Embedding

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• Embedding ... Fine-tuning ... RAG ... Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction. ...find outliers
• Prompting vs AI Model Fine-Tuning vs AI Embeddings
• AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
• Math for Intelligence ... Finding Paul Revere ... Social Network Analysis (SNA) ... Dot Product ... Kernel Trick
• Capabilities:
  • Semantic Search (where results are ranked by relevance to a query string)
  • Clustering (where text strings are grouped by similarity)
  • Recommendations (where items with related text strings are recommended)
  • Anomaly Detection (where outliers with little relatedness are identified)
  • Classification (where text strings are classified by their most similar label)
  • Dimensional Reduction
  • ...find outliers ... diversity measurement (where similarity distributions are analyzed)

Types:

• Local Linear Embedding (LLE)
• T-Distributed Stochastic Neighbor Embedding (t-SNE)

AI embeddings are a way for computers to understand the meaning of words, images, and other types of data. They are created using machine learning algorithms, which learn to represent the data in a way that captures its relationships to other data. For example, a text embedding algorithm might learn to map words to a vector space in which similar words are close to each other and dissimilar words are far apart.

Once embeddings have been created, they can be used as input to a variety of machine learning models. For example, text embeddings can be used to train a machine translation model, which can then translate text from one language to another. Image embeddings can be used to train an image classification model, which can then identify different objects in images. Graph embeddings can be used to train a node classification model, which can then identify different types of nodes in a network.

Embeddings are a powerful tool that can be used to improve the performance of machine learning models on a wide variety of tasks. By learning to represent data in a lower-dimensional space, embeddings can help machine learning models to learn more complex patterns and relationships in the data.

Imagine you have a big bag of words, and you want to teach a computer to understand the meaning of those words. You could start by grouping the words together based on their similarity. For example, you might put the words "cat," "dog," and "animal" in one group, and the words "red," "blue," and "green" in another group.

Once you have grouped the words together, you can teach the computer to represent each group of words with a number. This is called an embedding. For example, you might give the group of words "cat," "dog," and "animal" the embedding 1, and the group of words "red," "blue," and "green" the embedding.

Now, when the computer sees a new word, it can try to find the group of words that the new word is most similar to. This will give the computer a good idea of what the new word means.

Embeddings can be used to represent not only words, but also images, sounds, and other types of data. This makes them a very powerful tool for machine learning models.

Here is an example of how embeddings can be used in a machine learning model:

Imagine you are training a machine translation model to translate text from English to Spanish. You would start by creating embeddings for both English and Spanish words. Then, you would train the machine translation model to predict the Spanish embedding for a given English embedding.

Once the machine translation model is trained, it can be used to translate text from English to Spanish by predicting the Spanish embedding for each English word in the text, and then converting the Spanish embeddings to Spanish words.

AI Encoding & AI Embedding

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The terms "AI encodings" and "AI embeddings" are sometimes used interchangeably, but there is a subtle difference between the two.

• Encodings are a general term for any representation of data that is used by a Machine Learning (ML) model. This could be a one-hot encoding, a bag-of-words representation, or a more complex representation such as a word embedding.
• Embeddings are a specific type of AI encoding that is learned from data. Embeddings are typically represented as vectors of real numbers, and they capture the meaning and context of the data they represent.

In other words, all embeddings are encodings, but not all encodings are embeddings. Here are some examples of AI encodings that are not embeddings:

• One-hot Encoding is a simple way to represent categorical data as a vector. For example, the word "dog" would be represented as a vector of 100 zeros, with a single 1 at the index corresponding to the word "dog" in a vocabulary of 100 words.
• Bag-of-words is a more sophisticated way to represent text data as a vector. This involves counting the number of times each word appears in a document, and then representing the document as a vector of these counts.

Embedding Types

AI Embeddings are a type of representation of text that captures the meaning of the text. This can be used for tasks such as search, classification, and recommendation. allow the model to search in a “database” and return the best result. Here are some examples of AI Embeddings:

• Word embeddings: are a type of embedding that represents words as vectors of real numbers. These vectors are typically learned from a large corpus of text, and they capture the meaning and context of the words they represent.
• Image embeddings: are a type of embedding that represents images as vectors of real numbers. These vectors are typically learned from a large dataset of images, and they capture the visual features of the images they represent.
• Graph embeddings: Graph embeddings are used to represent nodes and edges in a graph in a way that captures their relationships to each other. This is useful for a variety of network analysis tasks, such as community detection, link prediction, and node classification.
• Audio embeddings: Audio embeddings are used to represent audio signals in a way that captures their acoustic features. This is useful for a variety of audio processing tasks, such as speech recognition, music classification, and sound event detection.

In addition to these general types of embeddings, there are also more specialized types of embeddings that have been developed for specific tasks or applications. For example, there are embeddings for code, video, and chemical compounds.

Traditional Approach

• Data Science in 5 Minutes: What is One Hot Encoding? | Amanda Fawcett - Educative
• A Count-based and Predictive vector models in the Semantic Age | Dateme Tubotamuno
• TF-IDF Simplified | Luthfi Ramadhan - Towards Data Science ... A short introduction to TF-IDF vectorizer
• A Comprehensive Guide To Feature Engineering with N-Grams For Natural Language Processing | David Adamson

• Binary Encoding: the categorical values are first mapped to integer values. Then, each integer value is represented as a binary vector, where the index of the integer is marked with a 1 and all other values are 0. For example, if we have three categories: red, green, and blue, we can assign them the integer values 1, 2, and 3. The binary encoding would be:
  • Red: 1 -> 001
  • Green: 2 -> 010
  • Blue: 3 -> 011

• One-Hot: the categorical values are also mapped to integer values. However, instead of using a binary vector with a single 1 at the index of the integer, we create a new binary variable for each unique integer value. Using the Binary Encoding example, the one-hot encoding would be:
  • Red: 1 -> 100
  • Green: 2 -> 010
  • Blue: 3 -> 001

• Count-based: Count-based embedding is a method of representing words or phrases as vectors based on the frequency of their co-occurrence with other words or phrases in a given context. This approach is often used in Natural Language Processing (NLP) tasks, such as text classification and word similarity analysis. The main idea behind count-based embedding is to capture the relationship between words by counting how often they appear together in a corpus of text. This information is then used to construct a vector representation for each word, where the elements of the vector correspond to the frequency of co-occurrence with other words. One common way to create count-based embeddings is by using a co-occurrence matrix, which is a square matrix where each row and column represents a unique word in the vocabulary, and the value at each position (i, j) represents the frequency of co-occurrence between the words i and j. This matrix can be large and memory-intensive, so dimensionality reduction techniques are often applied to make the model more efficient and robust. Count-based embeddings have some advantages and disadvantages compared to other methods, such as predictive models like Word2Vec. On the one hand, count-based methods can capture more significant information about word relationships, as they consider the entire co-occurrence matrix. On the other hand, they tend to consume more memory and may include some noise or less significant information

• TF-IDF techniques for vectorization: Term Frequency–Inverse Document Frequency (TF-IDF) has several advantages, including its simplicity, computational efficiency, and effectiveness in capturing the importance of words in a document. However, it also has limitations, such as not capturing the semantic meaning of words.

• Capturing local context with n-grams and challenges : N-grams are a way of capturing local context in a sequence of words. An n-gram is a contiguous sequence of n words. For example, the 3-grams of the sentence "The quick brown fox jumps over the lazy dog" are:
  • "The quick brown fox"
  • "quick brown fox jumps"
  • "brown fox jumps over"
  • "fox jumps over the"
  • "jumps over the lazy"
  • "over the lazy dog"

N-grams can be used to represent words in a way that captures their local context. This can be useful for natural language processing (NLP) tasks such as text classification, sentiment analysis, and machine translation. For example, an NLP model that is trained on n-grams of text data may be able to better understand the meaning of words that are ambiguous or that have different meanings depending on the context in which they are used. However, there are also some challenges associated with using n-grams in NLP. One challenge is that n-grams can be very sparse, especially for higher values of n. This means that many n-grams may not appear in the training data at all. This can make it difficult to train NLP models that are based on n-grams. Another challenge is that n-grams can be sensitive to the order of words in the sentence. This means that two n-grams that are very similar in meaning may have different representations if the words are in a different order. This can make it difficult to use n-grams for NLP tasks such as machine translation, where the order of words is not always preserved.

Semantic Encoding Approach

• NLP Zero to One: Dense Representations, Word2Vec (Part 5/30) | Kowshik chilamkurthy - Nerd For Tech - Medium ... Word Embeddings and Semantic Representations

• Word2Vec and dense word embeddings: Word2Vec is a semantic encoding approach that learns dense word embeddings. Dense word embeddings are vectors of real numbers that represent words in a way that captures their semantic relationships. For example, the embeddings for the words "king" and "queen" would be close together, while the embeddings for the words "king" and "apple" would be far apart. Word2Vec learns word embeddings by training a neural network to predict the context of a given word. The context of a word is the set of words that often appear around it in text. For example, some of the context words for the word "king" might be "queen", "crown", and "throne".

Text Embeddings & Text Similarity Measures

• How the dot product measures similarity | Tivadar Danka
• Introduction to Word and Sentence Embedding | Abdulkader Helwan - Medium
• Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation | Viktor Karlsson - DAIR.AI - Medium

• Dot product: a technique for representing data as a vector of real numbers in a way that preserves semantic relationships. The dot product embedding of a vector is calculated by multiplying the vector with a learned embedding matrix. The embedding matrix is a matrix of real numbers that has been learned to capture the semantic relationships between words, sentences, or documents. Once the dot product embedding of a vector has been calculated, it can be used to compare the vector to other vectors by calculating the dot product between the embeddings. The dot product between two embeddings is a measure of their similarity. A higher dot product indicates more similarity.

• Cosine Similarity: type of embedding that is designed to capture the semantic similarity between words, sentences, or documents. It is based on the idea that similar entities will have similar vectors in a high-dimensional space. Cosine similarity is a measure of the similarity between two vectors based on the cosine of the angle between them. It is calculated by dividing the dot product of the two vectors by the product of their magnitudes. Cosine similarity is a normalized value, meaning it is always between 0 and 1, with 1 indicating perfect similarity and 0 indicating no similarity. When cosine similarity is used to embed words, sentences, or documents, the vectors are typically learned using a neural network. The neural network is trained to predict the context of a given word, sentence, or document based on its embedding. As the neural network is trained, it learns to associate each entity with a vector that captures its semantic relationships.

• Inner Product: type of embedding that is based on the inner product of two vectors. The inner product of two vectors is a scalar value that is calculated by multiplying the corresponding components of the vectors and adding the products together. The inner product embedding of a vector is calculated by multiplying the vector with a learned embedding matrix. The embedding matrix is a matrix of real numbers that has been learned to capture the semantic relationships between words, sentences, or documents. Once the inner product embedding of a vector has been calculated, it can be used to compare the vector to other vectors by calculating the inner product between the embeddings. The inner product between two embeddings is a measure of their similarity. A higher inner product indicates more similarity.

• Word and sentence embeddings: are numerical representations of words and sentences, respectively, that capture their underlying semantics and meaning. Word embeddings are typically learned using a neural network that is trained on a large corpus of text. The neural network learns to associate each word with a vector of real numbers that captures its semantic relationships. For example, the embeddings for the words "king" and "queen" would be close together, while the embeddings for the words "king" and "apple" would be far apart. Sentence embeddings are typically learned by combining the embeddings of the individual words in the sentence. This can be done using a variety of methods, such as averaging the embeddings of the words or using a more complex neural network model.

• Multilingual sentence embeddings: are numerical representations of sentences in multiple languages that capture their underlying semantics and meaning. They are used in a variety of natural language processing (NLP) tasks, such as cross-lingual text classification, cross-lingual sentiment analysis, and cross-lingual machine translation. Multilingual sentence embeddings are typically learned using a neural network that is trained on a large corpus of text in multiple languages. The neural network learns to associate each sentence with a vector of real numbers that captures its semantic relationships, regardless of the language of the sentence. Multilingual sentence embeddings are powerful tools for cross-lingual NLP tasks because they are able to capture the semantic relationships between sentences in different languages. This makes them more effective than other methods of data representation, such as one-hot encoding. Here are some examples of how multilingual sentence embeddings are used in NLP:

• Cross-lingual text classification: Multilingual sentence embeddings can be used to represent text documents in multiple languages as vectors of real numbers. These vectors can then be used to train a machine learning model to classify documents into different categories, regardless of the language of the document. For example, a machine learning model could be trained to classify news articles into different categories such as politics, sports, and entertainment using the multilingual sentence embeddings of the words in the articles.
• Cross-lingual sentiment analysis: Multilingual sentence embeddings can be used to represent the sentiment of text documents in multiple languages. This can be done by training a machine learning model to predict the sentiment of a document based on the multilingual sentence embeddings of the words in the document. For example, a machine learning model could be trained to predict whether a tweet is positive, negative, or neutral using the multilingual sentence embeddings of the words in the tweet.
• Cross-lingual machine translation: Multilingual sentence embeddings can be used to translate text from one language to another. This can be done by training a machine learning model to translate text based on the multilingual sentence embeddings of the words in the text. For example, a machine learning model could be trained to translate sentences from English to Spanish using the multilingual sentence embeddings of the words in the sentences.

Multilingual sentence embeddings are a powerful tool for cross-lingual NLP tasks. They are able to capture the semantic relationships between sentences in different languages, which makes them more effective than other methods of data representation. Here are some of the advantages of using multilingual sentence embeddings:

• They are able to capture the semantic relationships between sentences in different languages.
• They are a flexible and powerful tool that can be used in a variety of cross-lingual NLP tasks.
• They are becoming increasingly easier to train and use.

However, there are also some disadvantages to using multilingual sentence embeddings:

• They can be computationally expensive to train, especially for large datasets in multiple languages.
• They may not perform well on new data that is different from the training data.
• Multilingual sentence embeddings may not be suitable for all cross-lingual NLP tasks. For example, they may not be effective for tasks that require reasoning or knowledge about the world.

Embedding Use Cases

• projecting an input into another more convenient representation space. For example we can project (embed) faces into a space in which face matching can be more reliable. | Chomba Bupe
• a mapping of a discrete — categorical — variable to a vector of continuous numbers. In the context of neural networks, embeddings are low-dimensional, learned continuous vector representations of discrete variables. Neural Network embeddings are useful because they can reduce the dimensionality of categorical variables and meaningfully represent categories in the transformed space. Neural Network Embeddings Explained | Will Koehrsen - Towards Data Science
• a relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do Machine Learning (ML) on large inputs like sparse vectors representing words. Ideally, an embedding captures some of the semantics of the input by placing semantically similar inputs close together in the embedding space. An embedding can be learned and reused across models. Embeddings | Machine Learning Crash Course

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By employing techniques like Word Embeddings, Sentence Embeddings, or Contextual embedding, vector embeddings provide a compact and meaningful representation of textual data. Word embeddings, for instance, map words to fixed-length vectors, where words with similar meanings are positioned closer to one another in the vector space. This allows for efficient semantic search, information retrieval, and language understanding tasks.

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Embeddings have 3 primary purposes:

1. Finding nearest neighbors in the embedding space. These can be used to make recommendations based on user interests or cluster categories.
2. As input to a Machine Learning (ML) model for a supervised task.
3. For visualization of concepts and relations between categories.

How to Generate the Correct Embeddings

• Vector Embeddings: From the Basics to Production | Sam Partee - Partee.IO
• How to Get the Right Vector Embeddings | Yujian Tang - The New Stack ... A comprehensive introduction to vector embeddings and how to generate them with popular open source models.
• Choosing the Right Embedding Model: A Guide for LLM Applications | Ryan Nguyen - Medium

To generate the right embeddings, you need to consider the following factors:

The type of data you are embedding: Are you embedding text, images, videos, or something else? Different types of data require different embedding models. For example, you would use a different model to embed text than images.

The task you are using the embeddings for: What do you want to do with the embeddings? Are you using them for classification, regression, or something else? The task you are using the embeddings for will affect the type of embedding model you choose and the parameters you use to train it.

The size of your dataset: How much data do you have to train your embedding model? If you have a small dataset, you may need to use a simpler embedding model.

The resources you have available: How much time and computing power do you have to train your embedding model? More complex embedding models require more training time and computing power.

Once you have considered these factors, you can choose an embedding model and train it on your data. Here is a general overview of the process:

1. Choose an embedding model. There are many different embedding models available, both pre-trained and trainable. Some popular embedding models include Word2Vec, Global Vectors for Word Representation (GloVe), FastText, BERT, and ResNet-50. 2. Preprocess your data. This may involve cleaning your data, removing stop words, and converting text to lowercase. 3. Train the embedding model. This process can be time-consuming, depending on the size and complexity of your dataset and the embedding model you are using. 4. Evaluate the embedding model. Once the model is trained, you should evaluate its performance on a held-out test set. This will help you to determine if the model is able to generate good embeddings for your data. 5. Use the embedding model. Once you are satisfied with the performance of the embedding model, you can use it to generate embeddings for your data and use those embeddings in your downstream task.

Here are some additional tips for generating good embeddings:

• Use a large and diverse dataset to train your embedding model.
• Use a pre-trained embedding model if possible. This can save you a lot of time and effort, especially if you have a small dataset.
• Fine-tune the pre-trained embedding model on your data if necessary. This can improve the performance of the embedding model on your downstream task.
• Use a hyperparameter tuning library to tune the parameters of your embedding model. This can help you to find the best parameters for your data and task.

If you are new to embedding, start with a pre-trained embedding model and fine-tuning it on your data. This is a relatively easy way to get started with embeddings and generate good results.

OpenAI Note

• New and improved embedding model ... We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use.
• Embeddings

Embeddings are a numerical representation of text that can be used to measure the relateness between two pieces of text. Our second generation embedding model, text-embedding-ada-002 is a designed to replace the previous 16 first-generation embedding models at a fraction of the cost. An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.