Add loading script

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."""
+
+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
+"""
+
+
+def generate_examples_triples(filepath, collection_path, queries_path):
+    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
+
+    with open(filepath, encoding="utf-8") as f:
+        for (idx, line) in enumerate(f):
+            query_id, pos_id, neg_id = line.rstrip().split("\t")
+            features = {
+                "query": queries[query_id],
+                "positive": collection[pos_id],
+                "negative": collection[neg_id],
+            }
+            yield idx, features
+
+
+def generate_examples_tuples(filepath):
+    with open(filepath, encoding="utf-8") as f:
+        for (idx, line) in enumerate(f):
+            idx, text = line.rstrip().split("\t")
+            features = {
+                "id": idx,
+                "text": text,
+            }
+            yield idx, features
+
+
+def generate_examples_runs(filepath, collection_path, queries_path):
+    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
+
+    qid_to_ranked_candidate_passages = {}
+    with open(filepath, encoding="utf-8") as f:
+        for line in f:
+            qid, pid, rank = line.rstrip().split("\t")
+            if qid not in qid_to_ranked_candidate_passages:
+                qid_to_ranked_candidate_passages[qid] = []
+            qid_to_ranked_candidate_passages[qid].append(pid)
+
+    for (idx, qid) in enumerate(qid_to_ranked_candidate_passages):
+        features = {
+            "id": qid,
+            "query": queries[qid],
+            "passages": [
+                {
+                    "id": pid,
+                    "passage": collection[pid],
+                }
+                for pid in qid_to_ranked_candidate_passages[qid]
+            ],
+        }
+        yield idx, features
+
+
+_BASE_URLS = {
+    "collections": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/v2/collections/",
+    "queries-train": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/v2/queries/train/",
+    "queries-dev": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/v2/queries/dev/",
+    "runs": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/v2/runs/",
+    "train": "https://huggingface.co/datasets/unicamp-dl/mmarco/resolve/main/data/triples.train.ids.small.tsv",
+}
+
+LANGUAGES = [
+    "chinese",
+    "english",
+    "french",
+    "german",
+    "indonesian",
+    "italian",
+    "portuguese",
+    "russian",
+    "spanish",
+]
+
+
+class MMarco(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = (
+        [
+            datasets.BuilderConfig(
+                name=language,
+                description=f"{language.capitalize()} version v2",
+                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
+        ]
+    )
+
+    DEFAULT_CONFIG_NAME = "english"
+
+    def _info(self):
+        name = self.config.name
+        if name.startswith("collection") or name.startswith("queries"):
+            features = {
+                "id": datasets.Value("int32"),
+                "text": datasets.Value("string"),
+            }
+        elif name.startswith("runs"):
+            features = {
+                "id": datasets.Value("int32"),
+                "query": datasets.Value("string"),
+                "passages": datasets.Sequence(
+                    {
+                        "id": datasets.Value("int32"),
+                        "passage": datasets.Value("string"),
+                    }
+                ),
+            }
+        else:
+            features = {
+                "query": datasets.Value("string"),
+                "positive": datasets.Value("string"),
+                "negative": 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."""
+
+        if self.config.name.startswith("collection"):
+            url = _BASE_URLS["collections"] + self.config.name[11:] + "_collection.tsv"
+            dl_path = dl_manager.download_and_extract(url)
+            return (datasets.SplitGenerator(name="collection", gen_kwargs={"filepath": dl_path}),)
+        elif self.config.name.startswith("queries"):
+            urls = {
+                "train": _BASE_URLS["queries-train"] + self.config.name[8:] + "_queries.train.tsv",
+                "dev": _BASE_URLS["queries-dev"] + self.config.name[8:] + "_queries.dev.tsv",
+            }
+            dl_path = dl_manager.download_and_extract(urls)
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["dev"]}),
+            ]
+        elif self.config.name.startswith("runs"):
+            urls = {
+                "collection": _BASE_URLS["collections"] + self.config.name[5:] + "_collection.tsv",
+                "queries": _BASE_URLS["queries-dev"] + self.config.name[5:] + "_queries.dev.tsv",
+                "run": _BASE_URLS["runs"] + "run.bm25_" + self.config.name[5:] + ".txt",
+            }
+
+            dl_path = dl_manager.download_and_extract(urls)
+            return (
+                datasets.SplitGenerator(
+                    name="bm25",
+                    gen_kwargs={
+                        "filepath": dl_path["run"],
+                        "args": {
+                            "collection": dl_path["collection"],
+                            "queries": dl_path["queries"],
+                        },
+                    },
+                ),
+            )
+        else:
+            urls = {
+                "collection": _BASE_URLS["collections"] + self.config.name + "_collection.tsv",
+                "queries": _BASE_URLS["queries-train"] + self.config.name + "_queries.train.tsv",
+                "train": _BASE_URLS["train"],
+            }
+            dl_path = dl_manager.download_and_extract(urls)
+
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": dl_path["train"],
+                        "args": {
+                            "collection": dl_path["collection"],
+                            "queries": dl_path["queries"],
+                        },
+                    },
+                )
+            ]
+
+    def _generate_examples(self, filepath, args=None):
+        """Yields examples."""
+
+        if self.config.name.startswith("collection") or self.config.name.startswith("queries"):
+            return generate_examples_tuples(filepath)
+        if self.config.name.startswith("runs"):
+            return generate_examples_runs(filepath, args["collection"], args["queries"])
+        else:
+            return generate_examples_triples(filepath, args["collection"], args["queries"])