added script with val

binhvq_dedup.py

@@ -0,0 +1,77 @@
+import datasets
+import os
+import logging
+import lm_dataformat
+
+
+_DESC = """
+BinhVQ news corpus version 2021 (~20M records)
+
+https://github.com/binhvq/news-corpus
+
+Preprocessed:
+    - Read mongo dump and export to jsonl
+    - Clean content with Beautifulsoup
+    - Concatenate title, sapo and content
+    - Remove exact match sha256
+    - Shuffle and split train / val (0.01)
+
+**IMPORTANT**: Please run `pip install lm_dataformat` before load this dataset
+
+"""
+
+_REPO_URL = "https://huggingface.co/datasets/imthanhlv/binhvq_dedup/tree/main/"
+_URLS = {
+    "val": "val.jsonl.zst",
+}
+
+
+try:
+    import lm_dataformat
+except ImportError:
+    print(
+        "Can't import lm_dataformat, please run pip install lm_dataformat and try again"
+    )
+    exit()
+
+
+class BinhvqConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(BinhvqConfig, self).__init__(**kwargs)
+
+
+class Binhvq(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        BinhvqConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESC,
+            features=datasets.Features({"text": datasets.Value("string")}),
+            homepage="https://github.com/binhvq/news-corpus",
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": downloaded_files["val"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": downloaded_files["val"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        logging.info(f"Generating examples from {filepath}")
+        reader = lm_dataformat.Reader(filepath)
+        for doc in reader.stream_data():
+            yield {"text": doc}
+