init

mmarco.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""mMARCO dataset."""
+
+from gc import collect
+import datasets
+
+
+_CITATION = """
+@misc{bonifacio2021mmarco,
+      title={mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset},
+      author={Luiz Henrique Bonifacio and Israel Campiotti and Vitor Jeronymo and Hugo Queiroz Abonizio and Roberto Lotufo and Rodrigo Nogueira},
+      year={2021},
+      eprint={2108.13897},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_URL = "https://github.com/unicamp-dl/mMARCO"
+
+_DESCRIPTION = """
+mMARCO translated datasets
+"""
+
+
+_BASE_URLS = {
+    "collections": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/collections/",
+    "queries-train": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/queries/train/",
+    "queries-dev": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/queries/dev/",
+    "runs": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/google/runs/",
+    "train": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/triples.train.ids.small.tsv",
+}
+
+LANGUAGES = [
+    "arabic",
+    "chinese",
+    "dutch",
+    "english",
+    "french",
+    "german",
+    "hindi",
+    "indonesian",
+    "italian",
+    "japanese",
+    "portuguese",
+    "russian",
+    "spanish",
+    "vietnamese",
+]
+
+
+class MMarco(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = (
+        [
+            datasets.BuilderConfig(
+                name=language,
+                description=f"{language.capitalize()} triples",
+                version=datasets.Version("2.0.0"),
+            )
+            for language in LANGUAGES
+        ]
+        + [
+            datasets.BuilderConfig(
+                name=f"collection-{language}",
+                description=f"{language.capitalize()} collection version v2",
+                version=datasets.Version("2.0.0"),
+            )
+            for language in LANGUAGES
+        ]
+        + [
+            datasets.BuilderConfig(
+                name=f"queries-{language}",
+                description=f"{language.capitalize()} queries version v2",
+                version=datasets.Version("2.0.0"),
+            )
+            for language in LANGUAGES
+        ]
+        + [
+            datasets.BuilderConfig(
+                name=f"runs-{language}",
+                description=f"{language.capitalize()} runs version v2",
+                version=datasets.Version("2.0.0"),
+            )
+            for language in LANGUAGES
+        ]
+        + [
+            datasets.BuilderConfig(
+                name=f"all",
+                description=f"All training data version v2",
+                version=datasets.Version("2.0.0"),
+            )
+        ]
+    )
+
+    DEFAULT_CONFIG_NAME = "english"
+
+    def _info(self):
+        name = self.config.name
+        assert name in LANGUAGES + ["all"], f"Does not support languge {name}. Must be one of {LANGUAGES}."
+
+        features = {
+            "query_id": datasets.Value("string"),
+            "query": datasets.Value("string"),
+            "positive_passages": [
+                {'docid': datasets.Value('string'), 'text': datasets.Value('string')}
+            ],
+            "negative_passages": [
+                {'docid': datasets.Value('string'), 'text': datasets.Value('string')}
+            ],
+        }
+
+        return datasets.DatasetInfo(
+            description=f"{_DESCRIPTION}\n{self.config.description}",
+            features=datasets.Features(features),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        languages = [self.config.name] if self.config.name in LANGUAGES else LANGUAGES
+        urls = {
+            "collection": {lang: _BASE_URLS["collections"] + lang + "_collection.tsv" for lang in languages},
+            "queries": {lang: _BASE_URLS["queries-train"] + lang + "_queries.train.tsv" for lang in languages}, 
+            "train": _BASE_URLS["train"],
+        }
+        dl_path = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dl_path["train"],
+                    "args": {
+                        "collection": dl_path["collection"],
+                        "queries": dl_path["queries"],
+                    },
+                },
+            )
+        ]
+
+    def _generate_examples(self, files, args=None):
+        """Yields examples."""
+
+        languages = [self.config.name] if self.config.name in LANGUAGES else LANGUAGES
+
+        # loading
+        lang2collection = {}
+        lang2query = {}
+        for lang in languages:
+            collection_path, queries_path = args["collection"][lang], args["queries"][lang]
+
+            collection = {}
+            with open(collection_path, encoding="utf-8") as f:
+                for line in f:
+                    doc_id, doc = line.rstrip().split("\t")
+                    collection[doc_id] = doc
+
+            queries = {}
+            with open(queries_path, encoding="utf-8") as f:
+                for line in f:
+                    query_id, query = line.rstrip().split("\t")
+                    queries[query_id] = query
+            
+            lang2collection[lang] = collection
+            lang2query[lang] = queries
+
+        with open(files, encoding="utf-8") as f:
+            # todo: group the queries
+
+            for (idx, line) in enumerate(f):
+                query_id, pos_id, neg_id = line.rstrip().split("\t")
+                for lang in languages:
+                    features = {
+                        "query_id": query_id,
+                        "query": queries[query_id],
+                        "positive_passages": [{
+                            "docid": pos_id, 
+                            "text": collection[pos_id],
+                        }],
+                        "negative_passages": [{
+                            "docid": neg_id, 
+                            "text": collection[neg_id],
+                        }],
+                    }
+                    yield f"{lang}-{query_id}", features