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.gitattributes

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data/.gitattributes

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+*.tsv.gz filter=lfs diff=lfs merge=lfs -text

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data/validation.tsv.gz

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ik-nlp-22_transqe.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""Dutch translation of the e-SNLI corpus with added quality estimation scores"""
+
+
+import csv
+csv.register_dialect("tsv", delimiter="\t")
+
+import datasets
+
+
+_CITATION = """
+@incollection{NIPS2018_8163,
+title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
+author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
+booktitle = {Advances in Neural Information Processing Systems 31},
+editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
+pages = {9539--9549},
+year = {2018},
+publisher = {Curran Associates, Inc.},
+url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
+}
+"""
+
+_DESCRIPTION = """
+The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
+include human-annotated natural language explanations of the entailment
+relations. This version includes an automatic translation to Dutch and two quality estimation annotations
+for each translated field.
+"""
+
+_HOMEPAGE = "https://www.rug.nl/masters/information-science/?lang=en"
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/raw/main/data/train.tsv",
+    "validation": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/raw/main/data/validation.tsv",
+    "test": "https://huggingface.co/datasets/GroNLP/ik-nlp-22_transqe/raw/main/data/test.tsv",
+}
+
+class IkNlp22ExpNLIConfig(datasets.GeneratorBasedBuilder):
+    """e-SNLI corpus with added translation and quality estimation scores"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text",
+            version=datasets.Version("0.0.2"),
+            description="Plain text import of e-SNLI",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "premise_en": datasets.Value("string"),
+                    "premise_nl": datasets.Value("string"),
+                    "hypothesis_en": datasets.Value("string"),
+                    "hypothesis_nl": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
+                    "explanation_1_en": datasets.Value("string"),
+                    "explanation_1_nl": datasets.Value("string"),
+                    "explanation_2_en": datasets.Value("string"),
+                    "explanation_2_nl": datasets.Value("string"),
+                    "explanation_3_en": datasets.Value("string"),
+                    "explanation_3_nl": datasets.Value("string"),
+                    "da_premise": datasets.Value("float32"),
+                    "mqm_premise": datasets.Value("float32"),
+                    "da_hypothesis": datasets.Value("float32"),
+                    "mqm_hypothesis": datasets.Value("float32"),
+                    "da_explanation_1": datasets.Value("float32"),
+                    "mqm_explanation_1": datasets.Value("float32"),
+                    "da_explanation_2": datasets.Value("float32"),
+                    "mqm_explanation_2": datasets.Value("float32"),
+                    "da_explanation_3": datasets.Value("float32"),
+                    "mqm_explanation_3": datasets.Value("float32"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=name,
+                gen_kwargs={"filepath": filepath},
+            )
+            for name, filepath in files.items()
+        ]
+
+    def _generate_examples(self, files):
+        """Yields examples."""
+        for filepath in files:
+            with open(filepath, encoding="utf-8") as f:
+                reader = csv.DictReader(f, dialect="tsv")
+                for i, row in enumerate(reader):
+                    yield i, {
+                        "premise_en": row["premise_en"],
+                        "premise_nl": row["premise_nl"],
+                        "hypothesis_en": row["hypothesis_en"],
+                        "hypothesis_nl": row["hypothesis_nl"],
+                        "label": row["label"],
+                        "explanation_1_en": row["explanation_1_en"],
+                        "explanation_1_nl": row["explanation_1_nl"],
+                        "explanation_2_en": row["explanation_2_en"],
+                        "explanation_2_nl": row["explanation_2_nl"],
+                        "explanation_3_en": row["explanation_3_en"],
+                        "explanation_3_nl": row["explanation_3_nl"],
+                        "da_premise": row["da_premise"],
+                        "mqm_premise": row["mqm_premise"],
+                        "da_hypothesis": row["da_hypothesis"],
+                        "mqm_hypothesis": row["mqm_hypothesis"],
+                        "da_explanation_1": row["da_explanation_1"],
+                        "mqm_explanation_1": row["mqm_explanation_1"],
+                        "da_explanation_2": row["da_explanation_2"],
+                        "mqm_explanation_2": row["mqm_explanation_2"],
+                        "da_explanation_3": row["da_explanation_3"],
+                        "mqm_explanation_3": row["mqm_explanation_3"]
+                    }