Some learning notes about ML.
Binary Classification - IMDb movie review dataset
| Model | accuracy | loss | f1 | Code |
|---|---|---|---|---|
| RFC+TFIDF | 0.8406 | N/A | 0.8406 | rfc_tfidf_binary_sklearn.ipynb |
| MLP | 0.8734 (+/- 0.0028) | 0.2989 (+/- 0.0053) | 0.8734 (+/- 0.0059) | mlp_binary_tf2.ipynb |
| MLP+TFIDF | 0.8775 (+/- 0.0018) | 0.2876 (+/- 0.0028) | 0.8782 (+/- 0.0025) | mlp_tfidf_binary_tf2.ipynb |
| CNN | 0.8737 (+/- 0.0045) | 0.2997 (+/- 0.0107) | 0.8745 (+/- 0.0039) | cnn_binary_tf2.ipynb |
| LSTM | 0.8754 (+/- 0.0035) | 0.3043 (+/- 0.0125) | 0.8753 (+/- 0.0047) | lstm_binary_tf2.ipynb |
| CNN+LSTM | 0.8824 (+/- 0.0027) | 0.2863 (+/- 0.0051) | 0.8832 (+/- 0.0032) | cnn_lstm_binary_tf2.ipynb |
| BiLSTM | 0.8785 (+/- 0.0034) | 0.2892 (+/- 0.0091) | 0.8778 (+/- 0.0045) | bilstm_binary_tf2.ipynb |
| BiLSTM+Attention | 0.8798 (+/- 0.0039) | 0.2892 (+/- 0.0105) | 0.8795 (+/- 0.0044) | bilstm_attention_binary_tf2.ipynb |
| FastText | 0.8866 (+/- 0.0009) | 0.2842 (+/- 0.0008) | 0.8867 (+/- 0.0012) | fasttext_binary_tf2.ipynb |
| bert_en_uncased_L-4_H-512_A-8 | 0.8438 (+/- 0.0051) | 0.3598 (+/- 0.0043) | 0.8379 (+/- 0.0090) | bert_binary_tf2.ipynb |
Multiclass Classification - US Consumer Finance Complaints
| Model | accuracy | loss | f1 | Code |
|---|---|---|---|---|
| CNN | 0.8317 (+/- 0.0042) | 0.5488 (+/- 0.0097) | 0.8252 (+/- 0.0062) | cnn_multiclass_tf2.ipynb |
| bert-base-uncased | 0.8632 | 0.4689 | 0.8611 | bert_multiclass_tf2.ipynb |
| albert-base-v1 | 0.8572 | 0.5220 | 0.8543 | albert_multiclass_simpletransformers.ipynb |
Named Entity Recognition - GMB(Groningen Meaning Bank) corpus
| Model | accuracy | loss | f1 | Code |
|---|---|---|---|---|
| BiLSTM | 0.9968 | 0.0168 | 0.7865 | bilstm_ner_tf2.ipynb |
| bert-base-cased - pytorch | 0.9626 | 0.1276 | 0.8263 | bert_ner_pytorch.ipynb |
| bert-base-uncased - simpletransformers | 0.9692 | 0.0994 | 0.8287 | bert_ner_simpletransformers.ipynb |
- Python 3.6
- TensorFlow 2
- Google Colaboratory