<jupyter_start><jupyter_text>TODOS- remove captions with confidence < 0.5<jupyter_code>import tensorflow as tf
import numpy as np
import os
import time
import datetime
# import data_helpers
from tensorflow.contrib import learn
import csv
from pathlib import Path 
import gensim
import pickle
import pandas as pd
from sklearn import preprocessing
import re
import numpy as np
data_files = [
    os.path.join(Path(os.getcwd()).parent.parent, 'deeptech-ai-data', 'img_data_{}.npy'.format(i)) for i in range(1, 7)
]
data_files
data_batches = [np.load(f) for f in data_files]
merged = np.concatenate(data_batches)
np.save(os.path.join(Path(os.getcwd()).parent.parent, 'deeptech-ai-data', 'merged_data'), merged)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10733695652173914, 'fraction_numerical': 0.005434782608695652, 'mean_word_length': 6.019047619047619, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '568488', 'license': 'MIT', 'n_tokens_mistral': 261, 'n_tokens_neox': 255, 'n_words': 71, 'path': 'notebooks/06-sb-merge_batch_datafiles.ipynb', 'repo_name': 'LeanderK/deeptech-ai', 'size': '736'}