# idiom
Access and operations with word2vec data
To install: ```pip install idiom```
## Overview
The `idiom` package provides access to word vector data and useful functions to manipulate and analyze it. It includes functionalities for finding the closest words to a given word, calculating word frequencies, and working with various word vector models.
## Features
- **Closest Words**: Find the closest words to a given word based on cosine similarity.
- **Word Frequencies**: Access and manipulate word frequency data.
- **Word Vector Models**: Work with pre-trained word vector models such as FastText.
- **IDF Calculations**: Compute different types of Inverse Document Frequency (IDF) values.
## Usage
### Finding Closest Words
You can find the closest words to a given word using the `closest_words` function:
```python
from idiom import closest_words
# Example: Find the closest words to 'mad' that start with 'l'
starts_with_L = lambda x: x.startswith('l')
print(closest_words('mad', k=10, search_words=starts_with_L))
```
### Accessing Word Frequencies
You can access the most frequent words using the `most_frequent_words` function:
```python
from idiom import most_frequent_words
# Get the top 100,000 most frequent words
frequent_words = most_frequent_words(max_n_words=100000)
print(frequent_words)
```
### Working with Word Vectors
You can load and work with pre-trained word vectors using the `WordVec` class:
```python
from idiom import WordVec
# Initialize WordVec with default word vectors
word_vec = WordVec()
# Calculate the distance between two queries
distance = word_vec.dist('france capital', 'paris')
print(distance)
```
### IDF Calculations
You can compute different types of IDF values using the `_IDF` class:
```python
from idiom import idf
# Access logarithmic IDF values
log_idf = idf.logarithmic
print(log_idf)
```
## Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue on GitHub.
## License
This project is licensed under the MIT License.
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"description": "# idiom\n\nAccess and operations with word2vec data\n\nTo install:\t```pip install idiom```\n\n## Overview\n\nThe `idiom` package provides access to word vector data and useful functions to manipulate and analyze it. It includes functionalities for finding the closest words to a given word, calculating word frequencies, and working with various word vector models.\n\n## Features\n\n- **Closest Words**: Find the closest words to a given word based on cosine similarity.\n- **Word Frequencies**: Access and manipulate word frequency data.\n- **Word Vector Models**: Work with pre-trained word vector models such as FastText.\n- **IDF Calculations**: Compute different types of Inverse Document Frequency (IDF) values.\n\n## Usage\n\n### Finding Closest Words\n\nYou can find the closest words to a given word using the `closest_words` function:\n\n```python\nfrom idiom import closest_words\n\n# Example: Find the closest words to 'mad' that start with 'l'\nstarts_with_L = lambda x: x.startswith('l')\nprint(closest_words('mad', k=10, search_words=starts_with_L))\n```\n\n### Accessing Word Frequencies\n\nYou can access the most frequent words using the `most_frequent_words` function:\n\n```python\nfrom idiom import most_frequent_words\n\n# Get the top 100,000 most frequent words\nfrequent_words = most_frequent_words(max_n_words=100000)\nprint(frequent_words)\n```\n\n### Working with Word Vectors\n\nYou can load and work with pre-trained word vectors using the `WordVec` class:\n\n```python\nfrom idiom import WordVec\n\n# Initialize WordVec with default word vectors\nword_vec = WordVec()\n\n# Calculate the distance between two queries\ndistance = word_vec.dist('france capital', 'paris')\nprint(distance)\n```\n\n### IDF Calculations\n\nYou can compute different types of IDF values using the `_IDF` class:\n\n```python\nfrom idiom import idf\n\n# Access logarithmic IDF values\nlog_idf = idf.logarithmic\nprint(log_idf)\n```\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a pull request or open an issue on GitHub.\n\n## License\n\nThis project is licensed under the MIT License.\n\n\n",
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