Upload liv4ever.py

liv4ever.py

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+# coding=utf-8
+# Copyright 2020 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.
+"""Liv4ever dataset."""
+
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{rikters-etal-2022,
+    title = "Machine Translation for Livonian: Catering for 20 Speakers",
+    author = "Rikters, Matīss  and
+      Tomingas, Marili and
+      Tuisk, Tuuli and
+      Valts, Ernštreits  and
+      Fishel, Mark",
+    booktitle = "Proceedings of ACL 2022",
+    year = "2022",
+    address = "Dublin, Ireland",
+    publisher = "Association for Computational Linguistics"
+}
+"""
+
+
+_DESCRIPTION = """\
+Livonian is one of the most endangered languages in Europe with just a tiny handful of speakers and virtually no publicly available corpora. 
+In this paper we tackle the task of developing neural machine translation (NMT) between Livonian and English, with a two-fold aim: on one hand, 
+preserving the language and on the other – enabling access to Livonian folklore, lifestories and other textual intangible heritage as well as 
+making it easier to create further parallel corpora. We rely on Livonian's linguistic similarity to Estonian and Latvian and collect parallel 
+and monolingual data for the four languages for translation experiments. We combine different low-resource NMT techniques like zero-shot translation, 
+cross-lingual transfer and synthetic data creation to reach the highest possible translation quality as well as to find which base languages are 
+empirically more helpful for transfer to Livonian. The resulting NMT systems and the collected monolingual and parallel data, including a manually 
+translated and verified translation benchmark, are publicly released.
+
+Fields:
+- source: source of the data
+- en: sentence in English
+- liv: sentence in Livonian
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/tartuNLP/liv4ever-data"
+
+_LICENSE = "CC BY-NC-SA 4.0"
+
+_REPO = "https://huggingface.co/datasets/tartuNLP/liv4ever/raw/main/"
+
+_URLs = {
+    "train": _REPO + "train.json",
+    "dev": _REPO + "dev.json",
+    "test": _REPO + "test.json",
+}
+
+
+class liv4ever(datasets.GeneratorBasedBuilder):
+    """Liv4ever dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "source": datasets.Value("string"),
+                "en": datasets.Value("string"),
+                "liv: datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URLs)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_dir["test"], "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir["dev"],
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+                
+                
+            for dialogue in data:
+                source = dialogue["source"]
+                sentences = dialogue["sentences"]
+                
+                i=0
+
+                for turn in sentences:
+                    i = i+1
+                    sent_no = i
+                    en = dialogue["en"]
+                    liv = dialogue["liv"]
+
+                    yield f"{sent_no}", {
+                        "no": sent_no,
+                        "source": source,
+                        "en": en,
+                        "liv": liv,
+                    }