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---
task_categories:
- image-to-text
- text-to-image
pretty_name: Data Filtering Networks, 200m, datacomp large
size_categories:
- 100M<n<1B
---
# Data filtering networks, 200m
This is a dataset released from the Data Filtering Networks paper. It consists of a subset of Datacomp large.
These parquet files are that subset. The following script was used to filter the parquet files using the subset from apf1/datafilteringnetworks_2b.
```python
import os
from os import path
import numpy as np
import pyarrow.parquet as pq
from glob import glob
from multiprocessing import Pool
parquet_files = list(glob("../*.parquet"))
out_path = "../resampled/"
os.makedirs(out_path, exist_ok=True)
subset_file = "../indices/datacomp_large_dfn_200m_inds.npy"
u16 = np.dtype("u8,u8")
def load_subset():
return np.load(subset_file, mmap_mode="r")
def process_parquet(parquet_file):
print("filtering", parquet_file)
subset = load_subset()
table = pq.read_table(parquet_file)
mask = []
for uid in table["uid"]:
uid = str(uid)
key_u16 = np.array([divmod(int(uid, 16), 2**64)], u16)[0]
a = np.searchsorted(subset, key_u16, "left")
b = np.searchsorted(subset, key_u16, "right")
count = b - a
assert count == 1 or count == 0
mask.append(count == 1)
table = table.filter(mask)
out_filename = out_path + "/" + path.basename(parquet_file)
pq.write_table(table, out_filename)
print("wrote ", out_filename)
with Pool(4) as pool:
pool.map(process_parquet, parquet_files)
print("done.")
``` |