A simple, efficient short-text classification tool based on LibLinear
Embed with jieba as default tokenizer to support Chinese tokenize
Other languages: 更详细的中文文档
- Train set: 48k news titles with 32 labels
- Test set: 16k news titles with 32 labels
- Compare with svm and naive-bayes of scikit-learn
| Classifier | Accuracy | Time cost(s) |
|---|---|---|
| scikit-learn(nb) | 76.8% | 134 |
| scikit-learn(svm) | 76.9% | 121 |
| TextGrocery | 79.6% | 49 |
>>> from tgrocery import Grocery # Create a grocery(don't forget to set a name) >>> grocery = Grocery('sample') # Train from list >>> train_src = [ ('education', 'Student debt to cost Britain billions within decades'), ('education', 'Chinese education for TV experiment'), ('sports', 'Middle East and Asia boost investment in top level sports'), ('sports', 'Summit Series look launches HBO Canada sports doc series: Mudhar') ] >>> grocery.train(train_src) # Or train from file >>> grocery.train('train_ch.txt') # Save model >>> grocery.save() # Load model(the same name as previous) >>> new_grocery = Grocery('sample') >>> new_grocery.load() # Predict >>> new_grocery.predict('Abbott government spends 8ドル million on higher education media blitz') education # Test from list >>> test_src = [ ('education', 'Abbott government spends 8ドル million on higher education media blitz'), ('sports', 'Middle East and Asia boost investment in top level sports'), ] >>> new_grocery.test(test_src) # Return Accuracy 1.0 # Or test from file >>> new_grocery.test('test_ch.txt') # Custom tokenize >>> custom_grocery = Grocery('custom', custom_tokenize=list)
More examples: sample/
$ pip install tgrocery
Only test under Unix-based System