<commit_before># Convolutional Neural Networks for Text Classification

Convolutional Neural Networks for text classification built on top of Gensim's well known word2vec implementation and Keras.

Dependencies:
- Numpy
- Gensim
- Keras
- H5Py (for saving the model)

Usage:

```python
X=['hello world','Μου αρέσουν οι ντολμάδες','الجو دافئ اليوم','池塘水很冷']
y=[1,2,3,4]

cnn = Text_CNN()
cnn.fit(X,y)           # Train the model.

cnn.predict(X)         # Predict most likely class.
cnn.predict_proba(X)   # Per class probabilities.

cnn.save()             # Save the model.
cnn.load()             # Load previously saved model.
```<commit_msg>improved documentation<commit_after># Convolutional Neural Networks for Text Classification

Convolutional Neural Networks for text classification built on top of Gensim's well known word2vec implementation and Keras.

Dependencies:
- Numpy
- Gensim
- Keras
- H5Py (for saving the model)

Usage:

```python
from text_cnn import Text_CNN

X=['hello world','Μου αρέσουν οι ντολμάδες','الجو دافئ اليوم','池塘水很冷']
y=[1,2,3,4]

cnn = Text_CNN()
cnn.fit(X,y)           # Train the model.

cnn.predict(X)         # Predict most likely class.
cnn.predict_proba(X)   # Per class probabilities.

cnn.save()             # Save the model.
cnn.load()             # Load previously saved model.
```
{'fraction_non_alphanumeric': 0.10407239819004525, 'fraction_numerical': 0.00904977375565611, 'mean_word_length': 3.52901023890785, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1808996', 'n_tokens_mistral': 504, 'n_tokens_neox': 463, 'n_words': 139}