Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
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+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- expert-generated
+- found
+- machine-generated
+languages:
+- ko
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- extended|snli
+task_categories:
+- text-classification
+- text-scoring
+task_ids:
+- natural-language-inference
+- semantic-similarity-scoring
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [Github](https://github.com/kakaobrain/KorNLUDatasets)
+- **Repository:** [Github](https://github.com/kakaobrain/KorNLUDatasets)
+- **Paper:** [Arxiv](https://arxiv.org/abs/2004.03289)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_infos.json

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+{"nli": {"description": "    The dataset contains data for bechmarking korean models on NLI and STS\n", "citation": "    @article{ham2020kornli,\n  title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},\n  author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},\n  journal={arXiv preprint arXiv:2004.03289},\n  year={2020}\n}\n", "homepage": "https://github.com/kakaobrain/KorNLUDatasets", "license": "", "features": {"premise": {"dtype": "string", "id": null, "_type": "Value"}, "hypothesis": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 3, "names": ["entailment", "neutral", "contradiction"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kor_nlu", "config_name": "nli", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 80135707, "num_examples": 550146, "dataset_name": "kor_nlu"}, "validation": {"name": "validation", "num_bytes": 318170, "num_examples": 1570, "dataset_name": "kor_nlu"}, "test": {"name": "test", "num_bytes": 1047250, "num_examples": 4954, "dataset_name": "kor_nlu"}}, "download_checksums": {"https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorNLI/snli_1.0_train.ko.tsv": {"num_bytes": 78486224, "checksum": "aaf12d8955fcda2b14319e5cabb9b31824c6f504aede460c975b518911f484c0"}, "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorNLI/xnli.dev.ko.tsv": {"num_bytes": 511383, "checksum": "821fb1c1c2e538ac2724a6132da36ffa379eb9a542ceb89243b3cf711377382c"}, "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorNLI/xnli.test.ko.tsv": {"num_bytes": 1032430, "checksum": "0db53cf8e182283a0d0404ec3fe1b15d1e29236d23d22082dc20d43067254b3e"}}, "download_size": 80030037, "post_processing_size": null, "dataset_size": 81501127, "size_in_bytes": 161531164}, "sts": {"description": "    The dataset contains data for bechmarking korean models on NLI and STS\n", "citation": "    @article{ham2020kornli,\n  title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},\n  author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},\n  journal={arXiv preprint arXiv:2004.03289},\n  year={2020}\n}\n", "homepage": "https://github.com/kakaobrain/KorNLUDatasets", "license": "", "features": {"genre": {"num_classes": 4, "names": ["main-news", "main-captions", "main-forum", "main-forums"], "names_file": null, "id": null, "_type": "ClassLabel"}, "filename": {"num_classes": 9, "names": ["images", "MSRpar", "MSRvid", "headlines", "deft-forum", "deft-news", "track5.en-en", "answers-forums", "answer-answer"], "names_file": null, "id": null, "_type": "ClassLabel"}, "year": {"num_classes": 7, "names": ["2017", "2016", "2013", "2012train", "2014", "2015", "2012test"], "names_file": null, "id": null, "_type": "ClassLabel"}, "id": {"dtype": "int32", "id": null, "_type": "Value"}, "score": {"dtype": "float32", "id": null, "_type": "Value"}, "sentence1": {"dtype": "string", "id": null, "_type": "Value"}, "sentence2": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "kor_nlu", "config_name": "sts", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1056664, "num_examples": 5703, "dataset_name": "kor_nlu"}, "validation": {"name": "validation", "num_bytes": 305009, "num_examples": 1471, "dataset_name": "kor_nlu"}, "test": {"name": "test", "num_bytes": 249671, "num_examples": 1379, "dataset_name": "kor_nlu"}}, "download_checksums": {"https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorSTS/sts-train.tsv": {"num_bytes": 1046952, "checksum": "b5aaa7f957d6ff46f4b6834a8b0f024a9234a6eefe9289aed66746b4533da3b8"}, "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorSTS/sts-dev.tsv": {"num_bytes": 307276, "checksum": "d835b65b424401fbcce6be28a78159df8d61f5b19977ea7a0b83b8cb6105f393"}, "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorSTS/sts-test.tsv": {"num_bytes": 249596, "checksum": "13f83a3440d9db52d134eb40303ce021b03264aa338fbf2871c8786ce04d2105"}}, "download_size": 1603824, "post_processing_size": null, "dataset_size": 1611344, "size_in_bytes": 3215168}}

dummy/nli/1.0.0/dummy_data.zip

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+size 1576

dummy/sts/1.0.0/dummy_data.zip

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kor_nlu.py

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+"""Korean Dataset for NLI and STS"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+
+import pandas as pd
+
+import datasets
+
+
+_CITATAION = """\
+    @article{ham2020kornli,
+  title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
+  author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
+  journal={arXiv preprint arXiv:2004.03289},
+  year={2020}
+}
+"""
+
+_DESCRIPTION = """\
+    The dataset contains data for bechmarking korean models on NLI and STS
+"""
+
+_URL = "https://github.com/kakaobrain/KorNLUDatasets"
+
+_DATA_URLS = {
+    "nli": {
+        # 'mnli-train': 'https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorNLI/multinli.train.ko.tsv',
+        "snli-train": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorNLI/snli_1.0_train.ko.tsv",
+        "xnli-dev": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorNLI/xnli.dev.ko.tsv",
+        "xnli-test": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorNLI/xnli.test.ko.tsv",
+    },
+    "sts": {
+        "train": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorSTS/sts-train.tsv",
+        "dev": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorSTS/sts-dev.tsv",
+        "test": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorSTS/sts-test.tsv",
+    },
+}
+
+
+class KorNluConfig(datasets.BuilderConfig):
+    """BuilderConfig for korNLU"""
+
+    def __init__(self, description, data_url, citation, url, **kwargs):
+        """
+        Args:
+            description: `string`, brief description of the dataset
+            data_url: `dictionary`, dict with url for each split of data.
+            citation: `string`, citation for the dataset.
+            url: `string`, url for information about the dataset.
+            **kwrags: keyword arguments frowarded to super
+        """
+        super(KorNluConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.description = description
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+
+class KorNlu(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        KorNluConfig(name=name, description=_DESCRIPTION, data_url=_DATA_URLS[name], citation=_CITATAION, url=_URL)
+        for name in ["nli", "sts"]
+    ]
+    BUILDER_CONFIG_CLASS = KorNluConfig
+
+    def _info(self):
+        features = {}
+        if self.config.name == "nli":
+            labels = ["entailment", "neutral", "contradiction"]
+            features["premise"] = datasets.Value("string")
+            features["hypothesis"] = datasets.Value("string")
+            features["label"] = datasets.features.ClassLabel(names=labels)
+
+        if self.config.name == "sts":
+            genre = ["main-news", "main-captions", "main-forum", "main-forums"]
+            filename = [
+                "images",
+                "MSRpar",
+                "MSRvid",
+                "headlines",
+                "deft-forum",
+                "deft-news",
+                "track5.en-en",
+                "answers-forums",
+                "answer-answer",
+            ]
+            year = ["2017", "2016", "2013", "2012train", "2014", "2015", "2012test"]
+
+            features["genre"] = datasets.features.ClassLabel(names=genre)
+            features["filename"] = datasets.features.ClassLabel(names=filename)
+            features["year"] = datasets.features.ClassLabel(names=year)
+            features["id"] = datasets.Value("int32")
+            features["score"] = datasets.Value("float32")
+            features["sentence1"] = datasets.Value("string")
+            features["sentence2"] = datasets.Value("string")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION, features=datasets.Features(features), homepage=_URL, citation=_CITATAION
+        )
+
+    def _split_generators(self, dl_manager):
+        if self.config.name == "nli":
+            # mnli_train = dl_manager.download_and_extract(self.config.data_url['mnli-train'])
+            snli_train = dl_manager.download_and_extract(self.config.data_url["snli-train"])
+            xnli_dev = dl_manager.download_and_extract(self.config.data_url["xnli-dev"])
+            xnli_test = dl_manager.download_and_extract(self.config.data_url["xnli-test"])
+
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN, gen_kwargs={"filepath": snli_train, "split": "train"}
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION, gen_kwargs={"filepath": xnli_dev, "split": "dev"}
+                ),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": xnli_test, "split": "test"}),
+            ]
+
+        if self.config.name == "sts":
+            train = dl_manager.download_and_extract(self.config.data_url["train"])
+            dev = dl_manager.download_and_extract(self.config.data_url["dev"])
+            test = dl_manager.download_and_extract(self.config.data_url["test"])
+
+            return [
+                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train, "split": "train"}),
+                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dev, "split": "dev"}),
+                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}),
+            ]
+
+    def _generate_examples(self, filepath, split):
+        if self.config.name == "nli":
+            df = pd.read_csv(filepath, sep="\t")
+            df = df.dropna()
+            for id_, row in df.iterrows():
+                yield id_, {
+                    "premise": str(row["sentence1"]),
+                    "hypothesis": str(row["sentence2"]),
+                    "label": str(row["gold_label"]),
+                }
+
+        if self.config.name == "sts":
+            with open(filepath, encoding="utf-8") as f:
+                data = csv.DictReader(f, delimiter="\t")
+                for id_, row in enumerate(data):
+                    yield id_, {
+                        "genre": row["genre"],
+                        "filename": row["filename"],
+                        "year": row["year"],
+                        "id": row["id"],
+                        "sentence1": row["sentence1"],
+                        "sentence2": row["sentence2"],
+                        "score": row["score"],
+                    }