Update files from the datasets library (from 1.2.0)

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

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- id
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+  bapos:
+  - 10K<n<100K
+  casa:
+  - 1K<n<10K
+  emot:
+  - 1K<n<10K
+  facqa:
+  - 1K<n<10K
+  hoasa:
+  - n<1K
+  keps:
+  - 1K<n<10K
+  nergrit:
+  - 1K<n<10K
+  nerp:
+  - 1K<n<10K
+  posp:
+  - 1K<n<10K
+  smsa:
+  - 10K<n<100K
+  terma:
+  - 1K<n<10K
+  wrete:
+  - n<1K
+source_datasets:
+- original
+task_categories:
+  bapos:
+  - structure-prediction
+  casa:
+  - text-classification
+  emot:
+  - text-classification
+  facqa:
+  - question-answering
+  hoasa:
+  - text-classification
+  keps:
+  - structure-prediction
+  nergrit:
+  - structure-prediction
+  nerp:
+  - structure-prediction
+  posp:
+  - structure-prediction
+  smsa:
+  - text-classification
+  terma:
+  - structure-prediction
+  wrete:
+  - text-classification
+task_ids:
+  bapos:
+  - structure-prediction-other-part-of-speech-tagging
+  casa:
+  - text-classification-other-aspect-based-sentiment-analysis
+  emot:
+  - multi-class-classification
+  facqa:
+  - closed-domain-qa
+  hoasa:
+  - text-classification-other-aspect-based-sentiment-analysis
+  keps:
+  - structure-prediction-other-keyphrase-extraction
+  nergrit:
+  - named-entity-recognition
+  nerp:
+  - named-entity-recognition
+  posp:
+  - structure-prediction-other-part-of-speech-tagging
+  smsa:
+  - sentiment-classification
+  terma:
+  - structure-prediction-other-span-extraction
+  wrete:
+  - semantic-similarity-classification
+  ---
+
+# Dataset Card for IndoNLU
+
+## 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:** [IndoNLU Website](https://www.indobenchmark.com/)
+- **Repository:** [IndoNLU GitHub](https://github.com/indobenchmark/indonlu)
+- **Paper:** [IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding](https://www.aclweb.org/anthology/2020aacl-main.85.pdf)
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia (Indonesian language).
+There are 12 datasets in IndoNLU benchmark for Indonesian natural language understanding.
+1. `EmoT`: An emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, happy, love, and sadness
+2. `SmSA`: This sentence-level sentiment analysis dataset is a collection of comments and reviews in Indonesian obtained from multiple online platforms. The text was crawled and then annotated by several Indonesian linguists to construct this dataset. There are three possible sentiments on the `SmSA` dataset: positive, negative, and neutral
+3. `CASA`: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms. The dataset covers six aspects of car quality. We define the task to be a multi-label classification task, where each label represents a sentiment for a single aspect with three possible values: positive, negative, and neutral.
+4. `HoASA`: An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, [AiryRooms](https://github.com/annisanurulazhar/absa-playground). The dataset covers ten different aspects of hotel quality. Similar to the `CASA` dataset, each review is labeled with a single sentiment label for each aspect. There are four possible sentiment classes for each sentiment label: positive, negative, neutral, and positive-negative. The positivenegative label is given to a review that contains multiple sentiments of the same aspect but for different objects (e.g., cleanliness of bed and toilet).
+5. `WReTE`: The Wiki Revision Edits Textual Entailment dataset consists of 450 sentence pairs constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic relations between the pairs. The data are labeled as entailed when the meaning of the second sentence can be derived from the first one, and not entailed otherwise.
+6. `POSP`: This Indonesian part-of-speech tagging (POS) dataset is collected from Indonesian news websites. The dataset consists of around 8000 sentences with 26 POS tags. The POS tag labels follow the [Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention](http://inacl.id/inacl/wp-content/uploads/2017/06/INACL-POS-Tagging-Convention-26-Mei.pdf).
+7. `BaPOS`: This POS tagging dataset contains about 1000 sentences, collected from the [PAN Localization Project](http://www.panl10n.net/). In this dataset, each word is tagged by one of [23 POS tag classes](https://bahasa.cs.ui.ac.id/postag/downloads/Tagset.pdf). Data splitting used in this benchmark follows the experimental setting used by [Kurniawan and Aji (2018)](https://arxiv.org/abs/1809.03391).
+8. `TermA`: This span-extraction dataset is collected from the hotel aggregator platform, [AiryRooms](https://github.com/jordhy97/final_project). The dataset consists of thousands of hotel reviews, which each contain a span label for aspect and sentiment words representing the opinion of the reviewer on the corresponding aspect. The labels use Inside-Outside-Beginning (IOB) tagging representation with two kinds of tags, aspect and sentiment.
+9. `KEPS`: This keyphrase extraction dataset consists of text from Twitter discussing banking products and services and is written in the Indonesian language. A phrase containing important information is considered a keyphrase. Text may contain one or more keyphrases since important phrases can be located at different positions. The dataset follows the IOB chunking format, which represents the position of the keyphrase.
+10. `NERGrit`: This NER dataset is taken from the [Grit-ID repository](https://github.com/grit-id/nergrit-corpus), and the labels are spans in IOB chunking representation. The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and ORGANIZATION (name of organization).
+11. `NERP`: This NER dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites. There are five labels available in this dataset, PER (name of person), LOC (name of location), IND (name of product or brand), EVT (name of the event), and FNB (name of food and beverage). Similar to the `TermA` dataset, the `NERP` dataset uses the IOB chunking format.
+12. `FacQA`: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article. Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the corresponding short passage. There are six categories of questions: date, location, name, organization, person, and quantitative.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+Indonesian
+
+## Dataset Structure
+
+### Data Instances
+
+1. `EmoT` dataset
+
+A data point consists of `tweet` and `label`. An example from the train set looks as follows:
+```
+{
+  'tweet': 'Ini adalah hal yang paling membahagiakan saat biasku foto bersama ELF #ReturnOfTheLittlePrince #HappyHeeChulDay'
+  'label': 4,
+}
+```
+
+2. `SmSA` dataset
+
+A data point consists of `text` and `label`. An example from the train set looks as follows:
+```
+{
+  'text': 'warung ini dimiliki oleh pengusaha pabrik tahu yang sudah puluhan tahun terkenal membuat tahu putih di bandung . tahu berkualitas , dipadu keahlian memasak , dipadu kretivitas , jadilah warung yang menyajikan menu utama berbahan tahu , ditambah menu umum lain seperti ayam . semuanya selera indonesia . harga cukup terjangkau . jangan lewatkan tahu bletoka nya , tidak kalah dengan yang asli dari tegal !'
+  'label': 0,
+}
+```
+
+3. `CASA` dataset
+
+A data point consists of `sentence` and multi-label `feature`, `machine`, `others`, `part`, `price`, and `service`. An example from the train set looks as follows:
+```
+{
+  'sentence': 'Saya memakai Honda Jazz GK5 tahun 2014 ( pertama meluncur ) . Mobil nya bagus dan enak sesuai moto nya menyenangkan untuk dikendarai',
+  'fuel': 1,
+  'machine': 1,
+  'others': 2,
+  'part': 1,
+  'price': 1,
+  'service': 1
+}
+```
+
+4. `HoASA` dataset
+
+A data point consists of `sentence` and multi-label `ac`, `air_panas`, `bau`, `general`, `kebersihan`, `linen`, `service`, `sunrise_meal`, `tv`, and `wifi`. An example from the train set looks as follows:
+```
+{
+  'sentence': 'kebersihan kurang...',
+  'ac': 1,
+  'air_panas': 1,
+  'bau': 1,
+  'general': 1,
+  'kebersihan': 0,
+  'linen': 1,
+  'service': 1,
+  'sunrise_meal': 1,
+  'tv': 1,
+  'wifi': 1
+}
+```
+
+5. `WreTE` dataset
+
+A data point consists of `premise`, `hypothesis`, `category`, and `label`. An example from the train set looks as follows:
+```
+{
+  'premise': 'Pada awalnya bangsa Israel hanya terdiri dari satu kelompok keluarga di antara banyak kelompok keluarga yang hidup di tanah Kanan pada abad 18 SM .',
+  'hypothesis': 'Pada awalnya bangsa Yahudi hanya terdiri dari satu kelompok keluarga di antara banyak kelompok keluarga yang hidup di tanah Kanan pada abad 18 SM .'
+  'category': 'menolak perubahan teks terakhir oleh istimewa kontribusi pengguna 141 109 98 87 141 109 98 87 dan mengembalikan revisi 6958053 oleh johnthorne',
+  'label': 0,
+}
+```
+
+6. `POSP` dataset
+
+A data point consists of `tokens` and `pos_tags`. An example from the train set looks as follows:
+```
+{
+  'tokens': ['kepala', 'dinas', 'tata', 'kota', 'manado', 'amos', 'kenda', 'menyatakan', 'tidak', 'tahu', '-', 'menahu', 'soal', 'pencabutan', 'baliho', '.', 'ia', 'enggan', 'berkomentar', 'banyak', 'karena', 'merasa', 'bukan', 'kewenangannya', '.'],
+  'pos_tags': [11, 6, 11, 11, 7, 7, 7, 9, 23, 4, 21, 9, 11, 11, 11, 21, 3, 2, 4, 1, 19, 9, 23, 11, 21]
+}
+```
+
+7. `BaPOS` dataset
+
+A data point consists of `tokens` and `pos_tags`. An example from the train set looks as follows:
+```
+{
+  'tokens': ['Kera', 'untuk', 'amankan', 'pesta', 'olahraga'],
+  'pos_tags': [27, 8, 26, 27, 30]
+}
+```
+
+8. `TermA` dataset
+
+A data point consists of `tokens` and `seq_label`. An example from the train set looks as follows:
+```
+{
+  'tokens': ['kamar', 'saya', 'ada', 'kendala', 'di', 'ac', 'tidak', 'berfungsi', 'optimal', '.', 'dan', 'juga', 'wifi', 'koneksi', 'kurang', 'stabil', '.'],
+  'seq_label': [1, 1, 1, 1, 1, 4, 3, 0, 0, 1, 1, 1, 4, 2, 3, 0, 1]
+}
+```
+
+9. `KEPS` dataset
+
+A data point consists of `tokens` and `seq_label`. An example from the train set looks as follows:
+```
+{
+  'tokens': ['Setelah', 'melalui', 'proses', 'telepon', 'yang', 'panjang', 'tutup', 'sudah', 'kartu', 'kredit', 'bca', 'Ribet'],
+  'seq_label': [0, 1, 1, 2, 0, 0, 1, 0, 1, 2, 2, 1]
+}
+```
+
+10. `NERGrit` dataset
+
+A data point consists of `tokens` and `ner_tags`. An example from the train set looks as follows:
+```
+{
+  'tokens': ['Kontribusinya', 'terhadap', 'industri', 'musik', 'telah', 'mengumpulkan', 'banyak', 'prestasi', 'termasuk', 'lima', 'Grammy', 'Awards', ',', 'serta', 'dua', 'belas', 'nominasi', ';', 'dua', 'Guinness', 'World', 'Records', ';', 'dan', 'penjualannya', 'diperkirakan', 'sekitar', '64', 'juta', 'rekaman', '.'],
+  'ner_tags': [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]}
+```
+
+11. `NERP` dataset
+
+A data point consists of `tokens` and `ner_tags`. An example from the train set looks as follows:
+```
+{
+  'tokens': ['kepala', 'dinas', 'tata', 'kota', 'manado', 'amos', 'kenda', 'menyatakan', 'tidak', 'tahu', '-', 'menahu', 'soal', 'pencabutan', 'baliho', '.', 'ia', 'enggan', 'berkomentar', 'banyak', 'karena', 'merasa', 'bukan', 'kewenangannya', '.'],
+  'ner_tags': [9, 9, 9, 9, 2, 7, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]
+}
+```
+
+12. `FacQA` dataset
+
+A data point consists of `question`, `passage`, and `seq_label`. An example from the train set looks as follows:
+```
+{
+  'passage': ['Lewat', 'telepon', 'ke', 'kantor', 'berita', 'lokal', 'Current', 'News', 'Service', ',', 'Hezb-ul', 'Mujahedeen', ',', 'kelompok', 'militan', 'Kashmir', 'yang', 'terbesar', ',', 'menyatakan', 'bertanggung', 'jawab', 'atas', 'ledakan', 'di', 'Srinagar', '.'],
+  'question': ['Kelompok', 'apakah', 'yang', 'menyatakan', 'bertanggung', 'jawab', 'atas', 'ledakan', 'di', 'Srinagar', '?'],
+  'seq_label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+}
+```
+
+### Data Fields
+
+1. `EmoT` dataset
+
+- `tweet`: a `string` feature.
+- `label`: an emotion label, with possible values including `sadness`, `anger`, `love`, `fear`, `happy`.
+
+2. `SmSA` dataset
+
+- `text`: a `string` feature.
+- `label`: a sentiment label, with possible values including `positive`, `neutral`, `negative`.
+
+3. `CASA` dataset
+
+- `sentence`: a `string` feature.
+- `fuel`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
+- `machine`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
+- `others`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
+- `part`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
+- `price`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
+- `service`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
+
+4. `HoASA` dataset
+
+- `sentence`: a `string` feature.
+- `ac`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
+- `air_panas`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
+- `bau`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
+- `general`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
+- `kebersihan`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
+- `linen`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
+- `service`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
+- `sunrise_meal`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
+- `tv`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
+- `wifi`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
+
+5. `WReTE` dataset
+
+- `premise`: a `string` feature.
+- `hypothesis`: a `string` feature.
+- `category`: a `string` feature.
+- `label`: a classification label, with possible values including `NotEntail`, `Entail_or_Paraphrase`.
+
+6. `POSP` dataset
+
+- `tokens`: a `list` of `string` features.
+- `pos_tags`: a `list` of POS tag labels, with possible values including `B-PPO`, `B-KUA`, `B-ADV`, `B-PRN`, `B-VBI`.
+
+The POS tag labels follow the [Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention](http://inacl.id/inacl/wp-content/uploads/2017/06/INACLPOS-Tagging-Convention-26-Mei.pdf).
+
+7. `BaPOS` dataset
+
+- `tokens`: a `list` of `string` features.
+- `pos_tags`: a `list` of POS tag labels, with possible values including `B-PR`, `B-CD`, `I-PR`, `B-SYM`, `B-JJ`.
+
+The POS tag labels from [Tagset UI](https://bahasa.cs.ui.ac.id/postag/downloads/Tagset.pdf).
+
+8. `TermA` dataset
+
+- `tokens`: a `list` of `string` features.
+- `seq_label`: a `list` of classification labels, with possible values including `I-SENTIMENT`, `O`, `I-ASPECT`, `B-SENTIMENT`, `B-ASPECT`.
+
+9. `KEPS` dataset
+
+- `tokens`: a `list` of `string` features.
+- `seq_label`: a `list` of classification labels, with possible values including `O`, `B`, `I`.
+
+The labels use Inside-Outside-Beginning (IOB) tagging.
+
+10. `NERGrit` dataset
+
+- `tokens`: a `list` of `string` features.
+- `ner_tags`: a `list` of NER tag labels, with possible values including `I-PERSON`, `B-ORGANISATION`, `I-ORGANISATION`, `B-PLACE`, `I-PLACE`.
+
+The labels use Inside-Outside-Beginning (IOB) tagging.
+
+11. `NERP` dataset
+
+- `tokens`: a `list` of `string` features.
+- `ner_tags`: a `list` of NER tag labels, with possible values including `I-PPL`, `B-EVT`, `B-PLC`, `I-IND`, `B-IND`.
+
+12. `FacQA` dataset
+
+- `question`: a `list` of `string` features.
+- `passage`: a `list` of `string` features.
+- `seq_label`: a `list` of classification labels, with possible values including `O`, `B`, `I`.
+
+### Data Splits
+
+The data is split into a training, validation and test set.
+
+|    | dataset | Train | Valid | Test |
+|----|---------|-------|-------|------|
+| 1  | EmoT    | 3521  | 440   | 440  |
+| 2  | SmSA    | 11000 | 1260  | 500  |
+| 3  | CASA    | 810   | 90    | 180  |
+| 4  | HoASA   | 2283  | 285   | 286  |
+| 5  | WReTE   | 300   | 50    | 100  |
+| 6  | POSP    | 6720  | 840   | 840  |
+| 7  | BaPOS   | 8000  | 1000  | 1029 |
+| 8  | TermA   | 3000  | 1000  | 1000 |
+| 9  | KEPS    | 800   | 200   | 247  |
+| 10 | NERGrit | 1672  | 209   | 209  |
+| 11 | NERP    | 6720  | 840   | 840  |
+| 12 | FacQA   | 2495  | 311   | 311  |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+The licensing status of the IndoNLU benchmark datasets is under MIT License.
+
+### Citation Information
+
+IndoNLU citation
+```
+@inproceedings{wilie2020indonlu,
+  title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
+  author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti},
+  booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
+  year={2020}
+}
+```
+
+`EmoT` dataset citation
+```
+@inproceedings{saputri2018emotion,
+  title={Emotion Classification on Indonesian Twitter Dataset},
+  author={Mei Silviana Saputri, Rahmad Mahendra, and Mirna Adriani},
+  booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)},
+  pages={90--95},
+  year={2018},
+  organization={IEEE}
+}
+```
+
+`SmSA` dataset citation
+```
+@inproceedings{purwarianti2019improving,
+  title={Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector},
+  author={Ayu Purwarianti and Ida Ayu Putu Ari Crisdayanti},
+  booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
+  pages={1--5},
+  year={2019},
+  organization={IEEE}
+}
+```
+
+`CASA` dataset citation
+```
+@inproceedings{ilmania2018aspect,
+  title={Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-based Sentiment Analysis},
+  author={Arfinda Ilmania, Abdurrahman, Samuel Cahyawijaya, Ayu Purwarianti},
+  booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)},
+  pages={62--67},
+  year={2018},
+  organization={IEEE}
+}
+```
+
+`HoASA` dataset citation
+```
+@inproceedings{azhar2019multi,
+  title={Multi-label Aspect Categorization with Convolutional Neural Networks and Extreme Gradient Boosting},
+  author={A. N. Azhar, M. L. Khodra, and A. P. Sutiono}
+  booktitle={Proceedings of the 2019 International Conference on Electrical Engineering and Informatics (ICEEI)},
+  pages={35--40},
+  year={2019}
+}
+```
+
+`WReTE` dataset citation
+```
+@inproceedings{setya2018semi,
+  title={Semi-supervised Textual Entailment on Indonesian Wikipedia Data},
+  author={Ken Nabila Setya and Rahmad Mahendra},
+  booktitle={Proceedings of the 2018 International Conference on Computational Linguistics and Intelligent Text Processing (CICLing)},
+  year={2018}
+}
+```
+
+`POSP` dataset citation
+```
+@inproceedings{hoesen2018investigating,
+  title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger},
+  author={Devin Hoesen and Ayu Purwarianti},
+  booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
+  pages={35--38},
+  year={2018},
+  organization={IEEE}
+}
+```
+
+`BaPOS` dataset citation
+```
+@inproceedings{dinakaramani2014designing,
+  title={Designing an Indonesian Part of Speech Tagset and Manually Tagged Indonesian Corpus},
+  author={Arawinda Dinakaramani, Fam Rashel, Andry Luthfi, and Ruli Manurung},
+  booktitle={Proceedings of the 2014 International Conference on Asian Language Processing (IALP)},
+  pages={66--69},
+  year={2014},
+  organization={IEEE}
+}
+@inproceedings{kurniawan2018toward,
+  title={Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging},
+  author={Kemal Kurniawan and Alham Fikri Aji},
+  booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
+  pages={303--307},
+  year={2018},
+  organization={IEEE}
+}
+```
+
+`TermA` dataset citation
+```
+@article{winatmoko2019aspect,
+  title={Aspect and Opinion Term Extraction for Hotel Reviews Using Transfer Learning and Auxiliary Labels},
+  author={Yosef Ardhito Winatmoko, Ali Akbar Septiandri, Arie Pratama Sutiono},
+  journal={arXiv preprint arXiv:1909.11879},
+  year={2019}
+}
+@article{fernando2019aspect,
+  title={Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews},
+  author={Jordhy Fernando, Masayu Leylia Khodra, Ali Akbar Septiandri},
+  journal={arXiv preprint arXiv:1908.04899},
+  year={2019}
+}
+```
+
+`KEPS` dataset citation
+```
+@inproceedings{mahfuzh2019improving,
+  title={Improving Joint Layer RNN based Keyphrase Extraction by Using Syntactical Features},
+  author={Miftahul Mahfuzh, Sidik Soleman, and Ayu Purwarianti},
+  booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
+  pages={1--6},
+  year={2019},
+  organization={IEEE}
+}
+```
+
+`NERGrit` dataset citation
+```
+@online{nergrit2019,
+  title={NERGrit Corpus},
+  author={NERGrit Developers},
+  year={2019},
+  url={https://github.com/grit-id/nergrit-corpus}
+}
+```
+
+`NERP` dataset citation
+```
+@inproceedings{hoesen2018investigating,
+  title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger},
+  author={Devin Hoesen and Ayu Purwarianti},
+  booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
+  pages={35--38},
+  year={2018},
+  organization={IEEE}
+}
+```
+
+`FacQA` dataset citation
+```
+@inproceedings{purwarianti2007machine,
+  title={A Machine Learning Approach for Indonesian Question Answering System},
+  author={Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa},
+  booktitle={Proceedings of Artificial Intelligence and Applications },
+  pages={573--578},
+  year={2007}
+}
+```

dataset_infos.json

@@ -0,0 +1 @@
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indonlu.py

@@ -0,0 +1,644 @@
+# 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.
+"""The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia"""
+
+from __future__ import absolute_import, division, print_function
+
+import ast
+import csv
+import textwrap
+
+import six
+
+import datasets
+
+
+_INDONLU_CITATION = """\
+@inproceedings{wilie2020indonlu,
+title = {{IndoNLU}: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
+authors={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti},
+booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
+year={2020}
+}
+"""
+
+_INDONLU_DESCRIPTION = """\
+The IndoNLU benchmark is a collection of resources for training, evaluating, \
+and analyzing natural language understanding systems for Bahasa Indonesia.
+"""
+
+_INDONLU_HOMEPAGE = "https://www.indobenchmark.com/"
+
+_INDONLU_LICENSE = "https://raw.githubusercontent.com/indobenchmark/indonlu/master/LICENSE"
+
+
+class IndonluConfig(datasets.BuilderConfig):
+    """BuilderConfig for IndoNLU"""
+
+    def __init__(
+        self,
+        text_features,
+        label_column,
+        label_classes,
+        train_url,
+        valid_url,
+        test_url,
+        citation,
+        **kwargs,
+    ):
+        """BuilderConfig for IndoNLU.
+
+        Args:
+          text_features: `dict[string, string]`, map from the name of the feature
+            dict for each text field to the name of the column in the txt/csv/tsv file
+          label_column: `string`, name of the column in the txt/csv/tsv file corresponding
+            to the label
+          label_classes: `list[string]`, the list of classes if the label is categorical
+          train_url: `string`, url to train file from
+          valid_url: `string`, url to valid file from
+          test_url: `string`, url to test file from
+          citation: `string`, citation for the data set
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(IndonluConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.text_features = text_features
+        self.label_column = label_column
+        self.label_classes = label_classes
+        self.train_url = train_url
+        self.valid_url = valid_url
+        self.test_url = test_url
+        self.citation = citation
+
+
+class Indonlu(datasets.GeneratorBasedBuilder):
+    """Indonesian Natural Language Understanding (IndoNLU) benchmark"""
+
+    BUILDER_CONFIGS = [
+        IndonluConfig(
+            name="emot",
+            description=textwrap.dedent(
+                """\
+            An emotion classification dataset collected from the social media
+            platform Twitter (Saputri et al., 2018). The dataset consists of
+            around 4000 Indonesian colloquial language tweets, covering five
+            different emotion labels: sadness, anger, love, fear, and happy."""
+            ),
+            text_features={"tweet": "tweet"},
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=["sadness", "anger", "love", "fear", "happy"],
+            label_column="label",
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/emot_emotion-twitter/train_preprocess.csv",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/emot_emotion-twitter/valid_preprocess.csv",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/emot_emotion-twitter/test_preprocess_masked_label.csv",
+            citation=textwrap.dedent(
+                """\
+            @inproceedings{saputri2018emotion,
+              title={Emotion Classification on Indonesian Twitter Dataset},
+              author={Mei Silviana Saputri, Rahmad Mahendra, and Mirna Adriani},
+              booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)},
+              pages={90--95},
+              year={2018},
+              organization={IEEE}
+            }"""
+            ),
+        ),
+        IndonluConfig(
+            name="smsa",
+            description=textwrap.dedent(
+                """\
+            This sentence-level sentiment analysis dataset (Purwarianti and Crisdayanti, 2019)
+            is a collection of comments and reviews in Indonesian obtained from multiple online
+            platforms. The text was crawled and then annotated by several Indonesian linguists
+            to construct this dataset. There are three possible sentiments on the SmSA
+            dataset: positive, negative, and neutral."""
+            ),
+            text_features={"text": "text"},
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=["positive", "neutral", "negative"],
+            label_column="label",
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/smsa_doc-sentiment-prosa/train_preprocess.tsv",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/smsa_doc-sentiment-prosa/valid_preprocess.tsv",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/smsa_doc-sentiment-prosa/test_preprocess_masked_label.tsv",
+            citation=textwrap.dedent(
+                """\
+            @inproceedings{purwarianti2019improving,
+              title={Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector},
+              author={Ayu Purwarianti and Ida Ayu Putu Ari Crisdayanti},
+              booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
+              pages={1--5},
+              year={2019},
+              organization={IEEE}
+            }"""
+            ),
+        ),
+        IndonluConfig(
+            name="casa",
+            description=textwrap.dedent(
+                """\
+            An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected
+            from multiple Indonesian online automobile platforms (Ilmania et al., 2018). The dataset covers
+            six aspects of car quality. We define the task to be a multi-label classification task, where
+            each label represents a sentiment for a single aspect with three possible values: positive,
+            negative, and neutral."""
+            ),
+            text_features={"sentence": "sentence"},
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=["negative", "neutral", "positive"],
+            label_column=["fuel", "machine", "others", "part", "price", "service"],
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/casa_absa-prosa/train_preprocess.csv",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/casa_absa-prosa/valid_preprocess.csv",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/casa_absa-prosa/test_preprocess_masked_label.csv",
+            citation=textwrap.dedent(
+                """\
+            @inproceedings{ilmania2018aspect,
+              title={Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-based Sentiment Analysis},
+              author={Arfinda Ilmania, Abdurrahman, Samuel Cahyawijaya, Ayu Purwarianti},
+              booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)},
+              pages={62--67},
+              year={2018},
+              organization={IEEE}
+            }"""
+            ),
+        ),
+        IndonluConfig(
+            name="hoasa",
+            description=textwrap.dedent(
+                """\
+            An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel
+            aggregator platform, AiryRooms (Azhar et al., 2019). The dataset covers ten different aspects of
+            hotel quality. Each review is labeled with a single sentiment label for each aspect. There are
+            four possible sentiment classes for each sentiment label: positive, negative, neutral, and
+            positive-negative. The positivenegative label is given to a review that contains multiple sentiments
+            of the same aspect but for different objects (e.g., cleanliness of bed and toilet)."""
+            ),
+            text_features={"sentence": "sentence"},
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=["neg", "neut", "pos", "neg_pos"],
+            label_column=[
+                "ac",
+                "air_panas",
+                "bau",
+                "general",
+                "kebersihan",
+                "linen",
+                "service",
+                "sunrise_meal",
+                "tv",
+                "wifi",
+            ],
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/hoasa_absa-airy/train_preprocess.csv",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/hoasa_absa-airy/valid_preprocess.csv",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/hoasa_absa-airy/test_preprocess_masked_label.csv",
+            citation=textwrap.dedent(
+                """\
+            @inproceedings{azhar2019multi,
+              title={Multi-label Aspect Categorization with Convolutional Neural Networks and Extreme Gradient Boosting},
+              author={A. N. Azhar, M. L. Khodra, and A. P. Sutiono}
+              booktitle={Proceedings of the 2019 International Conference on Electrical Engineering and Informatics (ICEEI)},
+              pages={35--40},
+              year={2019}
+            }"""
+            ),
+        ),
+        IndonluConfig(
+            name="wrete",
+            description=textwrap.dedent(
+                """\
+            The Wiki Revision Edits Textual Entailment dataset (Setya and Mahendra, 2018) consists of 450 sentence pairs
+            constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic
+            relations between the pairs. The data are labeled as entailed when the meaning of the second sentence can be
+            derived from the first one, and not entailed otherwise."""
+            ),
+            text_features={
+                "premise": "premise",
+                "hypothesis": "hypothesis",
+                "category": "category",
+            },
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=["NotEntail", "Entail_or_Paraphrase"],
+            label_column="label",
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/wrete_entailment-ui/train_preprocess.csv",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/wrete_entailment-ui/valid_preprocess.csv",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/wrete_entailment-ui/test_preprocess_masked_label.csv",
+            citation=textwrap.dedent(
+                """\
+            @inproceedings{setya2018semi,
+              title={Semi-supervised Textual Entailment on Indonesian Wikipedia Data},
+              author={Ken Nabila Setya and Rahmad Mahendra},
+              booktitle={Proceedings of the 2018 International Conference on Computational Linguistics and Intelligent Text Processing (CICLing)},
+              year={2018}
+            }"""
+            ),
+        ),
+        IndonluConfig(
+            name="posp",
+            description=textwrap.dedent(
+                """\
+            This Indonesian part-of-speech tagging (POS) dataset (Hoesen and Purwarianti, 2018) is collected from Indonesian
+            news websites. The dataset consists of around 8000 sentences with 26 POS tags. The POS tag labels follow the
+            Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention."""
+            ),
+            text_features={"tokens": "tokens"},
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=[
+                "B-PPO",
+                "B-KUA",
+                "B-ADV",
+                "B-PRN",
+                "B-VBI",
+                "B-PAR",
+                "B-VBP",
+                "B-NNP",
+                "B-UNS",
+                "B-VBT",
+                "B-VBL",
+                "B-NNO",
+                "B-ADJ",
+                "B-PRR",
+                "B-PRK",
+                "B-CCN",
+                "B-$$$",
+                "B-ADK",
+                "B-ART",
+                "B-CSN",
+                "B-NUM",
+                "B-SYM",
+                "B-INT",
+                "B-NEG",
+                "B-PRI",
+                "B-VBE",
+            ],
+            label_column="pos_tags",
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/posp_pos-prosa/train_preprocess.txt",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/posp_pos-prosa/valid_preprocess.txt",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/posp_pos-prosa/test_preprocess_masked_label.txt",
+            citation=textwrap.dedent(
+                """\
+            @inproceedings{hoesen2018investigating,
+              title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger},
+              author={Devin Hoesen and Ayu Purwarianti},
+              booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
+              pages={35--38},
+              year={2018},
+              organization={IEEE}
+            }"""
+            ),
+        ),
+        IndonluConfig(
+            name="bapos",
+            description=textwrap.dedent(
+                """\
+            This POS tagging dataset (Dinakaramani et al., 2014) contains about 1000 sentences, collected from the PAN Localization
+            Project. In this dataset, each word is tagged by one of 23 POS tag classes. Data splitting used in this benchmark follows
+            the experimental setting used by Kurniawan and Aji (2018)"""
+            ),
+            text_features={"tokens": "tokens"},
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=[
+                "B-PR",
+                "B-CD",
+                "I-PR",
+                "B-SYM",
+                "B-JJ",
+                "B-DT",
+                "I-UH",
+                "I-NND",
+                "B-SC",
+                "I-WH",
+                "I-IN",
+                "I-NNP",
+                "I-VB",
+                "B-IN",
+                "B-NND",
+                "I-CD",
+                "I-JJ",
+                "I-X",
+                "B-OD",
+                "B-RP",
+                "B-RB",
+                "B-NNP",
+                "I-RB",
+                "I-Z",
+                "B-CC",
+                "B-NEG",
+                "B-VB",
+                "B-NN",
+                "B-MD",
+                "B-UH",
+                "I-NN",
+                "B-PRP",
+                "I-SC",
+                "B-Z",
+                "I-PRP",
+                "I-OD",
+                "I-SYM",
+                "B-WH",
+                "B-FW",
+                "I-CC",
+                "B-X",
+            ],
+            label_column="pos_tags",
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/bapos_pos-idn/train_preprocess.txt",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/bapos_pos-idn/valid_preprocess.txt",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/bapos_pos-idn/test_preprocess_masked_label.txt",
+            citation=textwrap.dedent(
+                """\
+            @inproceedings{dinakaramani2014designing,
+              title={Designing an Indonesian Part of Speech Tagset and Manually Tagged Indonesian Corpus},
+              author={Arawinda Dinakaramani, Fam Rashel, Andry Luthfi, and Ruli Manurung},
+              booktitle={Proceedings of the 2014 International Conference on Asian Language Processing (IALP)},
+              pages={66--69},
+              year={2014},
+              organization={IEEE}
+            }
+            @inproceedings{kurniawan2019toward,
+              title={Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging},
+              author={Kemal Kurniawan and Alham Fikri Aji},
+              booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
+              pages={303--307},
+              year={2018},
+              organization={IEEE}
+            }"""
+            ),
+        ),
+        IndonluConfig(
+            name="terma",
+            description=textwrap.dedent(
+                """\
+            This span-extraction dataset is collected from the hotel aggregator platform, AiryRooms (Septiandri and Sutiono, 2019;
+            Fernando et al., 2019). The dataset consists of thousands of hotel reviews, which each contain a span label for aspect
+            and sentiment words representing the opinion of the reviewer on the corresponding aspect. The labels use
+            Inside-Outside-Beginning (IOB) tagging representation with two kinds of tags, aspect and sentiment."""
+            ),
+            text_features={"tokens": "tokens"},
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=["I-SENTIMENT", "O", "I-ASPECT", "B-SENTIMENT", "B-ASPECT"],
+            label_column="seq_label",
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/terma_term-extraction-airy/train_preprocess.txt",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/terma_term-extraction-airy/valid_preprocess.txt",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/terma_term-extraction-airy/test_preprocess_masked_label.txt",
+            citation=textwrap.dedent(
+                """\
+            @article{winatmoko2019aspect,
+              title={Aspect and Opinion Term Extraction for Hotel Reviews Using Transfer Learning and Auxiliary Labels},
+              author={Yosef Ardhito Winatmoko, Ali Akbar Septiandri, Arie Pratama Sutiono},
+              journal={arXiv preprint arXiv:1909.11879},
+              year={2019}
+            }
+            @article{fernando2019aspect,
+              title={Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews},
+              author={Jordhy Fernando, Masayu Leylia Khodra, Ali Akbar Septiandri},
+              journal={arXiv preprint arXiv:1908.04899},
+              year={2019}
+            }"""
+            ),
+        ),
+        IndonluConfig(
+            name="keps",
+            description=textwrap.dedent(
+                """\
+            This keyphrase extraction dataset (Mahfuzh et al., 2019) consists of text from Twitter discussing
+            banking products and services and is written in the Indonesian language. A phrase containing
+            important information is considered a keyphrase. Text may contain one or more keyphrases since
+            important phrases can be located at different positions. The dataset follows the IOB chunking format,
+            which represents the position of the keyphrase."""
+            ),
+            text_features={"tokens": "tokens"},
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=["O", "B", "I"],
+            label_column="seq_label",
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/keps_keyword-extraction-prosa/train_preprocess.txt",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/keps_keyword-extraction-prosa/valid_preprocess.txt",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/keps_keyword-extraction-prosa/test_preprocess_masked_label.txt",
+            citation=textwrap.dedent(
+                """\
+            @inproceedings{mahfuzh2019improving,
+              title={Improving Joint Layer RNN based Keyphrase Extraction by Using Syntactical Features},
+              author={Miftahul Mahfuzh, Sidik Soleman, and Ayu Purwarianti},
+              booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
+              pages={1--6},
+              year={2019},
+              organization={IEEE}
+            }"""
+            ),
+        ),
+        IndonluConfig(
+            name="nergrit",
+            description=textwrap.dedent(
+                """\
+            This NER dataset is taken from the Grit-ID repository, and the labels are spans in IOB chunking representation.
+            The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and
+            ORGANIZATION (name of organization)."""
+            ),
+            text_features={"tokens": "tokens"},
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=["I-PERSON", "B-ORGANISATION", "I-ORGANISATION", "B-PLACE", "I-PLACE", "O", "B-PERSON"],
+            label_column="ner_tags",
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nergrit_ner-grit/train_preprocess.txt",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nergrit_ner-grit/valid_preprocess.txt",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nergrit_ner-grit/test_preprocess_masked_label.txt",
+            citation=textwrap.dedent(
+                """\
+            @online{nergrit2019,
+              title={NERGrit Corpus},
+              author={NERGrit Developers},
+              year={2019},
+              url={https://github.com/grit-id/nergrit-corpus}
+            }"""
+            ),
+        ),
+        IndonluConfig(
+            name="nerp",
+            description=textwrap.dedent(
+                """\
+            This NER dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites.
+            There are five labels available in this dataset, PER (name of person), LOC (name of location), IND (name of product or brand),
+            EVT (name of the event), and FNB (name of food and beverage). The NERP dataset uses the IOB chunking format."""
+            ),
+            text_features={"tokens": "tokens"},
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=[
+                "I-PPL",
+                "B-EVT",
+                "B-PLC",
+                "I-IND",
+                "B-IND",
+                "B-FNB",
+                "I-EVT",
+                "B-PPL",
+                "I-PLC",
+                "O",
+                "I-FNB",
+            ],
+            label_column="ner_tags",
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nerp_ner-prosa/train_preprocess.txt",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nerp_ner-prosa/valid_preprocess.txt",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/nerp_ner-prosa/test_preprocess_masked_label.txt",
+            citation=textwrap.dedent(
+                """\
+            @inproceedings{hoesen2018investigating,
+              title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger},
+              author={Devin Hoesen and Ayu Purwarianti},
+              booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
+              pages={35--38},
+              year={2018},
+              organization={IEEE}
+            }"""
+            ),
+        ),
+        IndonluConfig(
+            name="facqa",
+            description=textwrap.dedent(
+                """\
+            The goal of the FacQA dataset is to find the answer to a question from a provided short passage from
+            a news article (Purwarianti et al., 2007). Each row in the FacQA dataset consists of a question,
+            a short passage, and a label phrase, which can be found inside the corresponding short passage.
+            There are six categories of questions: date, location, name, organization, person, and quantitative."""
+            ),
+            text_features={"question": "question", "passage": "passage"},
+            # label classes sorted refer to https://github.com/indobenchmark/indonlu/blob/master/utils/data_utils.py
+            label_classes=["O", "B", "I"],
+            label_column="seq_label",
+            train_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/facqa_qa-factoid-itb/train_preprocess.csv",
+            valid_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/facqa_qa-factoid-itb/valid_preprocess.csv",
+            test_url="https://raw.githubusercontent.com/indobenchmark/indonlu/master/dataset/facqa_qa-factoid-itb/test_preprocess_masked_label.csv",
+            citation=textwrap.dedent(
+                """\
+            @inproceedings{purwarianti2007machine,
+              title={A Machine Learning Approach for Indonesian Question Answering System},
+              author={Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa},
+              booktitle={Proceedings of Artificial Intelligence and Applications },
+              pages={573--578},
+              year={2007}
+            }"""
+            ),
+        ),
+    ]
+
+    def _info(self):
+        sentence_features = ["terma", "keps", "facqa"]
+        ner_ = ["nergrit", "nerp"]
+        pos_ = ["posp", "bapos"]
+
+        if self.config.name in (sentence_features + ner_ + pos_):
+            features = {
+                text_feature: datasets.Sequence(datasets.Value("string"))
+                for text_feature in six.iterkeys(self.config.text_features)
+            }
+        else:
+            features = {
+                text_feature: datasets.Value("string") for text_feature in six.iterkeys(self.config.text_features)
+            }
+
+        if self.config.label_classes:
+            if self.config.name in sentence_features:
+                features["seq_label"] = datasets.Sequence(
+                    datasets.features.ClassLabel(names=self.config.label_classes)
+                )
+            elif self.config.name in ner_:
+                features["ner_tags"] = datasets.Sequence(datasets.features.ClassLabel(names=self.config.label_classes))
+            elif self.config.name in pos_:
+                features["pos_tags"] = datasets.Sequence(datasets.features.ClassLabel(names=self.config.label_classes))
+            elif self.config.name == "casa" or self.config.name == "hoasa":
+                for label in self.config.label_column:
+                    features[label] = datasets.features.ClassLabel(names=self.config.label_classes)
+            else:
+                features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
+
+        return datasets.DatasetInfo(
+            description=self.config.description,
+            features=datasets.Features(features),
+            homepage=_INDONLU_HOMEPAGE,
+            citation=self.config.citation + "\n" + _INDONLU_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        train_path = dl_manager.download_and_extract(self.config.train_url)
+        valid_path = dl_manager.download_and_extract(self.config.valid_url)
+        test_path = dl_manager.download_and_extract(self.config.test_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, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """ Yields examples. """
+        csv_file = ["emot", "wrete", "facqa", "casa", "hoasa"]
+        tsv_file = ["smsa"]
+        txt_file = ["terma", "keps"]
+        txt_file_pos = ["posp", "bapos"]
+        txt_file_ner = ["nergrit", "nerp"]
+
+        with open(filepath, encoding="utf-8") as f:
+
+            if self.config.name in csv_file:
+                reader = csv.reader(f, delimiter=",", quotechar='"', quoting=csv.QUOTE_ALL)
+                next(reader)  # skip first row which is header
+
+                for id_, row in enumerate(reader):
+                    if self.config.name == "emot":
+                        label, tweet = row
+                        yield id_, {"tweet": tweet, "label": label}
+                    elif self.config.name == "wrete":
+                        premise, hypothesis, category, label = row
+                        yield id_, {"premise": premise, "hypothesis": hypothesis, "category": category, "label": label}
+                    elif self.config.name == "facqa":
+                        question, passage, seq_label = row
+                        yield id_, {
+                            "question": ast.literal_eval(question),
+                            "passage": ast.literal_eval(passage),
+                            "seq_label": ast.literal_eval(seq_label),
+                        }
+                    elif self.config.name == "casa" or self.config.name == "hoasa":
+                        sentence, *labels = row
+                        sentence = {"sentence": sentence}
+                        label = {l: labels[idx] for idx, l in enumerate(self.config.label_column)}
+                        yield id_, {**sentence, **label}
+            elif self.config.name in tsv_file:
+                reader = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+
+                for id_, row in enumerate(reader):
+                    if self.config.name == "smsa":
+                        text, label = row
+                        yield id_, {"text": text, "label": label}
+            elif self.config.name in (txt_file + txt_file_pos + txt_file_ner):
+                id_ = 0
+                tokens = []
+                seq_label = []
+                for line in f:
+                    if len(line.strip()) > 0:
+                        token, label = line[:-1].split("\t")
+                        tokens.append(token)
+                        seq_label.append(label)
+                    else:
+                        if self.config.name in txt_file:
+                            yield id_, {"tokens": tokens, "seq_label": seq_label}
+                        elif self.config.name in txt_file_pos:
+                            yield id_, {"tokens": tokens, "pos_tags": seq_label}
+                        elif self.config.name in txt_file_ner:
+                            yield id_, {"tokens": tokens, "ner_tags": seq_label}
+                        id_ += 1
+                        tokens = []
+                        seq_label = []
+                # add last example
+                if tokens:
+                    if self.config.name in txt_file:
+                        yield id_, {"tokens": tokens, "seq_label": seq_label}
+                    elif self.config.name in txt_file_pos:
+                        yield id_, {"tokens": tokens, "pos_tags": seq_label}
+                    elif self.config.name in txt_file_ner:
+                        yield id_, {"tokens": tokens, "ner_tags": seq_label}