add data

wmt_14.py

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+import datasets
+import os
+import json
+
+
+_CITATION = ""
+_DESCRIPTION = """
+    The 2014 Workshop on Statistical Machine Translation:
+    https://aclanthology.org/W14-3302.pdf
+
+    The scenario consists of 5 subsets, each of which is a parallel corpus between English and another language. The
+    non-English languages include Czech, German, French, Hindi, and Russian.
+
+    For each language pair, the validation and test set each includes around 3,000 examples, while the training set is
+    usually much larger. We therefore randomly downsample the training set to speedup data processing.
+
+    Task prompt structure:
+
+        Translate {source_language} to {target_language}:
+        {Hypothesis} = {Reference}
+
+    Example from WMT14 Fr-En:
+
+        Hypothesis: Assemblée générale
+        Reference: General Assembly
+"""
+
+class Summarization(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name=name, version=datasets.Version("1.0.0"), description="")
+            for name in ["cs-en", "de-en", "fr-en", "hi-en", "ru-en", "en-en"]
+        ]
+
+    def _info(self):
+        source_language, target_language = self.config.name.split('-')
+        features = datasets.Features(
+            {
+                source_language: datasets.Value("string"),
+                target_language: datasets.Value("string"),
+
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage="",
+            license="",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_json = dl_manager.download(os.path.join(self.config.name, "train.jsonl"))
+        test_json = dl_manager.download(os.path.join(self.config.name, "test.jsonl"))
+        val_json = dl_manager.download(os.path.join(self.config.name, "validation.jsonl"))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"path": train_json},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"path": test_json},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"path": val_json},
+            )
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, path):
+        with open(path, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                yield key, json.loads(row)