Text-Classification-Benchmark

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Text-Classification-Benchmark

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# text_classification
This repo contains code for training Machine Learning models for text classification.

## Overview
There are two types of ML algorithms.

- traditional - logistic regression, support vector machines, mutinomial naive bayes with tf-idf features. (Note: mutinomail naive bayes uses tf features because of the independent assumption.)

- neural - Recurrent Neural Networks and Convolutional Neural Networks. (Implemented in pytorch)

## Requirement
* python 3

* pytorch (for neural models)

* check requirements.txt for more
 
```
conda install pytorch torchvision -c soumith
```

For different environments, please check [pytorch](http://pytorch.org/).

## Downloading google's word2vec

```
sh get_word2vec.sh
gunzip GoogleNews-vectors-negative300.bin.gz
```

## Datasets
* AG's News Topic Classification Dataset

## Current Results
|   accuracy    |  RNN  | CNN  | LR  | SVM  | MNB  | NBSVM |
| ------------- | ----- | ---- | --- | ---- | ---- | ---   | 
|   AG's News   |  87.1% | 88.1%  | 92.3%| 92.6% | 91.7% | 91.1% |

## Reference
Recurrent Neural Network (RNN): [Character-level Convolutional Networks for Text Classification](https://arxiv.org/pdf/1509.01626.pdf)

Convolutional Neural Network (CNN): [Convolutional Neural Networks for Sentence Classification, Kim 2014](https://arxiv.org/pdf/1408.5882.pdf)

Naive Bayse SVM (NBSVM) : [Baselines and Bigrams: Simple, Good Sentiment and Topic Classification, Wang et al. 2012](https://nlp.stanford.edu/pubs/sidaw12_simple_sentiment.pdf)

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