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indolem_sentiment
IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.
This dataset is based on binary classification (positive and negative), with distribution:
Train: 3638 sentences
Development: 399 sentences
Test: 1011 sentences
The data is sourced from 1) Twitter (Koto and Rahmaningtyas, 2017)
and 2) hotel reviews.
The experiment is based on 5-fold cross validation.
Dataset Usage
Run pip install nusacrowd before loading the dataset through HuggingFace's load_dataset.
Citation
@article{DBLP:journals/corr/abs-2011-00677,
author = {Fajri Koto and
Afshin Rahimi and
Jey Han Lau and
Timothy Baldwin},
title = {IndoLEM and IndoBERT: {A} Benchmark Dataset and Pre-trained Language
Model for Indonesian {NLP}},
journal = {CoRR},
volume = {abs/2011.00677},
year = {2020},
url = {https://arxiv.org/abs/2011.00677},
eprinttype = {arXiv},
eprint = {2011.00677},
timestamp = {Fri, 06 Nov 2020 15:32:47 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2011-00677.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
License
Creative Commons Attribution Share-Alike 4.0 International
Homepage
NusaCatalogue
For easy indexing and metadata: https://indonlp.github.io/nusa-catalogue
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