read_random_lines(os.path.join(shard_directory, selected_shard_file), 5000, 10000)
            output_file.writelines(lines)

# Example usage
combine_shards("/path/to/output/combined_shards.jsonl", 10)

```

# Pre-Processing

```python
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** : 

```python

import json
import os

def read_dataset(file_path):
    try:
        with open(file_path, 'r') as file:
            data = [json.loads(line) for line in file]
        print(f"Dataset loaded successfully from {file_path}.")
        return data
    except Exception as e:
        print(f"Error reading dataset from {file_path}: {e}")
        return []

def shard_dataset(dataset, num_shards):
    shard_size = len(dataset) // num_shards
    shards = [dataset[i:i + shard_size] for i in range(0, len(dataset), shard_size)]
    if len(shards) > num_shards:
        shards[num_shards - 1].extend(shards.pop())
    print(f"Dataset sharded into {num_shards} parts.")
    return shards

def write_shards(shards, output_dir):
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
        print(f"Created output directory at {output_dir}.")

    for i, shard in enumerate(shards):
        shard_file = os.path.join(output_dir, f'shard_{i+1}.jsonl')
        with open(shard_file, 'w') as file:
            for item in shard:
                json.dump(item, file)
                file.write('\n')
        print(f"Shard {i+1} written to {shard_file}.")

def main():
    input_file = 'path_to_processed_dataset.jsonl'  # Update with your processed dataset file path
    output_dir = 'sharded_dataset'  # Update with your output directory for shards
    num_shards = 33

    dataset = read_dataset(input_file)
    if dataset:
        shards = shard_dataset(dataset, num_shards)
        write_shards(shards, output_dir)
        print("All shards have been successfully written.")
    else:
        print("No dataset to process.")

if __name__ == "__main__":
    main()

```

### Disclaimer :

🌟Re-format this dataset before use.

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

🌟**Good Luck !** 🧑🏻‍🚀🚀