Closes #426 | Create dataset loader for CLIRMatrix

seacrowd/sea_datasets/clir_matrix/clir_matrix.py

@@ -0,0 +1,234 @@
+# 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.
+
+from itertools import permutations
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses
+
+_CITATION = """\
+@inproceedings{sun-duh-2020-clirmatrix,
+    title = "{CLIRM}atrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval",
+    author = "Sun, Shuo  and
+      Duh, Kevin",
+    editor = "Webber, Bonnie  and
+      Cohn, Trevor  and
+      He, Yulan  and
+      Liu, Yang",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.emnlp-main.340",
+    doi = "10.18653/v1/2020.emnlp-main.340",
+    pages = "4160--4170",
+}
+"""
+
+_DATASETNAME = "clir_matrix"
+
+_DESCRIPTION = """\
+A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia.
+CLIRMatrix (Cross-Lingual Information Retrieval Matrix) comprises:
+  (1) BI-139, a bilingual dataset of queries in one language matched with relevant documents in another language for 139x138=19,182 language pairs, and
+  (2) MULTI-8, a multilingual dataset of queries and documents jointly aligned in 8 different languages.
+
+Only (1) BI-139 has languages covered in SEACROWD.
+"""
+
+_HOMEPAGE = "https://github.com/ssun32/CLIRMatrix"
+
+_LANGUAGES = ["tgl", "ilo", "min", "jav", "sun", "ceb", "vie", "tha"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_CLIR_LANG = {
+    "tgl": "tl",
+    "jav": "jv",
+    "sun": "su",
+    "vie": "vi",
+    "tha": "th",
+    "ilo": "ilo",
+    "min": "min",
+    "ceb": "ceb",
+}
+_URLS = {
+    ds: {
+        split: {(lque, ldoc): (f"https://www.cs.jhu.edu/~shuosun/clirmatrix/data/BI-139/{ds}/{_CLIR_LANG[lque]}/" f"{_CLIR_LANG[lque]}.{_CLIR_LANG[ldoc]}.{split}{'.base' if ds == 'base' else ''}.jl.gz") for lque, ldoc in permutations(_LANGUAGES, 2)}
+        for split in ["train", "dev", "test1", "test2"]
+    }
+    for ds in ["base", "full"]
+} | {"docs": {ldoc: f"https://www.cs.jhu.edu/~shuosun/clirmatrix/data/DOCS/{_CLIR_LANG[ldoc]}.tsv.gz" for ldoc in _LANGUAGES}}
+
+_SUPPORTED_TASKS = []
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+
+class CLIRMatrixDataset(datasets.GeneratorBasedBuilder):
+    """Cross-Lingual Information Retrieval dataset of 49 million unique queries and 34 billion triplets."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        *[
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}{subset}_source",  # refers to the `base` split in the original paper.
+                version=datasets.Version(_SOURCE_VERSION),
+                description=f"{_DATASETNAME} source schema",
+                schema="source",
+                subset_id=f"{_DATASETNAME}{subset}",
+            )
+            for subset in [f"{'_' if lque else ''}{lque}{'_' if ldoc else ''}{ldoc}" for lque, ldoc in [("", ""), *permutations(_LANGUAGES, 2)]]
+        ],
+        *[
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}{subset}_full_source",  # refers to the `full` split in the original paper.
+                version=datasets.Version(_SOURCE_VERSION),
+                description=f"{_DATASETNAME} full subset source schema",
+                schema="source",
+                subset_id=f"{_DATASETNAME}{subset}_full",
+            )
+            for subset in [f"{'_' if lque else ''}{lque}{'_' if ldoc else ''}{ldoc}" for lque, ldoc in [("", ""), *permutations(_LANGUAGES, 2)]]
+        ],
+        # source-only dataloader
+        # SEACrowdConfig(
+        #     name=f"{_DATASETNAME}_seacrowd_pairs",
+        #     version=SEACROWD_VERSION,
+        #     description=f"{_DATASETNAME} SEACrowd schema",
+        #     schema="seacrowd_pairs",
+        #     subset_id=f"{_DATASETNAME}",
+        # ),
+        # SEACrowdConfig(
+        #     name=f"{_DATASETNAME}_full_seacrowd_pairs",
+        #     version=SEACROWD_VERSION,
+        #     description=f"{_DATASETNAME} full subset SEACrowd schema",
+        #     schema="seacrowd_pairs",
+        #     subset_id=f"{_DATASETNAME}_full",
+        # ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "src_id": datasets.Value("string"),
+                    "src_query": datasets.Value("string"),
+                    "tgt_results": [
+                        {
+                            "doc_id": datasets.Value("string"),
+                            "score": datasets.Value("int32"),
+                            "doc_text": datasets.Value("string"),
+                        }
+                    ],
+                    "lang_query": datasets.Value("string"),
+                    "lang_doc": datasets.Value("string"),
+                }
+            )
+
+        # elif self.config.schema == "seacrowd_[seacrowdschema_name]":
+        # source_only, skipping this.
+        else:
+            raise NotImplementedError()
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        subset_id = self.config.subset_id.split("_")
+
+        urls = _URLS["full" if subset_id[-1] == "full" else "base"]
+        urls_doc = _URLS["docs"]
+
+        # filter subset direction
+        if len(subset_id) > 3:
+            lque, ldoc = subset_id[2:4]
+            urls = {split: {(lque, ldoc): v[(lque, ldoc)]} for split, v in urls.items()}
+            urls_doc = {ldoc: urls_doc[ldoc]}
+
+        data_paths = dl_manager.download_and_extract(urls)
+        doc_paths = dl_manager.download_and_extract(urls_doc)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_paths["train"], "doc_paths": doc_paths},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_paths["test1"], "doc_paths": doc_paths},
+            ),
+            datasets.SplitGenerator(
+                name="test2",
+                gen_kwargs={"filepath": data_paths["test2"], "doc_paths": doc_paths},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_paths["dev"], "doc_paths": doc_paths},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Dict[Tuple, Path], doc_paths: Dict[str, Path]) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        docs_id2txt = {}
+        for ldoc, p in doc_paths.items():
+            docs_id2txt[ldoc] = pd.read_csv(p, sep="\t", dtype=str, header=None).set_index(0).iloc[:, 0]
+
+        if self.config.schema == "source":
+            for (lque, ldoc), fp in filepath.items():
+                df = pd.read_json(fp, orient="records", lines=True)
+                not_found = set()
+                for idx, row in df.iterrows():
+                    ret = row.to_dict()
+                    for doc_id, score in ret["tgt_results"]:
+                        if doc_id not in docs_id2txt[ldoc]:
+                            not_found.add(doc_id)
+                    ret["lang_query"] = lque
+                    ret["lang_doc"] = ldoc
+                    ret["tgt_results"] = [
+                        {
+                            "doc_id": doc_id,
+                            "score": score,
+                            "doc_text": docs_id2txt[ldoc].get(doc_id, ""),
+                            # many doc_id discrepancy, i.e. not found in the tab-separated document files, in particular for Sundanese (sun);
+                        }
+                        for doc_id, score in ret["tgt_results"]
+                    ]
+                    yield f"{lque}_{ldoc}_{idx}", ret
+
+        # source-only dataloader, skipping seacrowd schema.
+        # elif self.config.schema == "seacrowd_[seacrowd_schema_name]":