Datasets:
File size: 3,660 Bytes
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---
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 :
```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"}
```
🤔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](https://huggingface.co/datasets/nreimers/reddit_question_best_answers)
# 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)
``` |