huseinzol05 / bahasa-nlp-tensorflow Goto Github PK

Bahasa-NLP-Tensorflow, Gathers Tensorflow deep learning models for Bahasa Malaysia NLP problems, code simplify inside Jupyter Notebooks 100% including dataset.

• Augmentation
• Sparse classification
• Long-text classification
• Dependency Parsing
• English-Malay Translation
• Entity Tagging
• Abstractive Summarization
• Extractive Summarization
• POS Tagging
• Optical Character Recognition
• Question-Answer
• Semantic Similarity
• Speech to Text
• Stemming
• Topic Generator
• Text to Speech
• Topic Modeling
• Word Vector

1. word2vec Malaya

Trained on Tatoeba dataset.

1. Fast-text Ngrams, test accuracy 88%

Trained on Bahasa subjectivity dataset.

1. RNN LSTM + Bahdanau Attention, test accuracy 84%
2. RNN LSTM + Luong Attention, test accuracy 82%
3. Transfer-learning Multilanguage BERT, test accuracy 94.88%

70+ more models can get from here.

Trained on Bahasa fakenews dataset.

1. Dilated CNN, test accuracy 74%
2. Wavenet, test accuracy 68%
3. BERT Multilanguage, test accuracy 85%
4. BERT-Bahasa Base, test accuracy 88%

Trained on Bahasa dependency parsing dataset. 80% to train, 20% to test.

Accuracy based on arc, types and root accuracies after 10 epochs only.

1. Bidirectional RNN + CRF + Biaffine, arc accuracy 60.64%, types accuracy 58.68%, root accuracy 89.03%
2. Bidirectional RNN + Bahdanau + CRF + Biaffine, arc accuracy 60.51%, types accuracy 59.01%, root accuracy 88.99%
3. Bidirectional RNN + Luong + CRF + Biaffine, arc accuracy 60.60%, types accuracy 59.06%, root accuracy 89.76%
4. BERT Base + CRF + Biaffine, arc accuracy 58.55%, types accuracy 58.12%, root accuracy 88.87%
5. Bidirectional RNN + Biaffine Attention + Cross Entropy, arc accuracy 69.53%, types accuracy 65.38%, root accuracy 90.71%
6. BERT Base + Biaffine Attention + Cross Entropy, arc accuracy 77.03%, types accuracy 66.73%, root accuracy 88.38%
7. XLNET Base + Biaffine Attention + Cross Entropy, arc accuracy 93.50%, types accuracy 92.48%, root accuracy 94.46%

Trained on 100k english-malay dataset.

1. Attention is All you need, train accuracy 19.09% test accuracy 20.38%
2. BiRNN Seq2Seq Luong Attention, Beam decoder, train accuracy 45.2% test accuracy 37.26%
3. Convolution Encoder Decoder, train accuracy 35.89% test accuracy 30.65%
4. Dilated Convolution Encoder Decoder, train accuracy 82.3% test accuracy 56.72%
5. Dilated Convolution Encoder Decoder Self-Attention, train accuracy 60.76% test accuracy 36.59%

Trained on Bahasa entity dataset.

1. Bidirectional LSTM + CRF, test accuracy 95.10%
2. Bidirectional LSTM + CRF + Bahdanau, test accuracy 94.34%
3. Bidirectional LSTM + CRF + Luong, test accuracy 94.84%
4. BERT Multilanguage, test accuracy 96.43%
5. BERT-Bahasa Base, test accuracy 98.11%
6. BERT-Bahasa Small, test accuracy 98.47%
7. XLNET-Bahasa Base, test accuracy 98.008%

Trained on Bahasa entity dataset.

1. Bidirectional LSTM + CRF
2. Bidirectional LSTM + CRF + Bahdanau
3. Bidirectional LSTM + CRF + Luong
4. Bert-Bahasa-Base + CRF, test accuracy 95.17%
5. XLNET-Bahasa-Base + CRF, test accuracy 95.58%

Trained on Malaysia news dataset.

Accuracy based on ROUGE-2 after 20 epochs only.

1. Dilated Seq2Seq, test accuracy 23.926%
2. Pointer Generator + Bahdanau Attention, test accuracy 15.839%
3. Pointer Generator + Luong Attention, test accuracy 26.23%
4. Dilated Seq2Seq + Pointer Generator, test accuracy 20.382%
5. BERT Multilanguage + Dilated CNN Seq2seq + Pointer Generator, test accuracy 23.7134%

Trained on Malaysia news dataset.

1. Skip-thought
2. Residual Network + Bahdanau Attention

Trained on OCR Jawi to Malay

1. CNN + LSTM RNN, test accuracy 63.86%
2. Im2Latex, test accuracy 89.38%

Trained on Bahasa QA dataset.

1. End-to-End + GRU, test accuracy 89.17%
2. Dynamic Memory + GRU, test accuracy 98.86%

Trained on Translated Duplicated Quora question dataset.

1. LSTM Bahdanau + Contrastive loss, test accuracy 79%
2. Dilated CNN + Contrastive loss, test accuracy 77%
3. Self-Attention + Contrastive loss, test accuracy 77%
4. BERT + Cross entropy, test accuracy 83%

Trained on Kamus speech dataset.

1. BiRNN + LSTM + CTC Greedy, test accuracy 72.03%
2. Wavenet, test accuracy 10.21%
3. Deep speech 2, test accuracy 56.51%
4. Dilated-CNN, test accuracy 59.31%
5. Im2Latex, test accuracy 58.59%

1. Tacotron
2. Seq2Seq + Bahdanau Attention
3. Deep CNN + Monothonic Attention + Dilated CNN vocoder

Trained on stemming dataset.

1. Seq2seq + Beam decoder
2. Seq2seq + Bahdanau Attention + Beam decoder
3. Seq2seq + Luong Attention + Beam decoder

Trained on Malaysia news dataset.

1. TAT-LSTM, test accuracy 32.89%
2. TAV-LSTM, test accuracy 40.69%
3. MTA-LSTM, test accuracy 32.96%

1. Lda2Vec

1. word2vec
2. ELMO
3. Fast-text