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README.md
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- word-analogy
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---
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We introduced KaWAT (Kata Word
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We evaluated on it several existing pretrained Indonesian word embeddings and embeddings trained on Indonesian online news corpus.
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We also tested them on two downstream tasks and found that pretrained word embeddings helped either by reducing the training epochs
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or yielding significant performance gains.
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## Supported Tasks
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Word Analogy
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## Dataset Usage
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### Using `datasets` library
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```
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```
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### Using `seacrowd` library
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```import seacrowd as sc
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# Load the dataset using the default config
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# Check all available subsets (config names) of the dataset
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# Load the dataset using a specific config
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```
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## Dataset Homepage
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- word-analogy
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---
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We introduced KaWAT (Kata Word AnalogyTask), a new word analogy task dataset for Indonesian.
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We evaluated on it several existing pretrained Indonesian word embeddings and embeddings trained on Indonesian online news corpus.
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We also tested them on two downstream tasks and found that pretrained word embeddings helped either by reducing the training epochs
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or yielding significant performance gains.
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## Supported Tasks
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Word Analogy
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## Dataset Usage
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### Using `datasets` library
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```
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from datasets import load_dataset
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dset = datasets.load_dataset("SEACrowd/kawat", trust_remote_code=True)
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```
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### Using `seacrowd` library
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```import seacrowd as sc
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# Load the dataset using the default config
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dset = sc.load_dataset("kawat", schema="seacrowd")
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# Check all available subsets (config names) of the dataset
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print(sc.available_config_names("kawat"))
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# Load the dataset using a specific config
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dset = sc.load_dataset_by_config_name(config_name="<config_name>")
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```
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More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).
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## Dataset Homepage
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