Metadata-Version: 2.4
Name: wordsim
Version: 0.1.2
Summary: This is simply a word similarity model that matches the desired word to the most similar word in a reference list of words provided by the user.
Home-page: https://github.com/Khaled-Terzaki/wordsim.git
Author: Khaled Terzaki
Author-email: khaled.m.terzaki@gmail.com
License: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Text Processing :: Linguistic
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: license-file
Dynamic: requires-python
Dynamic: summary

# wordsim
This is the source code of a smart model that finds the similarity between a given word and a reference list of words. Then it returns the word that has the most similarity.

## Code

```python
from wordsim import wordSim  # import the model.

lst = ['Red', 'Green', 'Blue']  # declare a list of reference words.

model = wordSim()  # create an instance of the class 'wordSim'.
model.train(lst)  # train the model.

test_word = 'gren'  # specify the required word to test.

results = model.predict(word=test_word, tunner=2)  # pass the 'test word' as an argument and tune the 'tunner' for best performance.

print(results)  # print the results.
print(model.get_best())  # or print the results that way.

#console:
{'word': 'gren', 'word_match': 'Green', 'status': 'successful', 'score': 0.3333333333333333}
{'word': 'gren', 'word_match': 'Green', 'status': 'successful', 'score': 0.3333333333333333}
