diff --git a/README.md b/README.md index 6121242abb5807d3c257915f2cb9111251134b91..6627b3d96d10803cf963bd86733eeb6f9c963450 100644 --- a/README.md +++ b/README.md @@ -336,7 +336,7 @@ dataset_info: - name: train num_bytes: 8905282792 num_examples: 2665357 - download_size: 6523215105 + download_size: 5343683253 dataset_size: 8905282792 - config_name: 20220301.en features: @@ -352,7 +352,7 @@ dataset_info: - name: train num_bytes: 20275516160 num_examples: 6458670 - download_size: 20598313936 + download_size: 11685147288 dataset_size: 20275516160 - config_name: 20220301.fr features: @@ -368,7 +368,7 @@ dataset_info: - name: train num_bytes: 7375920768 num_examples: 2402095 - download_size: 5602565274 + download_size: 4223919240 dataset_size: 7375920768 - config_name: 20220301.frr features: @@ -384,7 +384,7 @@ dataset_info: - name: train num_bytes: 9129760 num_examples: 15199 - download_size: 12438017 + download_size: 4529255 dataset_size: 9129760 - config_name: 20220301.it features: @@ -400,7 +400,7 @@ dataset_info: - name: train num_bytes: 4539944448 num_examples: 1743035 - download_size: 3516441239 + download_size: 2713949281 dataset_size: 4539944448 - config_name: 20220301.simple features: @@ -416,7 +416,7 @@ dataset_info: - name: train num_bytes: 235072360 num_examples: 205328 - download_size: 239682796 + download_size: 133886521 dataset_size: 235072360 config_names: - 20220301.aa @@ -719,6 +719,7 @@ config_names: - 20220301.zh-min-nan - 20220301.zh-yue - 20220301.zu +viewer: false --- # Dataset Card for Wikipedia @@ -762,12 +763,9 @@ The datasets are built from the Wikipedia dump contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.). -The articles are parsed using the ``mwparserfromhell`` tool. - -To load this dataset you need to install Apache Beam and ``mwparserfromhell`` first: - +The articles are parsed using the ``mwparserfromhell`` tool, which can be installed with: ``` -pip install apache_beam mwparserfromhell +pip install mwparserfromhell ``` Then, you can load any subset of Wikipedia per language and per date this way: @@ -775,11 +773,11 @@ Then, you can load any subset of Wikipedia per language and per date this way: ```python from datasets import load_dataset -load_dataset("wikipedia", language="sw", date="20220120", beam_runner=...) +load_dataset("wikipedia", language="sw", date="20220120") ``` -where you can pass as `beam_runner` any Apache Beam supported runner for (distributed) data processing -(see [here](https://beam.apache.org/documentation/runners/capability-matrix/)). -Pass "DirectRunner" to run it on your machine. + +> [!TIP] +> You can specify `num_proc=` in `load_dataset` to generate the dataset in parallel. You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html). @@ -824,39 +822,39 @@ Some subsets of Wikipedia have already been processed by HuggingFace, as you can #### 20220301.de -- **Size of downloaded dataset files:** 6.84 GB -- **Size of the generated dataset:** 9.34 GB -- **Total amount of disk used:** 16.18 GB +- **Size of downloaded dataset files:** 5.34 GB +- **Size of the generated dataset:** 8.91 GB +- **Total amount of disk used:** 14.25 GB #### 20220301.en -- **Size of downloaded dataset files:** 21.60 GB -- **Size of the generated dataset:** 21.26 GB -- **Total amount of disk used:** 42.86 GB +- **Size of downloaded dataset files:** 11.69 GB +- **Size of the generated dataset:** 20.28 GB +- **Total amount of disk used:** 31.96 GB #### 20220301.fr -- **Size of downloaded dataset files:** 5.87 GB -- **Size of the generated dataset:** 7.73 GB -- **Total amount of disk used:** 13.61 GB +- **Size of downloaded dataset files:** 4.22 GB +- **Size of the generated dataset:** 7.38 GB +- **Total amount of disk used:** 11.60 GB #### 20220301.frr -- **Size of downloaded dataset files:** 13.04 MB -- **Size of the generated dataset:** 9.57 MB -- **Total amount of disk used:** 22.62 MB +- **Size of downloaded dataset files:** 4.53 MB +- **Size of the generated dataset:** 9.13 MB +- **Total amount of disk used:** 13.66 MB #### 20220301.it -- **Size of downloaded dataset files:** 3.69 GB -- **Size of the generated dataset:** 4.76 GB -- **Total amount of disk used:** 8.45 GB +- **Size of downloaded dataset files:** 2.71 GB +- **Size of the generated dataset:** 4.54 GB +- **Total amount of disk used:** 7.25 GB #### 20220301.simple -- **Size of downloaded dataset files:** 251.32 MB -- **Size of the generated dataset:** 246.49 MB -- **Total amount of disk used:** 497.82 MB +- **Size of downloaded dataset files:** 133.89 MB +- **Size of the generated dataset:** 235.07 MB +- **Total amount of disk used:** 368.96 MB ### Data Fields 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def _split_generators(self, dl_manager, pipeline): - def _base_url(lang): - return _BASE_URL_TMPL.format(lang=lang.replace("-", "_"), date=self.config.date) - - lang = self.config.language - - info_url = _base_url(lang) + _INFO_FILE - # Use dictionary since testing mock always returns the same result. - downloaded_files = dl_manager.download_and_extract({"info": info_url}) - - xml_urls = [] - total_bytes = 0 - with open(downloaded_files["info"], encoding="utf-8") as f: - dump_info = json.load(f) - multistream_dump_info = dump_info["jobs"]["articlesmultistreamdump"] - assert ( - multistream_dump_info["status"] == "done" - ), "Specified dump (%s) multistream status is not 'done': %s" % ( - _base_url(lang), - multistream_dump_info["status"], - ) + def _split_generators(self, dl_manager): + if self.config.name in PROCESSED_CONFIG_TO_NUM_SHARDS: + num_shards = PROCESSED_CONFIG_TO_NUM_SHARDS[self.config.name] + parquet_urls = [ + os.path.join("data", self.config.name, f"train-{i:05d}-of-{num_shards:05d}.parquet") + for i in range(num_shards) + ] + # Use dictionary since testing mock always returns the same result. + downloaded_files = dl_manager.download({"parquet": parquet_urls}) + files = downloaded_files["parquet"] + is_processed = True + else: + def _base_url(lang): + return _BASE_URL_TMPL.format(lang=lang.replace("-", "_"), date=self.config.date) - for fname, info in multistream_dump_info["files"].items(): - if ".xml" not in fname: - continue - total_bytes += info["size"] - xml_urls.append(_base_url(lang) + fname) + lang = self.config.language + info_url = _base_url(lang) + _INFO_FILE # Use dictionary since testing mock always returns the same result. - downloaded_files = dl_manager.download({"xml": xml_urls}) - if not pipeline.is_local(): - downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline) + downloaded_files = dl_manager.download_and_extract({"info": info_url}) + + xml_urls = [] + total_bytes = 0 + with open(downloaded_files["info"], encoding="utf-8") as f: + dump_info = json.load(f) + multistream_dump_info = dump_info["jobs"]["articlesmultistreamdump"] + assert ( + multistream_dump_info["status"] == "done" + ), "Specified dump (%s) multistream status is not 'done': %s" % ( + _base_url(lang), + multistream_dump_info["status"], + ) + + for fname, info in multistream_dump_info["files"].items(): + if ".xml" not in fname: + continue + total_bytes += info["size"] + xml_urls.append(_base_url(lang) + fname) + + # Use dictionary since testing mock always returns the same result. + downloaded_files = dl_manager.download({"xml": xml_urls}) + files = downloaded_files["xml"] + is_processed = False return [ datasets.SplitGenerator( # pylint:disable=g-complex-comprehension - name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files["xml"], "language": lang} + name=datasets.Split.TRAIN, gen_kwargs={"files": files, "is_processed": is_processed} ) ] - def _build_pcollection(self, pipeline, filepaths, language): - """Build PCollection of examples in the raw (text) form.""" - import apache_beam as beam - import mwparserfromhell - - def _extract_content(filepath): - """Extracts article content from a single WikiMedia XML file.""" - logger.info("generating examples from = %s", filepath) - with beam.io.filesystems.FileSystems.open(filepath) as f: - f = bz2.BZ2File(filename=f) - # Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563 - utf_f = codecs.getreader("utf-8")(f) - context = etree.iterparse(utf_f, events=("end",)) - for unused_event, elem in context: - if not elem.tag.endswith("page"): - continue - namespace = elem.tag[:-4] - title = elem.find(f"./{namespace}title").text - ns = elem.find(f"./{namespace}ns").text - id_ = elem.find(f"./{namespace}id").text - red_ = elem.find(f"./{namespace}redirect") - - # Filter pages that are not in the "main" namespace. - if ns != "0": - elem.clear() - continue - - raw_content = elem.find(f"./{namespace}revision/{namespace}text").text - elem.clear() - - # Filter redirects. - if raw_content is None or red_ is not None: - beam.metrics.Metrics.counter(language, "filtered-redirects").inc() + def _generate_tables(self, files, is_processed): + if is_processed: + batch_idx = 0 + for file in files: + with open(file, "rb") as f: + f = pq.ParquetFile(f) + for batch in f.iter_batches(batch_size=self.BATCH_SIZE): + yield batch_idx, pa.Table.from_batches([batch]) + batch_idx += 1 + else: + batch = [] + batch_idx = 0 + for file in files: + logger.info("generating examples from = %s", file) + for content in _extract_content(file): + example = _clean_content(content, self.config.language) + if example is None: continue + batch.append(example) + if len(batch) >= self.BATCH_SIZE: + pa_table = pa.Table.from_pylist(batch) + yield batch_idx, pa_table + batch = [] + batch_idx += 1 - beam.metrics.Metrics.counter(language, "extracted-examples").inc() - yield (id_, title, raw_content) + if batch: + pa_table = pa.Table.from_pylist(batch) + yield batch_idx, pa_table - def _clean_content(inputs, language): - """Cleans raw wikicode to extract text.""" - id_, title, raw_content = inputs - try: - text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell, language=language) - except (mwparserfromhell.parser.ParserError) as e: - beam.metrics.Metrics.counter(language, "parser-error").inc() - logger.error("mwparserfromhell ParseError: %s", e) - return - if not text: - beam.metrics.Metrics.counter(language, "empty-clean-examples").inc() - return +def _extract_content(file): + """Extracts article content from a single WikiMedia XML file.""" + print("Extracting content from", file) + with open(file, "rb") as f: + f = bz2.BZ2File(filename=f) + # Workaround due to: https://github.com/tensorflow/tensorflow/issues/33563 + utf_f = codecs.getreader("utf-8")(f) + context = etree.iterparse(utf_f, events=("end",)) + for unused_event, elem in context: + if not elem.tag.endswith("page"): + continue + namespace = elem.tag[:-4] + title = elem.find(f"./{namespace}title").text + ns = elem.find(f"./{namespace}ns").text + id_ = elem.find(f"./{namespace}id").text + red_ = elem.find(f"./{namespace}redirect") - url = _construct_url(title, language) + # Filter pages that are not in the "main" namespace. + if ns != "0": + elem.clear() + continue - beam.metrics.Metrics.counter(language, "cleaned-examples").inc() + raw_content = elem.find(f"./{namespace}revision/{namespace}text").text + elem.clear() - yield id_, {"id": id_, "url": url, "title": title, "text": text} + # Filter redirects. + if raw_content is None or red_ is not None: + continue - return ( - pipeline - | "Initialize" >> beam.Create(filepaths) - | "Extract content" >> beam.FlatMap(_extract_content) - | "Distribute" >> beam.transforms.Reshuffle() - | "Clean content" >> beam.FlatMap(_clean_content, language=language) - ) + yield id_, title, raw_content def _parse_and_clean_wikicode(raw_content, parser, language): @@ -1102,3 +1125,22 @@ def _parse_and_clean_wikicode(raw_content, parser, language): def _construct_url(title, language): # See: https://meta.wikimedia.org/wiki/Help:URL return f"https://{language}.wikipedia.org/wiki/{quote(title)}" + + +def _clean_content(inputs, language): + """Cleans raw wikicode to extract text.""" + import mwparserfromhell + + id_, title, raw_content = inputs + try: + text = _parse_and_clean_wikicode(raw_content, parser=mwparserfromhell, language=language) + except (mwparserfromhell.parser.ParserError) as e: + logger.error("mwparserfromhell ParseError: %s", e) + return + + if not text: + return + + url = _construct_url(title, language) + + return {"id": id_, "url": url, "title": title, "text": text} \ No newline at end of file