add script

ted_talks.py

@@ -0,0 +1,153 @@
+import datasets
+
+
+_DESCRIPTION = """\
+Train, validation and test splits for TED talks as in http://phontron.com/data/ted_talks.tar.gz (detokenized)
+"""
+
+_CITATION = """\
+@inproceedings{Ye2018WordEmbeddings,
+  author  = {Ye, Qi and Devendra, Sachan and Matthieu, Felix and Sarguna, Padmanabhan and Graham, Neubig},
+  title   = {When and Why are pre-trained word embeddings useful for Neural Machine Translation},
+  booktitle = {HLT-NAACL},
+  year    = {2018},
+  }
+"""
+
+_DATA_URL = "data/TED.tar"
+
+_LANGUAGES = ["ar", "az", "be", "bg", "bn", "bs", "cs", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fr-ca", "gl", "he", "hi", "hr", "hu", "hy", "id", "it", "ja", "ka", "kk", "ko", "ku", "lt", "mk", "mn", "mr", "ms", "my", "nb", "nl", "pl", "pt", "pt-br", "ro", "ru", "sk", "sl", "sq", "sr", "sv", "ta", "th", "tr", "uk", "ur", "vi", "zh", "zh-cn", "zh-tw"]
+
+
+class TedTalksConfig(datasets.BuilderConfig):
+    """BuilderConfig for TED talk dataset."""
+
+    def __init__(self, language_pair=(None, None), **kwargs):
+        # sort such that az_ar is same as ar_az
+        self.language_pair = sorted(language_pair)
+        self.source, self.target = self.language_pair[0], self.language_pair[1]
+
+        name = f"{self.source}_{self.target}"
+        description = f"Parallel sentences in `{self.source}` and `{self.target}`."
+        super(TedTalksConfig, self).__init__(name=name, description=description, **kwargs)
+
+
+class TedTalks(datasets.GeneratorBasedBuilder):
+    """TED talk data from http://phontron.com/data/ted_talks.tar.gz."""
+
+    unique_pairs = sorted(set([
+        "_".join(sorted([l1, l2]))
+        for l1 in _LANGUAGES
+        for l2 in _LANGUAGES
+        if l1 != l2
+    ]))
+
+    BUILDER_CONFIGS = [
+        TedTalksConfig(
+            language_pair=(pair.split("_")[0], pair.split("_")[1]),
+            version=datasets.Version("1.0.0", ""),
+        )
+        for pair in unique_pairs
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {"translation": datasets.features.Translation(languages=self.config.language_pair)}
+            ),
+            homepage="https://github.com/neulab/word-embeddings-for-nmt",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive = dl_manager.download(_DATA_URL)
+
+        def _get_overlap(source_file, target_file):
+            for path, f in dl_manager.iter_archive(archive):
+                if path == source_file:
+                    source_sentences = f.read().decode("utf-8").split("\n")
+                elif path == target_file:
+                    target_sentences = f.read().decode("utf-8").split("\n")
+
+            return len([
+                (src, tgt)
+                for src, tgt
+                in zip(source_sentences, target_sentences)
+                if src != "" and tgt != ""
+            ])
+
+        split2tedsplit = {"train": "train", "validation": "dev", "test": "test"}
+
+        overlap = {
+            split: _get_overlap(
+                f"{split}/ted.{split2tedsplit[split]}.{self.config.source}",
+                f"{split}/ted.{split2tedsplit[split]}.{self.config.target}"
+            )   for split in ["train", "validation", "test"]
+        }
+
+        generators = []
+        if overlap["train"] > 0:
+            generators.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "source_file": f"train/ted.train.{self.config.source}",
+                        "target_file": f"train/ted.train.{self.config.target}",
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+            )
+        if overlap["validation"] > 0:
+            generators.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "source_file": f"validation/ted.dev.{self.config.source}",
+                        "target_file": f"validation/ted.dev.{self.config.target}",
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+            )   
+        if overlap["test"] > 0:
+            generators.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "source_file": f"test/ted.test.{self.config.source}",
+                        "target_file": f"test/ted.test.{self.config.target}",
+                        "files": dl_manager.iter_archive(archive),
+                    },
+                ),
+            ) 
+
+        return generators
+
+
+    def _generate_examples(self, source_file, target_file, files):
+        """Returns examples as raw text."""
+
+        source_sentences, target_sentences = None, None
+        for path, f in files:
+            if path == source_file:
+                source_sentences = f.read().decode("utf-8").split("\n")
+            elif path == target_file:
+                target_sentences = f.read().decode("utf-8").split("\n")
+
+        assert len(target_sentences) == len(source_sentences), (
+            f"Sizes do not match: {len(source_sentences)} vs {len(target_sentences)}." 
+        )
+
+        # ignore empty
+        source_target_pairs = [
+            (src, tgt)
+            for src, tgt
+            in zip(source_sentences, target_sentences)
+            if src != "" and tgt != ""
+        ]
+
+        if len(source_target_pairs) > 0:
+            source_sentences, target_sentences = zip(*source_target_pairs)
+
+            for idx, (l1, l2) in enumerate(zip(source_sentences, target_sentences)):
+                yield idx, {"translation": {self.config.source: l1, self.config.target: l2}}