Upload cc_aligned_sent.py with huggingface_hub

cc_aligned_sent.py

@@ -0,0 +1,167 @@
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+from datasets.download.download_manager import DownloadManager
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = r"""
+@inproceedings{chaudhary-etal-2019-low,
+    title = "Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings",
+    author = "Chaudhary, Vishrav  and
+      Tang, Yuqing  and
+      Guzm{\'a}n, Francisco  and
+      Schwenk, Holger  and
+      Koehn, Philipp",
+    editor = "Bojar, Ond{\v{r}}ej  and
+      Chatterjee, Rajen  and
+      Federmann, Christian  and
+      Fishel, Mark  and
+      Graham, Yvette  and
+      Haddow, Barry  and
+      Huck, Matthias  and
+      Yepes, Antonio Jimeno  and
+      Koehn, Philipp  and
+      Martins, Andr{\'e}  and
+      Monz, Christof  and
+      Negri, Matteo  and
+      N{\'e}v{\'e}ol, Aur{\'e}lie  and
+      Neves, Mariana  and
+      Post, Matt  and
+      Turchi, Marco  and
+      Verspoor, Karin",
+    booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)",
+    month = aug,
+    year = "2019",
+    address = "Florence, Italy",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W19-5435",
+    doi = "10.18653/v1/W19-5435",
+    pages = "261--266",
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind", "jav", "sun", "tha", "vie", "zlm", "lao", "khm", "mya", "ceb"]
+_DATASETNAME = "cc_aligned_sent"
+_DESCRIPTION = """\
+This dataset contains the sentence pairs extracted from CC-Aligned document
+pairs using similarity scores of LASER embeddings (minimum similarity 1.04,
+sorted based on decreasing similarity score). It misses some languages not
+covered by LASER.
+"""
+
+_HOMEPAGE = "https://www2.statmt.org/cc-aligned/"
+_LICENSE = Licenses.UNKNOWN.value
+_URL = "https://data.statmt.org/cc-aligned/sentence-aligned/"
+
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+_SUBSETS = ["id_ID", "jv_ID", "su_ID", "th_TH", "vi_VN", "ms_MY", "lo_LA", "km_KH", "my_MM", "cx_PH"]
+
+
+class CCAlignedSentencesDataset(datasets.GeneratorBasedBuilder):
+    """CC Aligned Sentences dataset by Chaudhary et al., (2019)"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "t2t"
+
+    # Add configurations for loading a dataset per language.
+    dataset_names = sorted([f"{_DATASETNAME}_{subset}" for subset in _SUBSETS])
+    BUILDER_CONFIGS = []
+    for name in dataset_names:
+        source_config = SEACrowdConfig(
+            name=f"{name}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=name,
+        )
+        BUILDER_CONFIGS.append(source_config)
+        seacrowd_config = SEACrowdConfig(
+            name=f"{name}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=name,
+        )
+        BUILDER_CONFIGS.append(seacrowd_config)
+
+    # Choose first language as default
+    first_subset = sorted(_SUBSETS)[0]
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_{first_subset}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "Source_Sentence": datasets.Value("string"),
+                    "Target_Sentence": datasets.Value("string"),
+                    "LASER_similarity": datasets.Value("float64"),
+                }
+            )
+
+        if self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_to_text.features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]:
+        """Return SplitGenerators."""
+        # Define some functions for parsing config and URL names
+        def _split_at_n(text: str, n: int) -> Tuple[str, str]:
+            """Split text on the n-th instance"""
+            return ("_".join(text.split("_")[:n]), "_".join(text.split("_")[n:]))
+
+        # Get URL. For cx_PH, the source and target languages are reversed
+        _, subset = _split_at_n(_split_at_n(self.config.name, 5)[0], 3)
+        (source_lang, target_lang) = (subset, "en_XX") if subset == "cx_PH" else ("en_XX", subset)
+        url = _URL + f"{source_lang}-{target_lang}.tsv.xz"
+        filepath = dl_manager.download_and_extract(url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": filepath,
+                    "source_lang": source_lang,
+                    "target_lang": target_lang,
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, source_lang: str, target_lang: str) -> Tuple[int, Dict]:
+        """Yield examples as (key, example) tuples"""
+        with open(filepath, encoding="utf-8") as file:
+            for idx, row in enumerate(file):
+                text_1, text_2, score = row.strip().split("\t")
+                if self.config.schema == "source":
+                    example = {
+                        "id": idx,
+                        "Source_Sentence": text_1,
+                        "Target_Sentence": text_2,
+                        "LASER_similarity": float(score),
+                    }
+                if self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+                    example = {
+                        "id": idx,
+                        "text_1": text_1,
+                        "text_2": text_2,
+                        "text_1_name": source_lang,
+                        "text_2_name": target_lang,
+                    }
+                yield idx, example