Update files from the datasets library (from 1.16.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.16.0

.gitattributes

@@ -0,0 +1,27 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.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
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,191 @@
+---
+pretty_name: IndoNLI
+annotations_creators:
+- expert-generated
+- crowdsourced
+language_creators:
+- expert-generated
+languages:
+- id
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- natural-language-inference
+paperswithcode_id: indonli
+---
+
+# Dataset Card for IndoNLI
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [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)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Repository:** [GitHub](https://github.com/ir-nlp-csui/indonli)
+- **Paper:** [EMNLP 2021](https://aclanthology.org/2021.emnlp-main.821/)
+- **Point of Contact:** [GitHub](https://github.com/ir-nlp-csui/indonli)
+
+### Dataset Summary
+
+IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian. 
+IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning.
+
+### Supported Tasks and Leaderboards
+
+- Natural Language Inference for Indonesian
+
+### Languages
+
+Indonesian
+
+## Dataset Structure
+
+### Data Instances
+
+An example of `train` looks as follows.
+
+```
+{
+  "premise": "Keindahan alam yang terdapat di Gunung Batu Jonggol ini dapat Anda manfaatkan sebagai objek fotografi yang cantik.", 
+  "hypothesis": "Keindahan alam tidak dapat difoto.", 
+  "label": 2
+}
+```
+### Data Fields
+
+The data fields are:
+- `premise`: a `string` feature
+- `hypothesis`: a `string` feature
+- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
+
+### Data Splits
+
+The data is split across `train`, `valid`, `test_lay`, and `test_expert`. 
+
+`test_expert` is written by expert annotators, whereas the rest are written by lay annotators.
+
+|   split   | # examples |
+|----------|-------:|
+|train|   10330|
+|valid|   2197|
+|test_lay|   2201|
+|test_expert|   2984|
+
+A small subset of `test_expert` is used as a diasnostic tool. For more info, please visit https://github.com/ir-nlp-csui/indonli
+
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+Indonesian NLP is considered under-resourced. Up until now, there is no publicly available human-annotated NLI dataset for Indonesian.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The premise were collected from Indonesian Wikipedia and from other public Indonesian dataset: Indonesian PUD and GSD treebanks provided by the [Universal Dependencies 2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) and [IndoSum](https://github.com/kata-ai/indosum)
+
+The hypothesis were written by annotators.
+
+#### Who are the source language producers?
+
+The data was produced by humans.
+
+### Annotations
+
+#### Annotation process
+
+We start by writing the hypothesis, given the premise and the target label. Then, we ask 2 different independent annotators to predict the label, given the premise and hypothesis. If all 3 (the original hypothesis + 2 independent annotators) agree with the label, then the annotation process ends for that sample. Otherwise, we incrementally ask additional annotator until  3 annotators agree with the label. If there's no majority concensus after 5 annotations, the sample is removed.
+
+#### Who are the annotators?
+
+Lay annotators were computer science students, and expert annotators were NLP scientists with 7+ years research experience in NLP. All annotators are native speakers.
+Additionally, expert annotators were explicitly instructed to provide challenging examples by incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. Annotators were compensated based on hourly rate.
+
+### Personal and Sensitive Information
+
+There might be some personal information coming from Wikipedia and news, especially the information of famous/important people.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+INDONLI is created using premise sentences taken from Wikipedia and news. These data sources may contain some bias.
+
+### Other Known Limitations
+
+No other known limitations
+
+## Additional Information
+
+### Dataset Curators
+
+This dataset is the result of the collaborative work of Indonesian researchers from the University of Indonesia, kata.ai, New York University, Fondazione Bruno Kessler, and the University of St Andrews.
+
+
+### Licensing Information
+
+CC-BY-SA 4.0.
+
+Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
+
+ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
+
+No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
+
+Please contact authors for any information on the dataset.
+
+### Citation Information
+
+```
+@inproceedings{mahendra-etal-2021-indonli,
+    title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian",
+    author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2021",
+    address = "Online and Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.emnlp-main.821",
+    pages = "10511--10527",
+}
+```
+
+### Contributions
+
+Thanks to [@afaji](https://github.com/afaji) for adding this dataset.

dataset_infos.json

@@ -0,0 +1 @@
+{"indonli": {"description": "  IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian.\n  IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set.\n  It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning.\n", "citation": "  @inproceedings{mahendra-etal-2021-indonli,\n    title = \"{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian\",\n    author = \"Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara\",\n    booktitle = \"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing\",\n    month = nov,\n    year = \"2021\",\n    address = \"Online and Punta Cana, Dominican Republic\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.emnlp-main.821\",\n    pages = \"10511--10527\",\n  }\n", "homepage": "https://github.com/ir-nlp-csui/indonli", "license": "\n  CC BY-SA 4.0\n\n  Attribution \u2014 You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\n  ShareAlike \u2014 If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.\n\n  No additional restrictions \u2014 You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.\n\n", "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, "task_templates": null, "builder_name": "indo_nli", "config_name": "indonli", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2265687, "num_examples": 10330, "dataset_name": "indo_nli"}, "validation": {"name": "validation", "num_bytes": 465299, "num_examples": 2197, "dataset_name": "indo_nli"}, "test_lay": {"name": "test_lay", "num_bytes": 473849, "num_examples": 2201, "dataset_name": "indo_nli"}, "test_expert": {"name": "test_expert", "num_bytes": 911916, "num_examples": 2984, "dataset_name": "indo_nli"}}, "download_checksums": {"https://raw.githubusercontent.com/ir-nlp-csui/indonli/main/data/indonli/train.jsonl": {"num_bytes": 3788819, "checksum": "c18a2974a8683d283d0c8eb3d944354d3c5ce54ab6232613d932c0bd2a82abf8"}, "https://raw.githubusercontent.com/ir-nlp-csui/indonli/main/data/indonli/val.jsonl": {"num_bytes": 784776, "checksum": "56a553253453bb710fde569b72b0c78a2bc52898ed6eb92b7b6d695f42ed18f1"}, "https://raw.githubusercontent.com/ir-nlp-csui/indonli/main/data/indonli/test_lay.jsonl": {"num_bytes": 796010, "checksum": "e907d015dd05b3ecb56620f680eaaf0e416f5c88b3416ad541892d872292f4d1"}, "https://raw.githubusercontent.com/ir-nlp-csui/indonli/main/data/indonli/test_expert.jsonl": {"num_bytes": 1608272, "checksum": "70a798b476c0753c36b7f8cb0b15b69797f5679cc50988089699c47f718e01b1"}}, "download_size": 6977877, "post_processing_size": null, "dataset_size": 4116751, "size_in_bytes": 11094628}}

dummy/indonli/1.1.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8dc2d28bae31a95950edcada96d4f03c1485390b37b23cacf94029a3ea548a09
+size 3745

indonli.py

@@ -0,0 +1,134 @@
+# 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.
+"""TODO: Add a description here."""
+
+
+import json
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+  @inproceedings{mahendra-etal-2021-indonli,
+    title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian",
+    author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2021",
+    address = "Online and Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.emnlp-main.821",
+    pages = "10511--10527",
+  }
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+  IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian.
+  IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set.
+  It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/ir-nlp-csui/indonli"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = """
+  CC BY-SA 4.0
+
+  Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
+
+  ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
+
+  No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
+
+"""
+
+_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/ir-nlp-csui/indonli/main/data/indonli/train.jsonl"
+
+_VALID_DOWNLOAD_URL = "https://raw.githubusercontent.com/ir-nlp-csui/indonli/main/data/indonli/val.jsonl"
+
+_TEST_LAY_DOWNLOAD_URL = "https://raw.githubusercontent.com/ir-nlp-csui/indonli/main/data/indonli/test_lay.jsonl"
+
+_TEST_EXPERT_DOWNLOAD_URL = "https://raw.githubusercontent.com/ir-nlp-csui/indonli/main/data/indonli/test_expert.jsonl"
+
+
+class IndoNLIConfig(datasets.BuilderConfig):
+    """BuilderConfig for IndoNLI Config"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for IndoNLI Config.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(IndoNLIConfig, self).__init__(**kwargs)
+
+
+class IndoNLI(datasets.GeneratorBasedBuilder):
+    """IndoNLI dataset -- Dataset providing natural language inference for Indonesian"""
+
+    BUILDER_CONFIGS = [
+        IndoNLIConfig(
+            name="indonli",
+            version=datasets.Version("1.1.0"),
+            description="IndoNLI: A Natural Language Inference Dataset for Indonesian",
+        ),
+    ]
+
+    def _info(self):
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "premise": datasets.Value("string"),
+                    "hypothesis": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=["entailment", "neutral", "contradiction"]),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+        valid_path = dl_manager.download_and_extract(_VALID_DOWNLOAD_URL)
+        test_lay_path = dl_manager.download_and_extract(_TEST_LAY_DOWNLOAD_URL)
+        test_expert_path = dl_manager.download_and_extract(_TEST_EXPERT_DOWNLOAD_URL)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
+            datasets.SplitGenerator(name=datasets.Split("test_lay"), gen_kwargs={"filepath": test_lay_path}),
+            datasets.SplitGenerator(name=datasets.Split("test_expert"), gen_kwargs={"filepath": test_expert_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+
+        with open(filepath, encoding="utf-8") as jsonl_file:
+            for id_, row in enumerate(jsonl_file):
+                row_jsonl = json.loads(row)
+                yield id_, {
+                    "premise": row_jsonl["premise"],
+                    "hypothesis": row_jsonl["hypothesis"],
+                    "label": {"e": "entailment", "n": "neutral", "c": "contradiction"}[row_jsonl["label"]],
+                }