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https://github.com/2shou/TextGrocery
A simple short-text classification tool based on LibLinear
https://github.com/2shou/TextGrocery
Last synced: 2 months ago
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A simple short-text classification tool based on LibLinear
- Host: GitHub
- URL: https://github.com/2shou/TextGrocery
- Owner: 2shou
- License: gpl-3.0
- Created: 2015-01-01T03:13:50.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2021-06-09T06:36:31.000Z (almost 3 years ago)
- Last Synced: 2024-03-07T01:02:30.209Z (3 months ago)
- Language: C++
- Homepage:
- Size: 460 KB
- Stars: 681
- Watchers: 50
- Forks: 208
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Lists
- awesome-python-cn - TextGrocery
- awesome-pysci - TextGrocery - A simple, efficient short-text classification tool based on LibLinear and Jieba. (自然言語解析 / ベイズ最適化)
- awesome-stars - 2shou/TextGrocery - A simple short-text classification tool based on LibLinear (C++)
- awesome-python-cn - 官网
- awesome-stars - 2shou/TextGrocery - `★681` A simple short-text classification tool based on LibLinear (C++)
- awesome-python-cn - TextGrocery
- awesome-python-cn - 官网
- awesome-python - TextGrocery - A simple, efficient short-text classification tool based on LibLinear and Jieba. (Natural Language Processing)
- fucking_awesome_python - :octocat: TextGrocery - :star: 284 :fork_and_knife: 89 - A simple, efficient short-text classification tool based on LibLinear and Jieba. (Natural Language Processing)
- awesome-python - TextGrocery - A simple, efficient short-text classification tool based on LibLinear and Jieba. (Natural Language Processing)
README
TextGrocery
===========
[](https://travis-ci.org/2shou/TextGrocery)
A simple, efficient short-text classification tool based on LibLinear
Embed with [jieba](https://github.com/fxsjy/jieba) as default tokenizer to support Chinese tokenize
Other languages: [更详细的中文文档](http://textgrocery.readthedocs.org/zh/latest/index.html)
Performance
-----------
- 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](https://github.com/scikit-learn/scikit-learn)
| Classifier | Accuracy | Time cost(s) |
|:------------------------:|:---------:|:--------------:|
| scikit-learn(nb) | 76.8% | 134 |
| scikit-learn(svm) | 76.9% | 121 |
| **TextGrocery** | **79.6%** | **49** |
Sample Code
-----------
```python
>>> 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
# Format: Label\tText
>>> 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 million on higher education media blitz')
education
# Test from list
>>> test_src = [
('education', 'Abbott government spends $8 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/](sample/)
Install
-------
$ pip install tgrocery
> Only test under Unix-based System