Update the data w/ compressed files to speed-up download process and reduce download size

.gitattributes

@@ -53,5 +53,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
-# Wiki data files CSV
+# Wiki data files CSV gz-compressed
 *.csv filter=lfs diff=lfs merge=lfs -text
+*.csv.gz filter=lfs diff=lfs merge=lfs -text

README.md

@@ -3,6 +3,36 @@ annotations_creators:
 - no-annotation
 language_creators:
 - crowdsourced
+language:
+- ace
+- ban
+- bcl
+- bjn
+- bug
+- cbk
+- ceb
+- gor
+- id
+- ilo
+- jv
+- km
+- lo
+- mad
+- min
+- mnw
+- ms
+- my
+- nia
+- pag
+- pam
+- shn
+- su
+- ta
+- th
+- tl
+- tet
+- vi
+- war
 license:
 - cc-by-sa-4.0
 multilinguality:
@@ -12,36 +42,6 @@ source_datasets:
 task_categories:
 - text-generation
 - fill-mask
-language:
-  - ace
-  - ban
-  - bcl
-  - bjn
-  - bug
-  - cbk
-  - ceb
-  - gor
-  - id
-  - ilo
-  - jv
-  - km
-  - lo
-  - mad
-  - min
-  - mnw
-  - ms
-  - my
-  - nia
-  - pag
-  - pam
-  - shn
-  - su
-  - ta
-  - th
-  - tl
-  - tet
-  - vi
-  - war
 task_ids:
 - language-modeling
 - masked-language-modeling
@@ -82,7 +82,7 @@ dataset_info:
     num_bytes: 2033238
     num_examples: 3285
   - name: ceb
-    num_bytes: 4572804909
+    num_bytes: 4572804910
     num_examples: 6302896
   - name: gor
     num_bytes: 6239133
@@ -148,13 +148,13 @@ dataset_info:
     num_bytes: 85356818
     num_examples: 45341
   - name: vi
-    num_bytes: 1603057632
+    num_bytes: 1603057633
     num_examples: 1288680
   - name: war
     num_bytes: 454304567
     num_examples: 1266394
-  download_size: 10940051715
-  dataset_size: 10923905689
+  download_size: 1829748651
+  dataset_size: 10923905691
 - config_name: seawiki_dedup_all
   features:
   - name: url
@@ -183,7 +183,7 @@ dataset_info:
     num_bytes: 1579651
     num_examples: 2242
   - name: ceb
-    num_bytes: 4346511152
+    num_bytes: 4346511153
     num_examples: 5815254
   - name: gor
     num_bytes: 6217480
@@ -249,13 +249,13 @@ dataset_info:
     num_bytes: 85286023
     num_examples: 45121
   - name: vi
-    num_bytes: 1602828123
-    num_examples: 1287910
+    num_bytes: 1602830022
+    num_examples: 1287912
   - name: war
     num_bytes: 454266479
     num_examples: 1266204
-  download_size: 10701952694
-  dataset_size: 10686874347
+  download_size: 1811459996
+  dataset_size: 10686876247
 - config_name: seawiki_with_countries_all
   features:
   - name: url
@@ -353,7 +353,7 @@ dataset_info:
     num_bytes: 1370162
     num_examples: 2665
   - name: phl_ceb
-    num_bytes: 4572804909
+    num_bytes: 4572804910
     num_examples: 6302896
   - name: sgp_ms
     num_bytes: 419662356
@@ -374,10 +374,10 @@ dataset_info:
     num_bytes: 1454499
     num_examples: 1468
   - name: vnm_vi
-    num_bytes: 1603057632
+    num_bytes: 1603057633
     num_examples: 1288680
-  download_size: 10940051715
-  dataset_size: 13074580032
+  download_size: 1829748651
+  dataset_size: 13074580034
 - config_name: seawiki_with_countries_dedup_all
   features:
   - name: url
@@ -475,7 +475,7 @@ dataset_info:
     num_bytes: 764869
     num_examples: 1108
   - name: phl_ceb
-    num_bytes: 4346511152
+    num_bytes: 4346511153
     num_examples: 5815254
   - name: sgp_ms
     num_bytes: 414783365
@@ -496,10 +496,10 @@ dataset_info:
     num_bytes: 1452151
     num_examples: 1464
   - name: vnm_vi
-    num_bytes: 1602828123
-    num_examples: 1287910
-  download_size: 10701952694
-  dataset_size: 12822597856
+    num_bytes: 1602830022
+    num_examples: 1287912
+  download_size: 1811459996
+  dataset_size: 12822599756
 ---
 
 # **SEA Wikipedia Data Repository**
@@ -582,7 +582,7 @@ You may check the following tables to understand the current coverage of this da
 | tgl | tl | Tagalog | phl | [Wiki Link](https://en.wikipedia.org/wiki/Tagalog_language) | 45121 | 81.34 |
 | tha | th | Thai | tha | [Wiki Link](https://en.wikipedia.org/wiki/Thai_language) | 159666 | 965.95 |
 | tet | tet | Tetum | tls, idn | [Wiki Link](https://en.wikipedia.org/wiki/Tetum_language) | 1464 | 1.38 |
-| vie | vi | Vietnamese | vnm | [Wiki Link](https://en.wikipedia.org/wiki/Vietnamese_language) | 1287910 | 1,528.58 |
+| vie | vi | Vietnamese | vnm | [Wiki Link](https://en.wikipedia.org/wiki/Vietnamese_language) | 1287912 | 1,528.58 |
 | war | war | Waray | phl | [Wiki Link](https://en.wikipedia.org/wiki/Waray_language) | 1266204 | 433.22 |
 | (dialect) | map_bms | Banyumasan <br>(Dialect of Javanese) | idn | [Wiki Link](https://en.wikipedia.org/wiki/Banyumasan_dialect) | 11839 | 4.83 |
 
@@ -590,7 +590,7 @@ You may check the following tables to understand the current coverage of this da
 #### 3. Table of Token Statistics for Covered Languages
 The token statistics is generated using ```tiktoken``` using encoder for GPT-4.
 
-| Lang Code | Total Token | Avg Token per Article | Min Token | Max Token | Token Deciles List |
+| Dataset Lang Code | Total Token | Avg Token per Article | Min Token | Max Token | Token Deciles List |
 | :---: | ---: | ---: | ---: | ---: | :--- |
 | ace | 1,370,829 | 105.61899992295247 | 3 | 9,659 | [38.0, 52.0, 54.0, 69.0, 76.0, 84.0, 90.0, 123.0, 126.0] |
 | ban | 5,924,610 | 287.44893503469024 | 5 | 24,364 | [97.0, 144.0, 165.0, 187.0, 209.0, 245.0, 276.0, 315.0, 421.0] |
@@ -620,7 +620,7 @@ The token statistics is generated using ```tiktoken``` using encoder for GPT-4.
 | tet | 487,016 | 332.6612021857924 | 4 | 24,287 | [30.3, 47.0, 66.9, 101.0, 164.0, 177.0, 187.0, 248.6, 604.4] |
 | th | 330,964,733 | 2,072.8566695476807 | 1 | 289,150 | [231.0, 390.0, 546.0, 727.0, 969.0, 1276.0, 1741.0, 2533.0, 4361.0] |
 | tl | 27,789,730 | 615.8934864032269 | 7 | 60,728 | [73.0, 116.0, 161.0, 214.0, 281.0, 360.0, 465.0, 666.0, 1136.0] |
-| vi | 546,481,258 | 424.3163404275143 | 3 | 246,463 | [46.0, 64.0, 71.0, 80.0, 86.0, 92.0, 120.0, 240.0, 824.0] |
+| vi | 546,481,913 | 424.3161900813099 | 3 | 246,463 | [46.0, 64.0, 71.0, 80.0, 86.0, 92.0, 120.0, 240.0, 824.0] |
 | war | 117,438,315 | 92.74833676090108 | 1 | 25,689 | [60.0, 77.0, 81.0, 84.0, 87.0, 90.0, 94.0, 99.0, 110.0] |
 
 Some other languages in SEA that are already exists its Wiki Index at Wikimedia might be missing from this list. Any lang update PR is greatly appreciated!
@@ -628,7 +628,8 @@ Some other languages in SEA that are already exists its Wiki Index at Wikimedia
 ### How does the data being preprocessed? What makes it different from loading it directly from Wikipedia HF?
 The data available in here are processed with following flows:
 1. Raw data is being deduplicated on ```title``` and ```text``` (text-content from a given article), to remove articles containing boilerplate text (template text that are used usually for unavailable informations or asking for contributions of content in that article), which usually deemed noisy for NLP data.
-2. Furthermore, the ```title``` and ```text``` data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars/UTF-8 chars validated). You may check this [ ```dedup_raw_wiki_data.py```](https://huggingface.co/datasets/sabilmakbar/sea_wiki/blob/main/dedup_raw_wiki_data.py) script to understand its implementation.
+2. Furthermore, the ```title``` and ```text``` data are being checked for string-matching duplication (duplication of text that are being pre-processed, i.e symbols removed, HTML tags striped, or ASCII-chars/UTF-8 chars validated).
+The source code can be found on this Github Repo [SEA Wiki Github Source Code](https://github.com/sabilmakbar/sea_wiki)
 
 ### How do I extract new Wikipedia Dataset of SEA languages?
 Please refer to the corresponding Github Repo for more detailed info [SEA Wiki Github Source Code](https://github.com/sabilmakbar/sea_wiki)

count_data_stats.py

@@ -35,7 +35,7 @@ if __name__ == "__main__":
         stat_dict[split] = {"total": total_token, "avg": avg_token, "min": min_token, "max": max_token, "deciles": deciles}
 
     # for markdown table format
-    print("| Lang Code | Total Token | Avg Token per Article | Min Token | Max Token | Token Deciles List |")
+    print("| Dataset Lang Code | Total Token | Avg Token per Article | Min Token | Max Token | Token Deciles List |")
     print("| :---: | ---: | ---: | ---: | ---: | :--- |")
     for key, data in stat_dict.items():
         print(f"| {key} | {data['total']:,} | {data['avg']:,} | {data['min']:,} | {data['max']:,} | {[round(num,2) for num in data['deciles']]} |")

dedup_raw_wiki_data.py

@@ -1,414 +0,0 @@
-# %%
-'''
-Script on Cleansing Wikipedia Data that has been extracted from extract_raw_wiki_data.py
-'''
-#core functionality modules
-import os, gc
-import logging
-import argparse
-import warnings
-
-from functools import partial
-
-#text preprocess modules
-import re
-import urllib
-from xml.etree import ElementTree as ET
-
-#dataset related modules
-import numpy as np
-import pandas as pd
-
-
-### MODULES DEFINITION ###
-#create custom type-checking of incoming ArgParse
-def argparse_bool_check(value: str):
-    #cast str with value like float into actual float
-    try:
-        value = float(value)
-    #can't be parsed as float, keep as it is
-    except ValueError:
-        pass
-
-    #cast float-like value (incl int) into str
-    if isinstance(value, float) and int(value) == value:
-        value = str(int(value))
-    #raise ArgumentTypeError if the value isn't in string already
-    else:
-        if not isinstance(value, str):
-            raise argparse.ArgumentTypeError(f"Not the correct value (args: {value})! Expected is cast-able to '1' or '0' or already in string. Please rectify!")
-    #check for these combinations of values
-    if value.lower() in ("yes", "true", "t", "y", "1"):
-        return True
-    elif value.lower() in ("no", "false", "f", "n", "0"):
-        return False
-    else:
-        raise argparse.ArgumentTypeError(f"Value Error! Not the correct value (args: {value})! Please rectify!")
-
-
-def text_processing_args_checker(value: str):
-    if value not in ["all", "text", "title", "neither"]:
-        raise argparse.ArgumentTypeError(f"Value Error! Not the correct value (args: {value})! Please rectify!")
-    else:
-        return value
-
-
-def set_logger():
-    # Set up the logger
-    logging.basicConfig(
-        level=logging.INFO,  # Set the desired logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
-        format='%(asctime)s [%(levelname)s]: %(message)s',  # Customize the log message format
-        datefmt='%Y-%m-%d %H:%M:%S'  # Customize the date/time format
-    )
-
-    # Create a file handler to write logs into a file
-    file_handler = logging.FileHandler('app.log')
-
-    # Set the log level for the file handler
-    file_handler.setLevel(logging.INFO) 
-
-    # Create a formatter for the file handler (customize the log format for the file)
-    file_formatter = logging.Formatter('%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
-    file_handler.setFormatter(file_formatter)
-
-    logger = logging.getLogger("Wiki Dataset Generation")
-    logger.addHandler(file_handler)
-
-    return logger
-
-
-#wrapper fn of text-cleansing
-def text_cleansing_wrapper(fn, exception_class_names = []):
-
-    #ensure caught exception class names passed to decorator is a list (if provided)
-    if not isinstance(exception_class_names, list):
-        raise TypeError("Exception Class Name for Wrapper is not a list!")
-    #ensure all values of caught exception class name list is a string
-    if not all([isinstance(val, str) for val in exception_class_names]):
-        raise ValueError("Found an element of Exception Class Name for Wrapper that is not a string!")
-
-    #lowercase all exception class name
-    exception_class_names = [val.lower() for val in exception_class_names]
-    if len(exception_class_names) == 0:
-        warnings.warn("The wrapper receives 0 `exception_class_names` to be warned! Will return the function value with its input!")
-
-    def text_fn_wrapper(text: str, *args, **kwargs):
-        try:
-            return fn(text, *args, **kwargs)
-        except Exception as e:
-            _exc_name = type(e).__name__
-            if _exc_name.lower() not in exception_class_names and len(exception_class_names)>0:
-                raise Exception(f"Exception Occured of {_exc_name} in {fn.__name__}!") from e
-            else:
-                _followup_msg = "Returning the input as it is..."
-                _text_warn = f"An exception of {_exc_name} occured in {fn.__name__}! {_followup_msg}"
-                warnings.warn(_text_warn)
-                return text
-
-    return text_fn_wrapper
-
-
-#create html tags cleanser of a given text
-partial_decorator = partial(text_cleansing_wrapper, exception_class_names=["parseerror"])
-@partial_decorator
-def remove_html_tags(text: str):
-    #extracted from "https://stackoverflow.com/a/9662410", w/ additional decorator of error handler
-    return (''.join(ET.fromstring(text).itertext())).strip()
-
-
-#create url decoder of text
-@text_cleansing_wrapper
-def decode_url(text: str):
-    # return (urllib.parse.unquote(text)).encode('utf8', errors='ignore').decode().strip()
-    return (urllib.parse.unquote(text)).strip()
-
-#create encoder check of text
-@text_cleansing_wrapper
-def check_text_by_encoder(text: str, encoder: str="utf8"):
-    return text.encode(encoder, errors='ignore').decode().strip()
-
-#create excessive whitespace removal of text
-@text_cleansing_wrapper
-def remove_excessive_whitespace(text: str):
-    return re.sub("(\s)(\s+)", r"\1", text).strip()
-
-#create non-alphanumeric removal of text
-@text_cleansing_wrapper
-def remove_non_alphanumeric(text: str):
-    return re.sub("[^a-z0-9\s]", "", text, flags=re.I).strip()
-
-# def cleanse_wiki_text(text: str):
-#     return remove_html_tags(decode_url_and_remove_non_ascii(text))
-
-# def normalize_wiki_title(text: str):
-#     return remove_non_alphanumeric(remove_excessive_whitespace(text.lower()))
-
-
-def _text_normalizer_constructor(
-        remove_non_alphanumeric_bool: bool, remove_excessive_whitespace_bool: bool,
-        remove_html_tags_bool: bool, decode_url_bool: bool, encoder_check_bool: bool,
-        encoder: str="utf8"):
-
-    _lambda_fn_1 = partial(check_text_by_encoder, encoder=encoder) if encoder_check_bool else lambda x: x
-    _lambda_fn_2 = lambda x: remove_non_alphanumeric(_lambda_fn_1(x)) if remove_non_alphanumeric_bool else _lambda_fn_1(x)
-    _lambda_fn_3 = lambda x: remove_excessive_whitespace(_lambda_fn_2(x)) if remove_excessive_whitespace_bool else _lambda_fn_2(x)
-    _lambda_fn_4 = lambda x: remove_html_tags(_lambda_fn_3(x)) if remove_html_tags_bool else _lambda_fn_3(x)
-    _lambda_fn_5 = lambda x: decode_url(_lambda_fn_4(x)) if decode_url_bool else _lambda_fn_4(x)
-
-    return _lambda_fn_5
-
-
-def _args_to_text_constructor_fn(**kwargs):
-
-    def _decode_options(opt: str):
-        # return decoded options with format `text_opt`, `title_opt`
-        # possible values are ["all", "text", "title", "neither"]
-        if opt == "all":
-            return True, True
-        elif opt == "text":
-            return True, False
-        elif opt == "title":
-            return False, True
-        else:
-            return False, False
-
-    kwargs_title, kwargs_text = {}, {}
-
-    kwargs_title["encoder"] = kwargs["text_encoder_choice_title"]
-    kwargs_text["encoder"] = kwargs["text_encoder_choice_text"]
-
-    for key, val in kwargs.items():
-        if key not in [
-            "remove_non_alphanumeric_option", "remove_excessive_whitespace_option",
-            "remove_html_tags_option", "decode_url_option", "encoder_check_option"]:
-            continue
-        new_key = "_".join(key.split("_")[:-1]) + "_bool"
-        text_opt_val, title_opt_val = _decode_options(val)
-        kwargs_text[new_key], kwargs_title[new_key] = text_opt_val, title_opt_val
-
-    return _text_normalizer_constructor(**kwargs_text), _text_normalizer_constructor(**kwargs_title)
-
-
-def _text_processing_wrapper(text: str, _fn, mode: str="text"):
-    if mode not in ["text", "title"]:
-        raise ValueError(f"Provided `mode` isn't either 'text' or 'title'! Received: {mode}")
-    return _fn(text.lower()) if mode=="title" else _fn(text)
-
-
-### MAIN CODE ###
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--raw-csv-path", help="Relative location of csv file containing raw Wikipedia data")
-
-    parser.add_argument("--drop-hard-dupl", help="""Flag whether to drop hard duplicates
-                        (exact values of data of relevant text fields, Titles & Desc)""",
-          default=True, type=argparse_bool_check)
-
-    parser.add_argument("--drop-soft-dupl", help="""Flag whether to drop soft duplicates
-                        (duplicates after cleansed and normalized relevant text fields, Titles & Desc)""",
-          default=True, type=argparse_bool_check)
-
-    parser.add_argument("--save-dir-path", help="""Relative dir path of saved Wikipedia CSV data
-                        to the `dedup_raw_wiki_data.py` script dir""",
-            default=os.path.dirname(os.path.abspath(__file__)))
-
-    ### THE FOLLOWING ARGUMENTS ONLY TEMPORARILY ALTER THE TEXT DATA ONLY FOR SOFT-DEDUP CHECK ###
-    ### THE INITIAL TEXT DATA WON'T BE OVERWRITTEN AFTER BEING PREPROCESSED ###
-    ### UNLESS YOU ARE SPECIFYING IN ARGS `overwrite-initial-title-data` AND `overwrite-initial-text-data` ###
-
-    ### ARGS TO OVERWRITTE INITIAL TEXT DATA WITH PROCESSED ONES ###
-    parser.add_argument("--overwrite-initial-title-data", help="""Flag whether to overwrite title
-                        init data w/ processed data (True) or keep it as it is (False)""",
-          default=False, type=argparse_bool_check)
-
-    parser.add_argument("--overwrite-initial-text-data", help="""Flag whether to overwrite text
-                        init data w/ processed data (True) or keep it as it is (False)""",
-          default=False, type=argparse_bool_check)
-
-    ### INSTANTIATOR ARGS FOR CONSTRUCTING TEXT PROCESSING FN TO BE APPLIED ###
-    parser.add_argument("--remove-non-alphanumeric-option", help="""Identifier which columns to be preprocessed
-                        using `remove_non_alphanumeric` for soft duplicates detection
-                        (Choices are "all", "text", "title", "neither")""",
-          default="neither", type=text_processing_args_checker)
-
-    parser.add_argument("--remove-excessive-whitespace-option", help="""Identifier which columns to be preprocessed
-                        using `remove_excessive_whitespace` for soft duplicates detection
-                        (Choices are "all", "text", "title", "neither")""",
-          default="all", type=text_processing_args_checker)
-
-    parser.add_argument("--remove-html-tags-option", help="""Identifier which columns to be preprocessed
-                        using `remove_html_tags` for soft duplicates detection
-                        (Choices are "all", "text", "title", "neither")""",
-          default="all", type=text_processing_args_checker)
-
-    parser.add_argument("--decode-url-option", help="""Identifier which columns to be preprocessed
-                        using `decode_url` for soft duplicates detection
-                        (Choices are "all", "text", "title", "neither")""",
-          default="all", type=text_processing_args_checker)
-
-    ### ARGS TO CHOOSE ENCODER CHECKING AND ITS CONFIG INITIALIZATION ###
-    parser.add_argument("--encoder-check-option", help="""Identifier which columns to be preprocessed
-                        using `check_text_by_encoder` for soft duplicates detection
-                        (Choices are "all", "text", "title", "neither")""",
-          default="all", type=text_processing_args_checker)
-
-    parser.add_argument("--text-encoder-choice-title", help="""Identifier of title encoder type
-                        to be applied into `check_text_by_encoder` for soft duplicates detection""",
-          default="utf8", type=str)
-
-    parser.add_argument("--text-encoder-choice-text", help="""Identifier of text encoder type
-                        to be applied into `check_text_by_encoder` for soft duplicates detection""",
-          default="utf8", type=str)
-
-
-    _EXPECTED_COLNAMES = ["id", "url", "title", "text"]
-
-    logger = set_logger()
-    logger.info("Parsing arguments...")
-
-    args = parser.parse_args()
-
-    # class dotdict(dict):
-    #     """dot.notation access to dictionary attributes"""
-    #     __getattr__ = dict.get
-    #     __setattr__ = dict.__setitem__
-    #     __delattr__ = dict.__delitem__
-    
-    # args = dotdict({
-    #     "raw_csv_path":"",
-    #     "drop_hard_dupl": True,
-    #     "drop_soft_dupl": True,
-    #     "save_dir_path": os.path.dirname(os.path.abspath(__file__)),
-    #     "overwrite_initial_title_data": False,
-    #     "overwrite_initial_text_data": False,
-    #     "remove_non_alphanumeric_option":"neither",
-    #     "remove_excessive_whitespace_option": "neither",
-    #     "remove_html_tags_option":"neither",
-    #     "decode_url_option":"neither",
-    #     "encoder_check_option":"all",
-    #     "text_encoder_choice_title":"utf8",
-    #     "text_encoder_choice_text":"utf8"
-    # })
-
-    _TEXT_PROCESSING_FN, _TITLE_PROCESSING_FN = _args_to_text_constructor_fn(
-        remove_non_alphanumeric_option = args.remove_non_alphanumeric_option,
-        remove_excessive_whitespace_option = args.remove_excessive_whitespace_option,
-        remove_html_tags_option = args.remove_html_tags_option,
-        decode_url_option = args.text_encoder_choice_title,
-        encoder_check_option = args.encoder_check_option,
-        text_encoder_choice_title = args.text_encoder_choice_title,
-        text_encoder_choice_text = args.text_encoder_choice_text
-    )
-
-    raw_data_path = args.raw_csv_path
-    drop_hard_dupl = args.drop_hard_dupl
-    drop_soft_dupl = args.drop_soft_dupl
-    save_dir = args.save_dir_path
-
-    overwrite_initial_title_data = args.overwrite_initial_title_data
-    overwrite_initial_text_data = args.overwrite_initial_text_data
-
-
-    df = pd.read_csv(raw_data_path)
-    if len(set(df.columns).difference(set(_EXPECTED_COLNAMES))) != 0 or len(set(_EXPECTED_COLNAMES).difference(set(df.columns))) != 0:
-        raise ValueError(f"The data schema expected, consist of columns: {', '.join(df.columns.to_list())} doesn't match with expected column values of {', '.join(_EXPECTED_COLNAMES)}!")
-
-    if (not drop_hard_dupl) and (not drop_soft_dupl):
-        raise AssertionError("The script won't run with both `drop-hard-dupl` and `drop-soft-dupl` args turned off!")
-    elif (not drop_hard_dupl):
-        warnings.warn("The args of `drop_hard_dupl` isn't turned off! Possibly the data will contain one template value of Wikipedia (usually no contribution text!)")
-
-    #will save id identifier colname first (popping first list val)
-    id_colname = _EXPECTED_COLNAMES.pop(0)
-
-    # if any of the data has duplicate values from columns checked (url, title, or text),
-    # it means the data integrity is questionable
-    # i.e. copied from other article or filled with template text
-    # hence, we will delete those duplicated datasets
-
-    #hard duplicate drop (drop all duplicate values that has exact same text on expected unique colnames)
-    if drop_hard_dupl:
-
-        for colname in _EXPECTED_COLNAMES:
-            logger.info(f"Checking data integrity on column {colname} on removing hard-duplicate(s)...")
-            dupl_text_df = df[df.duplicated(subset=colname,keep=False)]
-            shape_of_dupl_data = dupl_text_df.shape[0]
-
-            if shape_of_dupl_data > 0:
-                logger.info(f"Found {shape_of_dupl_data} data duplicated! Will be dropped")
-                df.drop_duplicates(subset=colname, keep=False, inplace=True)
-
-
-        #check id/idx of the cleansed data, whether it has duplicate
-        # (the duplication of id/idx should came from the very first extraction, not from the cleansing)
-
-        if df[df.duplicated(subset=id_colname,keep=False)].shape[0] > 0:
-            logger.info("Duplicated ID found! Re-assigning ID to the new ones based on `df.reset_index` method!")
-            df[id_colname] = df.reset_index().index
-
-    #soft duplicate drop (drop all except one duplicate values that has exact same text on expected unique colnames)
-    #keep the data that has longest value of its raw form
-    if drop_soft_dupl:
-
-        idx_to_keep = set(df.index.to_list())
-        #clean from text & title only, url isn't needed for this process
-        _EXPECTED_COLNAMES.remove("url")
-
-        for colname in _EXPECTED_COLNAMES:
-            #Construct Text Cleanser Fn for soft-duplicate cleansing
-            _PROCESSING_FN = _TEXT_PROCESSING_FN if colname == "text" else _TITLE_PROCESSING_FN
-            text_processing_fn = partial(_text_processing_wrapper, _fn=_PROCESSING_FN, mode=colname)
-            logger.info(f"Checking data integrity on column {colname} on removing soft-duplicate(s)...")
-            _df = df.copy(deep=True)
-
-            #Setting up DF cols as String so it can be text-processed
-            _df = _df[[colname]]
-            _df[colname] = _df[colname].astype("str")
-            logger.info(f"Cleansing the data based on {colname}")
-
-            #applying text processing
-            _df[colname+"_raw_len"] = _df[colname].apply(len)
-            _df[colname+"_cleansed"] = _df[colname].apply(lambda row_text: text_processing_fn(text=row_text))
-
-            #overwrite its text data if set as true
-            if overwrite_initial_title_data and colname == "title":
-                df[colname] = _df[colname+"_cleansed"]
-            elif overwrite_initial_text_data and colname == "text":
-                df[colname] = _df[colname+"_cleansed"]
-
-            #choose the data to keep by "ranking" it according to len of its raw text (greatest to keep)
-            logger.info(f"Ranking and grouping the data based on {colname}")
-            _df["rk"] = _df.groupby(colname+"_cleansed")[colname+"_raw_len"].rank(method="min", ascending=False)
-            shape_of_dupl_data = _df[_df["rk"]>1].shape[0]
-
-            if shape_of_dupl_data > 0:
-                logger.info(f"Found {shape_of_dupl_data} data duplicated! Will be dropped")
-                _idx_to_keep = _df[_df["rk"]==1].index.to_list()
-                if len(_idx_to_keep)+shape_of_dupl_data != df.shape[0]:
-                    raise AssertionError("Mismatch of data number!")
-                idx_to_keep = idx_to_keep.intersection(set(_idx_to_keep))
-            else:
-                logger.info(f"No soft-duplicate found in colname {colname}. Continuing")
-
-            del _df
-            gc.collect()
-
-        logger.info(f"The final data kept is {len(idx_to_keep)} from {df.shape[0]}")
-        df = df.loc[list(idx_to_keep),:]
-
-    logger.info("Saving dataset cleansed form...")
-    #input path splitted by ("/") for the last entry should return filename
-    #whereas the filename splitted by (".") except the last value should return the filename w/o ".csv" extension
-
-    _override_suffix_identifier = ""
-    if overwrite_initial_title_data or overwrite_initial_text_data:
-        _override_suffix_identifier = "_overwritten"
-        if overwrite_initial_text_data:
-            _override_suffix_identifier = "_text"+_override_suffix_identifier
-        if overwrite_initial_title_data:
-            _override_suffix_identifier = "_title"+_override_suffix_identifier
-
-    _save_file_name = ".".join(raw_data_path.split("/")[-1].split(".")[:-1]) + "_dedup_cleansed" + _override_suffix_identifier + ".csv"
-    _save_file_name = _save_file_name.replace("_raw", "")
-    df.to_csv(f"{save_dir}/{_save_file_name}", index=False)

dedup_raw_wiki_data_sea.sh

@@ -1,62 +0,0 @@
-#!/bin/bash
-
-# all available lang codes in SEA local-languages or linguistically-related to following countries in SEA:
-# Indonesia: "ace" (Acehnese), "ban" (Balinese), "bjn" (Banjarese), "bug" (Buginese), "gor" (Gorontalo), "id" (Indonesian), "jv" (Javanese), "mad" (Madurese), "map-bms" (Banyumasan, Dialect of Javanese), "min" (Minangkabau), "ms" (Malay), "nia" (Nias), "su" (Sundanese), "tet" (Tetum)
-# Singapore: "ms" (Malay), "ta" (Tamil)
-# Malaysia: "ms" (Malay), "ta" (Tamil)
-# Brunei: "ms" (Malay)
-# Thailand: "mnw" (Mon), "shn" (Shan), "th" (Thai)
-# Myanmar: "my" (Burmese), "mnw" (Mon), "shn" (Shan)
-# Laos: "lo" (Lao)
-# Vietnam: "vi" (Vietnamese)
-# Cambodia: "km" (Khmer)
-# East Timor: "tet" (Tetum)
-# Philippines: "bcl" (Central Bicolano), "cbk-zam" (Chavacano), "ceb" (Cebuano), "ilo" (Ilokano), "pag" (Pangasinan), "pam" (Kapampangan), "tl" (Tagalog), "war" (Waray)
-
-#params of executions
-folder_dir_to_save=./sea_wiki_dedup_data
-input_folder_to_be_dedup=./sea_wiki_raw_data
-
-drop_hard_dupl=True
-drop_soft_dupl=True
-
-
-# main executions
-
-# src: https://stackoverflow.com/a/18887210 (to list all files under a dir)
-shopt -s nullglob
-file_name_array=($input_folder_to_be_dedup/*)
-shopt -u nullglob # Turn off nullglob to make sure it doesn't interfere with anything later
-file_name_array="${file_name_array}"
-
-if [ ${#file_name_array[@]} == 0 ]; then
-    echo "No files found under directory $input_folder_to_be_dedup" >&2
-fi
-
-if [ ! -d $folder_dir_to_save ];
-then
-    echo "Dir $folder_dir_to_save not exists! Creating the dir..."
-    mkdir $folder_dir_to_save
-fi
-
-echo "The params hard-dedup drop is set as $drop_hard_dupl"
-echo "The params soft-dedup drop is set as $drop_soft_dupl"
-
-for val in ${!file_name_array[@]}; do
-    csv_path=${file_name_array[$val]}
-
-    if [[ ${csv_path} != *".csv" ]]; then
-        echo "The extracted file name isn't a CSV! Skipping! Received $csv_path"
-        continue
-    fi
-
-    echo "Executing Dedup on iteration no "$((val+1))" of total ${#file_name_array[@]} for input data $csv_path"
-    #see the script bcs there are more args than this command is using
-    python dedup_raw_wiki_data.py \
-        --raw-csv-path $csv_path \
-        --drop-hard-dupl $drop_hard_dupl \
-        --drop-soft-dupl $drop_soft_dupl \
-        --save-dir-path $folder_dir_to_save
-    echo "Done Execution"
-done
-echo "Done Dedup Process"

extract_raw_wiki_data.py

@@ -1,73 +0,0 @@
-'''
-Script on Generating Wikipedia Data that are dumped into https://dumps.wikimedia.org/
-More info can be read on https://huggingface.co/datasets/wikipedia
--------------------
-Check here to see available indexed data: https://dumps.wikimedia.org/backup-index.html
-Also check here to see language meta from its code: https://meta.wikimedia.org/wiki/List_of_Wikipedias
-'''
-
-import os, gc
-import logging
-import argparse
-
-import pandas as pd
-from datasets import load_dataset
-
-
-def set_logger():
-    # Set up the logger
-    logging.basicConfig(
-        level=logging.INFO,  # Set the desired logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
-        format='%(asctime)s [%(levelname)s]: %(message)s',  # Customize the log message format
-        datefmt='%Y-%m-%d %H:%M:%S'  # Customize the date/time format
-    )
-
-    # Create a file handler to write logs into a file
-    file_handler = logging.FileHandler('app.log')
-
-    # Set the log level for the file handler
-    file_handler.setLevel(logging.INFO) 
-
-    # Create a formatter for the file handler (customize the log format for the file)
-    file_formatter = logging.Formatter('%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
-    file_handler.setFormatter(file_formatter)
-
-    logger = logging.getLogger("Wiki Dataset Generation")
-    logger.addHandler(file_handler)
-
-    return logger
-
-
-#only executed if called directly
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--lang-id", help="Lang ID from Wikipedia Data to extract")
-
-    parser.add_argument("--date-ver", help="Date of Wikipedia Data (YYYYMMDD) generation to extract")
-
-    parser.add_argument("--save-dir-path", help="""Relative dir path of saved Wikipedia CSV data
-                        to the `extract_raw_wiki_data.py` script dir""",
-            default=os.path.dirname(os.path.abspath(__file__)))
-
-    args = parser.parse_args()
-
-
-    dset_name = "wikipedia"
-
-    logger = set_logger()
-    logger.info("Parsing arguments...")
-
-    lang_id = args.lang_id
-    date_ver = args.date_ver
-    save_dir = args.save_dir_path
-
-    logger.info("Loading the dataset from Wikipedia...")
-    df = load_dataset(dset_name, language=lang_id, date=date_ver, beam_runner='DirectRunner', split="train").to_pandas()
-    logger.info("Loading done!")
-    logger.info(f"#Data collected: {df.shape[0]}")
-    logger.info("Saving dataset raw form...")
-    df.to_csv(f"{save_dir}/wiki_{lang_id}_{date_ver}_raw_dataset.csv", index=False)
-
-    del df
-    gc.collect()

extract_raw_wiki_data_batched.py

@@ -1,87 +0,0 @@
-'''
-Script on Generating Wikipedia Data that are dumped into https://dumps.wikimedia.org/
-More info can be read on https://huggingface.co/datasets/wikipedia
--------------------
-Check here to see available indexed data: https://dumps.wikimedia.org/backup-index.html
-Also check here to see language meta from its code: https://meta.wikimedia.org/wiki/List_of_Wikipedias
-'''
-
-import os, gc
-import logging
-import argparse
-
-import pandas as pd
-from datasets import load_dataset
-
-
-def set_logger():
-    # Set up the logger
-    logging.basicConfig(
-        level=logging.INFO,  # Set the desired logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
-        format='%(asctime)s [%(levelname)s]: %(message)s',  # Customize the log message format
-        datefmt='%Y-%m-%d %H:%M:%S'  # Customize the date/time format
-    )
-
-    # Create a file handler to write logs into a file
-    file_handler = logging.FileHandler('app.log')
-
-    # Set the log level for the file handler
-    file_handler.setLevel(logging.INFO) 
-
-    # Create a formatter for the file handler (customize the log format for the file)
-    file_formatter = logging.Formatter('%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
-    file_handler.setFormatter(file_formatter)
-
-    logger = logging.getLogger("Wiki Dataset Generation")
-    logger.addHandler(file_handler)
-
-    return logger
-
-
-#only executed if called directly
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--lang-id", help="Lang ID from Wikipedia Data to extract")
-
-    parser.add_argument("--date-ver", help="Date of Wikipedia Data (YYYYMMDD) generation to extract")
-
-    #default: all
-    parser.add_argument("--split-extr", help="""Split extraction config for choosing
-                        subsets of data to process. It follows python list slicing string args""",
-            default=":")
-
-    #default: all
-    parser.add_argument("--force_rerun_split", help="""Flag to identify whether to check existing
-                        splits or forcing to re-create it""",
-            default=False)
-
-    parser.add_argument("--save-dir-path", help="""Relative dir path of saved Wikipedia CSV data
-                        to the `extract_raw_wiki_data.py` script dir""",
-            default=os.path.dirname(os.path.abspath(__file__)))
-
-    args = parser.parse_args()
-
-
-    dset_name = "sea_loader_batched/wiki_loader.py"
-
-    logger = set_logger()
-    logger.info("Parsing arguments...")
-
-    lang_id = args.lang_id
-    date_ver = args.date_ver
-    generated_split_extraction = args.split_extr
-    force_rerun_split_generation = args.force_rerun_split
-    save_dir = args.save_dir_path
-
-    logger.info("Loading the dataset from Wikipedia...")
-    df = load_dataset(dset_name, language=lang_id, date=date_ver, beam_runner='DirectRunner',
-                        split="train", subset_file_to_process=generated_split_extraction,
-                        force_rerun_split=force_rerun_split_generation).to_pandas()
-    logger.info("Loading done!")
-    logger.info(f"#Data collected: {df.shape[0]}")
-    logger.info("Saving dataset raw form...")
-    df.to_csv(f"{save_dir}/wiki_{lang_id}_{date_ver}_raw_dataset_splitted.csv", index=False)
-
-    del df
-    gc.collect()

extract_raw_wiki_data_sea.sh

@@ -1,38 +0,0 @@
-#!/bin/bash
-
-# all available lang codes in SEA local-languages or linguistically-related to following countries in SEA:
-# Indonesia: "ace" (Acehnese), "ban" (Balinese), "bjn" (Banjarese), "bug" (Buginese), "gor" (Gorontalo), "id" (Indonesian), "jv" (Javanese), "mad" (Madurese), "map-bms" (Banyumasan, Dialect of Javanese), "min" (Minangkabau), "ms" (Malay), "nia" (Nias), "su" (Sundanese), "tet" (Tetum)
-# Singapore: "ms" (Malay), "ta" (Tamil)
-# Malaysia: "ms" (Malay), "ta" (Tamil)
-# Brunei: "ms" (Malay)
-# Thailand: "mnw" (Mon), "shn" (Shan), "th" (Thai)
-# Myanmar: "my" (Burmese), "mnw" (Mon), "shn" (Shan)
-# Laos: "lo" (Lao)
-# Vietnam: "vi" (Vietnamese)
-# Cambodia: "km" (Khmer)
-# East Timor: "tet" (Tetum)
-# Philippines: "bcl" (Central Bicolano), "cbk-zam" (Chavacano), "ceb" (Cebuano), "ilo" (Ilokano), "pag" (Pangasinan), "pam" (Kapampangan), "tl" (Tagalog), "war" (Waray)
-
-#params of executions
-date_ver=20231101
-folder_dir_to_save=./sea_wiki_raw_data
-lang_list=(ace ban bcl bjn bug cbk-zam ceb gor id ilo jv km lo mad map-bms min mnw ms my nia pag pam shn su tet ta th tl vi war)
-
-
-#main executions
-
-if [ ! -d $folder_dir_to_save ]; then
-    echo "Dir $folder_dir_to_save not exists! Creating the dir..."
-    mkdir $folder_dir_to_save
-fi
-
-for val in ${!lang_list[@]}; do
-    lang=${lang_list[$val]}
-    echo "Executing Extractor on iteration no $((val+1)) of total ${#lang_list[@]} for language $lang and date version of $date_ver"
-    python extract_raw_wiki_data.py \
-        --lang-id $lang \
-        --date-ver $date_ver \
-        --save-dir-path $folder_dir_to_save
-    echo "Done Execution"
-done
-echo "Done Extraction Process"

sea_wiki.py

@@ -66,11 +66,24 @@ _LATEST_DUMP_VERSION_DATE = sorted(_AVAILABLE_DUMP_VERSION_DATE)[-1]
 
 def _construct_dset_url_from_dset_version_and_lang(date_ver: str, lang: str, mode: str):
     _mode_to_folder_mapper = {"dedup": "sea_wiki_dedup_data", "raw": "sea_wiki_raw_data"}
-    _mode_to_file_suffix_mapper = {"dedup": "dataset_dedup_cleansed.csv", "raw": "raw_dataset.csv"}
+    _mode_to_file_suffix_mapper = {"dedup": "dataset_dedup_cleansed.csv.gz", "raw": "raw_dataset.csv.gz"}
 
     return os.path.join(_mode_to_folder_mapper[mode], f"wiki_{lang}_{date_ver}_{_mode_to_file_suffix_mapper[mode]}")
 
 
+def read_csv_ignore_some_nulls(path: str, null_list_data: list=None, *args, **kwargs):
+    '''
+    Wrapper of `pd.read_csv` fn that ignores some of null data
+    '''
+    #values of pd._libs.parsers.STR_NA_VALUES: {'', '<NA>', 'NaN', 'N/A', 'null', '1.#QNAN', 'None', '#NA', 'nan', '-NaN', '#N/A N/A', '-1.#QNAN', 'NA', '-1.#IND', 'n/a', 'NULL', '-nan', '1.#IND', '#N/A'}
+    _unconsidered_for_null_list = ['NA', 'NULL', 'null', 'nan', 'null', 'NaN', 'None', 'N/A']
+    if null_list_data is not None:
+        _unconsidered_for_null_list.extend(null_list_data)
+
+    values_to_considered_missing_data = [val for val in pd._libs.parsers.STR_NA_VALUES if val not in _unconsidered_for_null_list]
+    return pd.read_csv(path, keep_default_na=False, na_values=values_to_considered_missing_data, *args, **kwargs)
+
+
 class SEAWikiConfig(datasets.BuilderConfig):
     """BuilderConfig for SEAWiki."""
 
@@ -199,7 +212,7 @@ class SEAWiki(datasets.GeneratorBasedBuilder):
         # handle cases of config "seawiki_all", "seawiki_dedup_all", and custom config where only country is provided (take all langs in a country)
         if self.config.name in ("seawiki_all", "seawiki_dedup_all") or (self.config.country is not None and self.config.lang is None):
             file_dict = {self._get_lang_name_from_data_url(file): file for file in self.config.data_url}
-            dl_dir = dl_manager.download_and_extract(file_dict)
+            dl_dir = dl_manager.download(file_dict)
 
             return [
                 datasets.SplitGenerator(
@@ -218,7 +231,7 @@ class SEAWiki(datasets.GeneratorBasedBuilder):
                 for file in file_list:
                     file_dict[country + "_" + self._get_lang_name_from_data_url(file)] = file
 
-            dl_dir = dl_manager.download_and_extract(file_dict)
+            dl_dir = dl_manager.download(file_dict)
 
             return [
                 datasets.SplitGenerator(
@@ -231,7 +244,7 @@ class SEAWiki(datasets.GeneratorBasedBuilder):
 
         # handle custom config where only country is provided
         elif self.config.lang is not None:
-            dl_dir = dl_manager.download_and_extract(self.config.data_url)
+            dl_dir = dl_manager.download(self.config.data_url)
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
@@ -243,7 +256,7 @@ class SEAWiki(datasets.GeneratorBasedBuilder):
         
 
     def _generate_examples(self, data_file):
-        pd_df = pd.read_csv(data_file)
+        pd_df = read_csv_ignore_some_nulls(data_file, compression='gzip')
         for _, row in pd_df.iterrows():
             example = {feature: row[feature] for feature in self.config.features}
             idx = row["id"]

sea_wiki_dedup_data/{wiki_pag_20231101_dataset_dedup_cleansed.csv → wiki_ace_20231101_dataset_dedup_cleansed.csv.gz}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2bc9b5c45ddbf2288944abf5bc1ffecb0c3ba28fdd28db335031cf6ec52c4f6b
-size 764394
+oid sha256:cfaba4f6590aa0259c3d4d6086768fd74fdfadc687bb0f674f9e3a0aee365b83
+size 813603

sea_wiki_dedup_data/{wiki_ace_20231101_dataset_dedup_cleansed.csv → wiki_ban_20231101_dataset_dedup_cleansed.csv.gz}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f52ce2ae7c8d340a30014bd036bc0806f5782f5a0856f80ffe3bf80f71b33152
-size 4938934
+oid sha256:e5c32ae0977f94f6c3d7d99516d30909eec686383ddffa71d599255c4f39184d
+size 3957396

sea_wiki_dedup_data/{wiki_bug_20231101_dataset_dedup_cleansed.csv → wiki_bcl_20231101_dataset_dedup_cleansed.csv.gz}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cc2a1d85b4306eb2d0cf74db4ca2d997b0ea20e2f4016167aeb66394ac5b9b59
-size 2172844
+oid sha256:7b125d1585cbf0cb1fda5c4124de07d3bf2e7752943108f3bbba56905f8cf456
+size 6967103

sea_wiki_dedup_data/{wiki_cbk-zam_20231101_dataset_dedup_cleansed.csv → wiki_bjn_20231101_dataset_dedup_cleansed.csv.gz}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e089c0accfbb22e26567995e4341b4f5c0756ef3598c5966a624922af77ce9f0
-size 1578089
+oid sha256:baa5ed89e351ce165e04214c4512b9e2264a352a154ec11fcebac6d78b91c8af
+size 1980505

sea_wiki_dedup_data/{wiki_bjn_20231101_dataset_dedup_cleansed.csv → wiki_bug_20231101_dataset_dedup_cleansed.csv.gz}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0a0914f929502fa632e764dadeddf49969b45ccf35d9933743bd4e9e2b16ea0f
-size 6791315
+oid sha256:38f22a07979c260b77ba2a6bdd84b2402c68a197520bbeaabdcf1e930304feee
+size 243060

sea_wiki_dedup_data/wiki_cbk-zam_20231101_dataset_dedup_cleansed.csv.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6767c2c1800f961b5a218082554c18e0531ac4897c795dc089ef192fe1e41af5
+size 461691

sea_wiki_dedup_data/wiki_ceb_20231101_dataset_dedup_cleansed.csv

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