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\FIX: readme #9

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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -9,7 +9,7 @@ State-of-the-Art Language Modeling and Text Classification in Malayalam Language


## Results
We trained a Malayalam language model on the Wikipedia article dump from Oct, 2018. The Wikipedia dump had 55k+ articles. The difficuly in training a Malayalam language model is *text tokenization*, since [Malayalam is a highly inflectional and agglutinative language.](https://thottingal.in/blog/2017/11/26/towards-a-malayalam-morphology-analyser/) In the current model, we are using `nltk tokenizer` (will try better alternative in the future) and the vocab size is 30k. The language model was used to train a classifier which classifies a news into 5 categories (India, Kerala, Sports, Business, Entertainment). Our classifier came out with a whooping 92% accuracy in the classification task.
We trained a Malayalam language model on the Wikipedia article dump from Oct, 2018. The Wikipedia dump had 55k+ articles. The difficulty in training a Malayalam language model is *text tokenization*, since [Malayalam is a highly inflectional and agglutinative language.](https://thottingal.in/blog/2017/11/26/towards-a-malayalam-morphology-analyser/) In the current model, we are using `nltk tokenizer` (will try better alternative in the future) and the vocab size is 30k. The language model was used to train a classifier which classifies a news into 5 categories (India, Kerala, Sports, Business, Entertainment). Our classifier came out with a whooping 92% accuracy in the classification task.


## Releases
Expand Down Expand Up @@ -58,7 +58,7 @@ eg: `python lm/tok2id.py /home/adamshamsudeen/mal/Vaaku2Vec/wiki/ml/`

### Testing the classifier:

1. To test the classifier trained on Manorama news, download the `Pretrained Malyalam Text Classifier ` mentioned in the downloads.
1. To test the classifier trained on Manorama news, download the `Pretrained Malayalam Text Classifier ` mentioned in the downloads.
2. Use `prediction.ipynb` and test out your input.

We manually tested the model on news from other leading news paper and the model performed pretty well.
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