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import os
import json
import random
from datasets import Dataset, DatasetDict
# Configuration
input_folder = "./Raw" # Replace with the path to your folder containing .jsonl files
train_ratio = 0.8 # 80% for training, 20% for validation
output_repo = "BXYMartin/OpenHearthstoneLLM" # Replace with your Hugging Face dataset repository name
# Load all .jsonl files from the folder
data = []
for filename in os.listdir(input_folder):
if filename.endswith(".jsonl"):
with open(os.path.join(input_folder, filename), "r", encoding="utf-8") as f:
for line in f:
blob = json.loads(line)
entry = {key: str(value) for key, value in blob.items()}
data.append(entry)
# Shuffle the data
random.shuffle(data)
# Split the data into train and validation sets
train_size = int(len(data) * train_ratio)
train_data = data[:train_size]
val_data = data[train_size:]
# Convert to Hugging Face Dataset
train_dataset = Dataset.from_list(train_data)
val_dataset = Dataset.from_list(val_data)
# Create a DatasetDict
dataset_dict = DatasetDict({
"train": train_dataset,
"validation": val_dataset
})
# Push to Hugging Face Hub
dataset_dict.push_to_hub(output_repo)
print(f"Dataset pushed to Hugging Face Hub: {output_repo}")
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