Convert dataset to Parquet (#3)

- Convert dataset to Parquet (530e8f77bbbe5e6c7ed467b51fb9592d645706e2)
- Add 'de-en' config data files (61777f5ebc60a0dc7265f62438e4a05f646a2d50)
- Add 'fr-en' config data files (5c5ccad74b22add8513434f4cd93acb6f4c0e73f)
- Add 'hi-en' config data files (3b83b3828b0a05b19c5f2b4b10b43766319c0fca)
- Add 'ru-en' config data files (cf4fea818b8540cfc93932b10660c79a1914d2a2)
- Delete loading script (52a847b951d4d2e13fec49c3e18acd767f31692d)
- Delete data file (440ceb6fb02a9cb7739418d4a07e93e1c5b84279)
- Delete data file (3cb810bf279f098300d0dcfb2950d8a611a611b3)
- Delete data file (9f90443075577ee391a7bbd9ef9d96bbe9061618)
- Delete data file (b8ea954e24c73a5e463849d73a2f4f81be911e81)
- Delete data file (40eb538c8d2b410c81061759762eb68dc2f27f6d)
- Delete data file (b164bc51901ab5245ee39b13614e052b5b5a2030)
- Delete data file (59cf3723d81222ae140f9aede826839c17cc2861)
- Delete data file (606c8deb56d09a6234cb217a0425c338e308e71f)
- Delete data file (f73b78df474e865d675a9a053668eaa3d0a15aa6)
- Delete data file (560b53fbb585add92eb46605cbd24f79fa81b209)
- Delete data file (f8b3b28dba993effa0b875f1738919152882604a)
- Delete data file (0b645f2ce74d24db7dfed1627b8cc03f7f589602)
- Delete data file (774277eb4f6269d706b938f2685ca588da3c31d7)
- Delete data file (49a28b87d0d3a13d022641c894a224e30b02ef2c)
- Delete data file (987e691cff701293d8d27e6e1ca380ae16818a3f)

README.md

@@ -0,0 +1,134 @@
+---
+dataset_info:
+- config_name: cs-en
+  features:
+  - name: cs
+    dtype: string
+  - name: en
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 5873698
+    num_examples: 19762
+  - name: test
+    num_bytes: 757809
+    num_examples: 3003
+  - name: validation
+    num_bytes: 702465
+    num_examples: 3000
+  download_size: 5009942
+  dataset_size: 7333972
+- config_name: de-en
+  features:
+  - name: de
+    dtype: string
+  - name: en
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 6022832
+    num_examples: 19923
+  - name: test
+    num_bytes: 777326
+    num_examples: 3003
+  - name: validation
+    num_bytes: 736407
+    num_examples: 3000
+  download_size: 5142787
+  dataset_size: 7536565
+- config_name: fr-en
+  features:
+  - name: fr
+    dtype: string
+  - name: en
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 7209551
+    num_examples: 19901
+  - name: test
+    num_bytes: 838849
+    num_examples: 3003
+  - name: validation
+    num_bytes: 744439
+    num_examples: 3000
+  download_size: 5722109
+  dataset_size: 8792839
+- config_name: hi-en
+  features:
+  - name: hi
+    dtype: string
+  - name: en
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1158493
+    num_examples: 19635
+  - name: test
+    num_bytes: 1075008
+    num_examples: 2507
+  - name: validation
+    num_bytes: 181457
+    num_examples: 520
+  download_size: 1309781
+  dataset_size: 2414958
+- config_name: ru-en
+  features:
+  - name: ru
+    dtype: string
+  - name: en
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 5811505
+    num_examples: 19879
+  - name: test
+    num_bytes: 1087738
+    num_examples: 3003
+  - name: validation
+    num_bytes: 977938
+    num_examples: 3000
+  download_size: 4774832
+  dataset_size: 7877181
+configs:
+- config_name: cs-en
+  data_files:
+  - split: train
+    path: cs-en/train-*
+  - split: test
+    path: cs-en/test-*
+  - split: validation
+    path: cs-en/validation-*
+- config_name: de-en
+  data_files:
+  - split: train
+    path: de-en/train-*
+  - split: test
+    path: de-en/test-*
+  - split: validation
+    path: de-en/validation-*
+- config_name: fr-en
+  data_files:
+  - split: train
+    path: fr-en/train-*
+  - split: test
+    path: fr-en/test-*
+  - split: validation
+    path: fr-en/validation-*
+- config_name: hi-en
+  data_files:
+  - split: train
+    path: hi-en/train-*
+  - split: test
+    path: hi-en/test-*
+  - split: validation
+    path: hi-en/validation-*
+- config_name: ru-en
+  data_files:
+  - split: train
+    path: ru-en/train-*
+  - split: test
+    path: ru-en/test-*
+  - split: validation
+    path: ru-en/validation-*
+---

cs-en/test-00000-of-00001.parquet

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+oid sha256:ba77cc3f8550064472d221a0749b229adaf33489037135e2613da81255ce65b8
+size 503708

cs-en/train-00000-of-00001.parquet

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+size 4035513

cs-en/validation-00000-of-00001.parquet

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+size 470721

de-en/test-00000-of-00001.parquet

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+size 505274

de-en/train-00000-of-00001.parquet

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de-en/validation-00000-of-00001.parquet

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+size 471678

fr-en/test-00000-of-00001.parquet

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+size 533175

fr-en/train-00000-of-00001.parquet

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fr-en/validation-00000-of-00001.parquet

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hi-en/test-00000-of-00001.parquet

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hi-en/train-00000-of-00001.parquet

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hi-en/validation-00000-of-00001.parquet

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ru-en/test-00000-of-00001.parquet

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ru-en/train-00000-of-00001.parquet

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ru-en/validation-00000-of-00001.parquet

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wmt_14.py

@@ -1,77 +0,0 @@
-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 WMT14(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"]
-        ]
-
-    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)