Create README.md

README.md

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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.
+
+```
+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.")
+```