The WordEmbeddings model is a VB.NET implementation of the Word Embeddings technique, which represents words as dense vectors in a continuous vector space. This model allows you to train word embeddings on a given corpus and perform various operations such as calculating similarity between words, discovering collocations, and generating training data.

• Train the WordEmbeddings model using a corpus of text
• Calculate Pointwise Mutual Information (PMI) matrix for the trained model
• Discover collocations (word pairs) based on trained word embeddings
• Find most similar words to a given word
• Generate training data for machine learning tasks using the trained model
• Save and load the trained model for future use

1. Install the necessary dependencies (e.g., .NET framework) for running VB.NET applications.

2. Clone or download the repository to your local machine.

3. Import the WordEmbeddings class into your VB.NET project.

4. Initialize an instance of the WordEmbeddings model with the desired parameters:

   Dim model As New WordEmbeddings(embeddingSize:=100, learningRate:=0.01, windowSize:=5)

Dim corpus As String() = {
    "united states is a country.", "England is a country",
    "the united kingdom is in europe.",
    "united airlines is an airline company.",
    "doberman is a breed of dog.",
    "dogs have many breeds.",
    "dogs love eating pizza."
}
model.Train(corpus)

Dim pmiMatrix As Dictionary(Of String, Dictionary(Of String, Double)) = model.CalculatePMI()

Dim words As String() = {"united", "states", "kingdom", "airlines"}
Dim collocations As List(Of Tuple(Of String, String)) = model.DiscoverCollocations(words, threshold:=1)

Dim similarWords As List(Of String) = model.GetMostSimilarWords("dog", topK:=3)

' Save the model
model.SaveModel("path/to/save/model.json")

' Load the model
Dim loadedModel As WordEmbeddings = WordEmbeddings.LoadModel("path/to/saved/model.json")

The WordEmbeddings model requires the following dependencies:

License This project is licensed under the MIT License.

Contributions to the WordEmbeddings model are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.