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cc100.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+
+"""
+This corpus is an attempt to recreate the dataset used for training XLM-R. This
+corpus comprises of monolingual data for 100+ languages and also includes data
+for romanized languages (indicated by *_rom). This was constructed using the
+urls and paragraph indices provided by the CC-Net repository by processing
+January-December 2018 Commoncrawl snapshots. Each file comprises of documents
+separated by double-newlines and paragraphs within the same document separated
+by a newline. The data is generated using the open source CC-Net repository. No
+claims of intellectual property are made on the work of preparation of the
+corpus.
+
+This contains the Indonesian (ind), the Javanese (jav), and the Sundanese (sun) subset.
+
+[nusantara_schema_name] = ssp
+"""
+
+from posixpath import split
+from typing import Dict, List, Tuple
+
+import datasets
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import (DEFAULT_NUSANTARA_VIEW_NAME,
+                                       DEFAULT_SOURCE_VIEW_NAME, Tasks)
+
+_DATASETNAME = "cc100"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
+
+_LANGUAGES = ["ind", "jav", "sun"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+
+_CITATION = """\
+        @inproceedings{conneau-etal-2020-unsupervised,
+    title = "Unsupervised Cross-lingual Representation Learning at Scale",
+    author = "Conneau, Alexis  and
+      Khandelwal, Kartikay  and
+      Goyal, Naman  and
+      Chaudhary, Vishrav  and
+      Wenzek, Guillaume  and
+      Guzm{'a}n, Francisco  and
+      Grave, Edouard  and
+      Ott, Myle  and
+      Zettlemoyer, Luke  and
+      Stoyanov, Veselin",
+    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.acl-main.747",
+    doi = "10.18653/v1/2020.acl-main.747",
+    pages = "8440--8451",
+    abstract = "This paper shows that pretraining multilingual language models
+    at scale leads to significant performance gains for a wide range of
+    cross-lingual transfer tasks. We train a Transformer-based masked language
+    model on one hundred languages, using more than two terabytes of filtered
+    CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms
+    multilingual BERT (mBERT) on a variety of cross-lingual benchmarks,
+    including +14.6{%} average accuracy on XNLI, +13{%} average F1 score on
+    MLQA, and +2.4{%} F1 score on NER. XLM-R performs particularly well on
+    low-resource languages, improving 15.7{%} in XNLI accuracy for Swahili and
+    11.4{%} for Urdu over previous XLM models. We also present a detailed
+    empirical analysis of the key factors that are required to achieve these
+    gains, including the trade-offs between (1) positive transfer and capacity
+    dilution and (2) the performance of high and low resource languages at
+    scale. Finally, we show, for the first time, the possibility of
+    multilingual modeling without sacrificing per-language performance; XLM-R
+    is very competitive with strong monolingual models on the GLUE and XNLI
+    benchmarks. We will make our code and models publicly available.",
+}
+
+@inproceedings{wenzek-etal-2020-ccnet,
+    title = "{CCN}et: Extracting High Quality Monolingual Datasets from Web Crawl Data",
+    author = "Wenzek, Guillaume  and
+      Lachaux, Marie-Anne  and
+      Conneau, Alexis  and
+      Chaudhary, Vishrav  and
+      Guzm{'a}n, Francisco  and
+      Joulin, Armand  and
+      Grave, Edouard",
+    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://www.aclweb.org/anthology/2020.lrec-1.494",
+    pages = "4003--4012",
+    abstract = "Pre-training text representations have led to significant
+    improvements in many areas of natural language processing. The quality of
+    these models benefits greatly from the size of the pretraining corpora as
+    long as its quality is preserved. In this paper, we describe an automatic
+    pipeline to extract massive high-quality monolingual datasets from Common
+    Crawl for a variety of languages. Our pipeline follows the data processing
+    introduced in fastText (Mikolov et al., 2017; Grave et al., 2018), that
+    deduplicates documents and identifies their language. We augment this
+    pipeline with a filtering step to select documents that are close to high
+    quality corpora like Wikipedia.",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+"""
+
+_DESCRIPTION = """\
+        This corpus is an attempt to recreate the dataset used for training
+        XLM-R. This corpus comprises of monolingual data for 100+ languages and
+        also includes data for romanized languages (indicated by *_rom). This
+        was constructed using the urls and paragraph indices provided by the
+        CC-Net repository by processing January-December 2018 Commoncrawl
+        snapshots. Each file comprises of documents separated by
+        double-newlines and paragraphs within the same document separated by a
+        newline. The data is generated using the open source CC-Net repository.
+        No claims of intellectual property are made on the work of preparation
+        of the corpus.
+"""
+
+_HOMEPAGE = "https://data.statmt.org/cc-100/"
+
+_LICENSE = "MIT"
+
+_LANGUAGES_MAP = {
+    "ind": "id",
+    "jav": "jv",
+    "sun": "su",
+}
+
+_URLS = {
+    "train": "https://data.statmt.org/cc-100/{lang}.txt.xz",
+}
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+
+_SOURCE_VERSION = "2018.12.01"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+def nusantara_config_constructor(lang, schema, version):
+    """Construct NusantaraConfig with cc100_{lang}_{schema} as the name format."""
+    if schema != "source" and schema != "nusantara_ssp":
+        raise ValueError(f"Invalid schema: {schema}")
+
+    if lang == "":
+        raise ValueError(f"Language is required. Choose one of these languages: {_LANGUAGES}.")
+    elif lang in _LANGUAGES:
+        return NusantaraConfig(
+            name=f"cc100_{lang}_{schema}",
+            version=datasets.Version(version),
+            description=f"CC100 with {schema} schema for {lang} language",
+            schema=schema,
+            subset_id="cc100",
+        )
+    else:
+        raise ValueError(f"Invalid language: {lang}. Choose one of these languages: {_LANGUAGES}.")
+
+
+class CC100(datasets.GeneratorBasedBuilder):
+    """Monolingual Datasets from Web Crawl Data."""
+
+    DEFAULT_CONFIG_NAME = "cc100_jav_source"
+
+    BUILDER_CONFIGS = [
+        nusantara_config_constructor(lang, "source", _SOURCE_VERSION) for lang in _LANGUAGES_MAP
+    ] + [
+        nusantara_config_constructor(lang, "nusantara_ssp", _NUSANTARA_VERSION) for lang in _LANGUAGES_MAP
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "nusantara_ssp":
+            features = schemas.self_supervised_pretraining.features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        split_name = self.config.name.split("_")
+        if split_name[1] == "source" or split_name[1] == "nusantara":
+            lang = _DEFAULT_LANGUAGE
+        else:
+            lang = split_name[1]
+        url = _URLS["train"].format(lang=_LANGUAGES_MAP[lang])
+        path = dl_manager.download_and_extract(url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": path,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        with open(filepath, encoding="utf-8") as f:
+            if self.config.schema == "source":
+                for counter, row in enumerate(f):
+                    if row.strip() != "":
+                        yield (
+                            counter,
+                            {
+                                "id": str(counter),
+                                "text": row.strip(),
+                            },
+                        )
+            elif self.config.schema == "nusantara_ssp":
+                for counter, row in enumerate(f):
+                    if row.strip() != "":
+                        yield (
+                            counter,
+                            {
+                                "id": str(counter),
+                                "text": row.strip(),
+                            },
+                        )