init data

data/Mishnah_english.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c370361d5bc54750bd0657840e8b5c71270122f09169f0fcabc0065bffd1cc7b
+size 7291639

data/Mishnah_hebrew.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:477612cd11799ac1103055438a5e56fe774be5595bf5e96848678cca09645aee
+size 88534468

data/Talmud_english.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:14c0a6e0ac18c3efc20b6f8f2a06347f4baa101158d68d2c764a44c546a8842d
+size 50222575

data/Talmud_hebrew.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:751245b158e12fb0a9c27f731df216df0d3de9f5b4161a3184ac85144dcaeb80
+size 294414866

scrape_script.py

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+import os
+import sys
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+import json
+
+
+def traverse_directory(root_path, callback):
+    for dirpath, _, filenames in os.walk(root_path):
+        for filename in filenames:
+            file_path = os.path.join(dirpath, filename)
+            callback(file_path)
+
+
+def process_file(file_path):
+    if not file_path.endswith(".txt"):
+        return
+
+    with open(file_path, "r", encoding="utf-8") as file:
+        content = file.read()
+
+    dirname = os.path.dirname(file_path)
+    dir_name = os.path.basename(dirname)
+    top_level_directory = os.path.relpath(dirname, root_directory).split(os.sep)[0]
+
+    if dir_name.lower() == "english":
+        append_to_parquet(content, file_path, "en", top_level_directory)
+    elif dir_name.lower() == "hebrew":
+        append_to_parquet(content, file_path, "he", top_level_directory)
+
+
+def append_to_parquet(content, file_path, lang, top_level_directory):
+    data_dir = "data"
+    if not os.path.exists(data_dir):
+        os.makedirs(data_dir)
+
+    if lang == "en":
+        parquet_file = os.path.join(data_dir, f"{top_level_directory}_english.parquet")
+    elif lang == "he":
+        parquet_file = os.path.join(data_dir, f"{top_level_directory}_hebrew.parquet")
+    else:
+        return
+
+    metadata = {"file": file_path}
+    meta_json = json.dumps(metadata)
+
+    data = pd.DataFrame({"meta": [meta_json], "text": [content]})
+    table = pa.Table.from_pandas(data)
+
+    if not os.path.exists(parquet_file) or os.path.getsize(parquet_file) == 0:
+        with pq.ParquetWriter(parquet_file, table.schema, compression="snappy") as writer:
+            writer.write_table(table)
+    else:
+        pf = pq.ParquetFile(parquet_file)
+        old_table = pf.read()
+        combined_table = pa.concat_tables([old_table, table])
+
+        with pq.ParquetWriter(parquet_file, combined_table.schema, compression="snappy") as writer:
+            writer.write_table(combined_table)
+
+    print(f"Successfully saved: {file_path}")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python script.py <root_directory_path>")
+        sys.exit(1)
+
+    root_directory = sys.argv[1]
+    traverse_directory(root_directory, process_file)
+

sefaria.py

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+import os
+import pandas as pd
+
+import datasets
+
+_CITATION = """\
+@misc{shmidman2022introducing,
+      title={Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language}, 
+      author={Avi Shmidman and Joshua Guedalia and Shaltiel Shmidman and Cheyn Shmuel Shmidman and Eli Handel and Moshe Koppel},
+      year={2022},
+      eprint={2208.01875},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+This Dataset is a Hugging Face Reflection of the Sefaria Database.
+The following is the description of Sefaria from the sefaria.org website:
+
+Sefaria is home to 3,000 years of Jewish texts. We are a non-profit organization 
+offering free access to texts, translations, and commentaries so that everyone 
+can participate in the ongoing process of studying, interpreting, and creating Torah
+"""
+
+_HOMEPAGE = "https://github.com/Sefaria/Sefaria-Export"
+_LICENSE = "Each text is licensed separately, so there is no overall license for this repository."
+
+class Sefaria(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="english", version=VERSION, description="This configuration contains only English texts"),
+        datasets.BuilderConfig(name="hebrew", version=VERSION, description="This configuration contains only Hebrew texts"),
+        datasets.BuilderConfig(name="all", version=VERSION, description="This configuration contains both English and Hebrew texts"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+                "meta": datasets.Value("string")
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = "data"
+
+        if self.config.name == "english":
+            filepaths = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith("_english.parquet")]
+        elif self.config.name == "hebrew":
+            filepaths = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith("_hebrew.parquet")]
+        else:
+            filepaths = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith(".parquet")]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": filepaths,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepaths):
+        for filepath in filepaths:
+            df = pd.read_parquet(filepath)
+            for idx, row in df.iterrows():
+                yield idx, {
+                    "text": row["text"],
+                    "language": row["language"],
+                }
+