High level fuzzy duplicates module

config/fuzzy_dedup_config.yaml

@@ -0,0 +1,16 @@
+cache_dir: "./fuzzy_dedup_cache"
+# Optional Params below with default values
+# profile_dir: null
+# id_field: "id"
+# text_field: "text"
+
+# seed: 42
+# char_ngrams: 5
+# num_buckets: 20
+# hashes_per_bucket: 13
+# use_64_bit_hash: false
+# buckets_per_shuffle: 1
+
+# false_positive_check: True
+# num_anchors: 2
+# jaccard_threshold: 0.8

examples/fuzzy_deduplication.py

@@ -0,0 +1,109 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import time
+
+import dask
+from dask import dataframe as dd
+
+from nemo_curator import FuzzyDuplicates, FuzzyDuplicatesConfig
+from nemo_curator.datasets import DocumentDataset
+from nemo_curator.utils.distributed_utils import get_client, write_to_disk
+from nemo_curator.utils.script_utils import add_distributed_args
+
+
+def pre_imports():
+    import cudf  # noqa: F401
+
+
+def main(args):
+
+    dataset_dir = "/path/to/dataset"
+    log_dir = "./"
+    cache_dir = "./fuzzy_cache"
+    output_dir = "./output"
+    dataset_id_field = "id"
+    dataset_text_field = "text"
+
+    filetype = "parquet"
+
+    # Fuzzy dup calculation only supports the cuDF/GPU backend
+    backend = "cudf"
+    assert args.device == "gpu"
+
+    with dask.config.set({"dataframe.backend": backend}):
+        client = get_client(args, args.device)
+        client.run(pre_imports)
+
+        t0 = time.time()
+        if filetype == "parquet":
+            input_dataset = DocumentDataset(
+                dd.read_parquet(
+                    dataset_dir,
+                    columns=[dataset_id_field, dataset_text_field],
+                    blocksize="256MiB",
+                    aggregate_files=True,
+                )
+            )
+        elif filetype == "jsonl":
+            input_dataset = DocumentDataset.read_json(
+                dataset_dir,
+                backend=backend,
+            )
+
+        fuzzy_dedup_config = FuzzyDuplicatesConfig(
+            cache_dir=cache_dir,
+            id_field=dataset_id_field,
+            text_field=dataset_text_field,
+            seed=42,
+            char_ngrams=5,
+            num_buckets=20,
+            hashes_per_bucket=13,
+            use_64_bit_hash=False,
+            buckets_per_shuffle=5,
+            false_positive_check=True,
+            num_anchors=2,
+            jaccard_threshold=0.8,
+        )
+        fuzzy_dup = FuzzyDuplicates(logger=log_dir, config=fuzzy_dedup_config)
+        duplicates = fuzzy_dup(dataset=input_dataset)
+
+        # By default all duplicate id's and the group they belong to are included in the result
+        # keep 1 document from each group of duplcates and mark the others to remove
+        # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.duplicated.html
+        docs_to_remove = duplicates.df.map_partitions(
+            lambda x: x[x.group.duplicated(keep="first")]
+        )
+
+        # When there are few duplicates we can compute the results to a list and use `isin`.
+        result = input_dataset.df[
+            ~input_dataset.df[dataset_id_field].isin(
+                docs_to_remove[dataset_id_field].compute()
+            )
+        ]
+        write_to_disk(result, output_dir, output_type=filetype)
+        print(time.time() - t0)
+
+
+def attach_args(
+    parser=argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    ),
+):
+    return add_distributed_args(parser)
+
+
+if __name__ == "__main__":
+    main(attach_args().parse_args())