indonlu
The first-ever vast natural language processing benchmark for Indonesian Language. We provide multiple downstream tasks, pre-trained IndoBERT models, and a starter code! (AACL-IJCNLP 2020)
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Download Latest Version (.zip)- ISSUE_TEMPLATE.md
- PULL_REQUEST_TEMPLATE.md
- aspect_based_sentiment_analysis.ipynb
- aspect_extraction_data_util.ipynb
- ner_data_utils.ipynb
- postag_data_utils.ipynb
- test_preprocess.txt
- test_preprocess_masked_label.txt
- train_preprocess.txt
- valid_preprocess.txt
- vocab.txt
- vocab_uncased.txt
- test_preprocess.csv
- test_preprocess_masked_label.csv
- train_preprocess.csv
- valid_preprocess.csv
- vocab.txt
- vocab_uncased.txt
- test_preprocess.csv
- test_preprocess_masked_label.csv
- train_preprocess.csv
- valid_preprocess.csv
- vocab.txt
- vocab_uncased.txt
- test_preprocess.csv
- test_preprocess_masked_label.csv
- train_preprocess.csv
- valid_preprocess.csv
- vocab.txt
- vocab_uncased.txt
- test_preprocess.csv
- test_preprocess_masked_label.csv
- train_preprocess.csv
- valid_preprocess.csv
- vocab.txt
- vocab_uncased.txt
- test_preprocess.txt
- test_preprocess_masked_label.txt
- train_preprocess.txt
- valid_preprocess.txt
- vocab.txt
- vocab_uncased.txt
- test_preprocess.txt
- test_preprocess_masked_label.txt
- train_preprocess.txt
- valid_preprocess.txt
- vocab.txt
- vocab_uncased.txt
- test_preprocess.txt
- test_preprocess_masked_label.txt
- train_preprocess.txt
- valid_preprocess.txt
- vocab.txt
- vocab_uncased.txt
- test_preprocess.txt
- test_preprocess_masked_label.txt
- train_preprocess.txt
- valid_preprocess.txt
- vocab.txt
- vocab_uncased.txt
- test_preprocess.tsv
- test_preprocess_masked_label.tsv
- train_preprocess.tsv
- valid_preprocess.tsv
- vocab.txt
- vocab_uncased.txt
- test_preprocess.txt
- test_preprocess_masked_label.txt
- train_preprocess.txt
- valid_preprocess.txt
- vocab.txt
- vocab_uncased.txt
- test_preprocess.csv
- test_preprocess_masked_label.csv
- train_preprocess.csv
- valid_preprocess.csv
- vocab.txt
- vocab_uncased.txt
- finetune_casa.ipynb
- finetune_emot.ipynb
- finetune_fasttext_casa.ipynb
- finetune_ner_grit.ipynb
- finetune_smsa.ipynb
- finetune_wrete.ipynb
- requirements.txt
- multi_label_classification.py
- word_classification.py
- absa-airy_test.csv
- absa-prosa_test.csv
- doc-sentiment_test.csv
- emotion-twitter_test.csv
- entailment-ui_test.csv
- keyword-extraction-prosa_test.csv
- ner-grit_test.csv
- ner-prosa_test.csv
- pos-idn_test.csv
- pos-prosa_test.csv
- qa-factoid-itb_test.csv
- term-extraction-airy_test.csv
- backprop.png
- bag_of_word.png
- bert_pretraining.png
- chain_rule.png
- cnn.png
- fasttext.png
- finetune_smsa_tutorial.ipynb
- gradient_descent.png
- indonlu_result.png
- logistic_regression.png
- mlp.png
- rnn.png
- tf_idf.png
- transformer.png
- why_transformer.png
- word_vector.png
- args_helper.py
- conlleval.py
- data_utils.py
- forward_fn.py
- functions.py
- metrics.py
- .gitignore
- CODE_OF_CONDUCT.md
- CONTRIBUTING.md
- LICENSE
- main.py
- predict.py
- predict.sh
- README.id.md
- README.md
- requirements.txt
- run_all_tasks.sh
- run_single_task.sh
π Installation Guide
1. Get the code
git clone https://github.com/IndoNLP/indonlu
Downloads the entire project code from GitHub to your computer.
cd indonlu
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
pip install -r examples/requirements.txt
Installs the Python libraries listed in requirements.txt (or similar).
python main.py
Runs the Python script (or module).
If it runs without errors and prints output in the terminal, it worked.
// repository documentation
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