Questions tagged [embeddings]

For questions about embeddings (not necessarily just word embeddings, for which there is a specific tag) in the context of machine learning.

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Using Langchain with Open Search and multiple LLMs

I have some questions about using langchain and Open Search as a vector database to implement RAG (Retrieval Augmented Generation) for multiple LLMs. My questions are: If langchain can provide LLM-...
Mark_Sagecy's user avatar
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How do I obtain human-readable descriptions (keywords, phrases, etc.) for an embedding vector

I am using an embedding model to obtain vectors corresponding to a group of short documents. I then cluster related documents using these vectors. For each cluster, I would like to describe to the ...
davidconner's user avatar
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Best way to create a summary of variable length set of vectors where order does not matter

I'm trying to design a system to optimize over a variable-length set (like a sentence) of variable length vectors (like words). But unlike a sentence, the order of words does not matter. I'll have to ...
hasdrubal's user avatar
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Fine-tuned Stable Diffusion not converging with custom encoder

I'm currently fine-tuning a stable diffusion model for the task of dataset augmentation. I am training the model on 80k images from hte CelebA-attributes dataset, replacing the text encoder with ...
Tomas Premoli Muniagurria's user avatar
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Can all good language models be used as a good embedding model and vice versa?

Does solving the language modeling task go hand in hand with finding a good embedding?
JobHunter69's user avatar
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How do I code so that the embedding output and input share the same weight matrices?

I am trying to implement the Attention is All You Need paper from scratch. The authors mentioned in section 3.4 that "In our model, we share the same weight matrix between the two embedding ...
OneMoreGamble's user avatar
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How to classify a new node into an existing cluster of nodes?

I have a graph with many disjoint subgraphs that are not connected to each other. Essentially these subgraphs could represent different clusters. What is a general process to figure out node ...
Kiran Manicka's user avatar
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Navigating encoder-only text/sentence embedding models

tldr; I am researching/looking for vec2text methods that work (a) without training a special model to guide the mutations, and (b) without access to the model weights. I am doing some experiments in ...
Realz Slaw's user avatar
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which the effective ranks of embedding matrices in Large LMs?

The hidden size of current LLMs is well in the 8K and above, and I was wondering, has the rank of the embedding matrix, and the rank of the transpose of the output matrix, been increasing with the ...
arivero's user avatar
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Approaching construction of model that interprets financial reports [closed]

I want to train a model to be able to interpret financial reports (from a company). Basically, I want to be able to extract relevant information without needing to read through hundreds of pages of ...
Hlkwtz's user avatar
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Do diffusion models take a long time to train?

I am trying to train a diffusion model (from scratch in pytorch). UNet used is not anything too fancy, takes in images and time step as input for about 512 time steps. I am using learnable embeddings ...
Aditya 's user avatar
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Encoding categorical data with "many" unique values in neural network

I am new to machine learning, in fact, I am implementing my first deep neural network from scratch without any framework. The dataset has 3500 rows, and 4 categorial columns of which two have about ...
M a m a D's user avatar
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From where do the Encoders in Transformers gets Input Embedding from?

In Transformers Encoders, from where do the Encoders get Input Embedding from? So when a sentence is given to a transformer-based model it first tokenises the sentence and each token is mapped with ...
Swastik's user avatar
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Why is $z$ considered a random variable in the following formulation of Locally Linear Embedding Problem?

The following is the derivation of "Locally Linear Embedding Problem" from the book Machine Learning, 4-th edt, page 152, by E.Alpaydin. Why is $z$ considered a random variable so that $E(z)...
DSPinfinity's user avatar
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Diffusion Model Failing to Learn

I'm trying to train a diffusion model to map between paired embedding spaces - ie using a CLIP text embedding to predict a CLIP image embedding. I have a working baseline model that predicts the ...
Karl's user avatar
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Why are the vector stores I generate via langchain FAISS from_documents always ~4.8 GB in size?

I am working on a small application to query a set of PDF documents. Specifically, I am aiming to provide the same query to each document in parallel, not to all documents as a whole. I have utilized <...
KDecker's user avatar
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How can I teach a book to an LLM?

I am trying to find out how I can teach the content of a whole, multiple hundert pages book to an LLM so that it "knows" all details and can be queried, give summaries etc. The book is one ...
dschuld's user avatar
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How to recover the text of the sentence from an embedding (vec2text)?

I'm trying to re-invent the code that would do it in the same way as in the vec2text article. Does anybody here have something similar?
Alex Fedotov's user avatar
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262 views

Understanding embedding outputs in transformer models like CLIP

I'm working with OpenAI's CLIP model and trying to understand the output of the text encoder. When I input a short prompt like "cat", the output is a tensor of shape [77, 1024]. My ...
hanugm's user avatar
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Seq2Seq model- Confusing about the dimension of Seq2Seq model [closed]

I am new to Seq2Seq and hope to find a proper guildances, advices. I am doing a Project from an online course so I can not give the material but I got my Project notebook on Github I want to ask ...
QH.Chu's user avatar
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Can transformers autoregressively generate a sequence of embeddings (instead of predictions)?

Is it theoretically possible to use a transformer architecture to autoregressively generate a sequence of embedding vectors, instead of discrete tokens? For example, if I were to provide an input of a ...
Theo Coombes's user avatar
3 votes
1 answer
237 views

Match two paragraphs of text

I'm building a friend finder app and I need to match people based on a paragraph of text. Here is an example of what I mean: Person A: I love walking and going to the beach, I also love reading and ...
Dom's user avatar
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Why does LLM inference cost scale in both input tokens and output tokens?

EDIT This question was flawed. See my answer with help from commenters. Original question This question has been asked in other forums [1] [2] but I'm not sure I understand the claims, which are (...
llllvvuu's user avatar
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Is there an ANN vector-search index that supports incremental ingestion and deletion of elements?

I have looked at a few libraries for ANN search of high dimensional vectors. Although impressive, they come with a huge baggage of fine print. Many of them only support ingesting the vectors in one ...
morpheus's user avatar
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Update OpenAI embedding based on own domain corpus

I have a large domain corpus. Is there anyway to update the word/sentence/document embedding obtained from OpenAI embedding API based on my own domain corpus? There may be some words in my corpus that ...
Salty Gold Fish's user avatar
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Mining overlapping orthogonal clusters from high dimensional data

What are the recommended strategies for identifying overlapping and somewhat orthogonal clusters in a large, high-dimensional dataset? As an illustration, consider a dataset comprised of various ...
curl-up's user avatar
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1 answer
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Ignoring aspects of text embeddings, e.g. making the embedding topic-agnostic

Imagine a large set of text embeddings (e.g. by OpenAI model), created on user inputs in a natural language interface (e.g. a semantic search app), which we want to cluster on some "non-topic ...
curl-up's user avatar
1 vote
0 answers
253 views

How is OpenAI embeddings obtained

There is OpenAI embedding API https://platform.openai.com/docs/guides/embeddings. How is this embedding related to the GPT3.5 transformer model architecture? Is it the vectors learned from the input ...
Salty Gold Fish's user avatar
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141 views

Regarding the use of Time2Vec as positional encoding for Timeseries Transformer

Although the transformer architecture was originally designed for NLP, there exists several articles and papers that attempt to apply the same architecture for numerical timeseries classification. ...
Enk9456's user avatar
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Are the dimensions in embedding vectors ordered (similar to PCA)?

I am getting started with the vector embeddings. I have a general question about the embedding vectors generated by popular algorithms. In PCA, usually, there is an implicit order of importance in the ...
JackDaniels's user avatar
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How to learn Categorial Embeddings in Unsupervised Learning?

I want to cluster mixed-type tabular data, for the categorial columns I want to use Categorial Embeddings and then an Autoencoder Network before clustering with KMeans or similar. Now, when I want to ...
Jaanis's user avatar
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Is it possible to generate new text matching a vector embedding with an LLM

I'm interested in generating variations of text using an LLM - is it possible to take a text embedding, move it in different directions in vector space, and generate new text from the resulting ...
Charles's user avatar
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3 votes
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395 views

How to work with multiple embeddings?

This is a conceptual gap that I have concerning embeddings, and would really appreciate some help closing it. I understand when you embed a corpus for, let's say, a question-and-answer task you can ...
Ian Murray's user avatar
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120 views

Improving embedding similarity search of aggregated embeddings

I am building an author suggestion tool that proposes authors writing about similar topics as a given input text. I want to use embeddings for this. The way I currently do it is to store embeddings of ...
Stefan's user avatar
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2 answers
999 views

How do I choose a good treshold for classification (using cosine similarity scores)?

I am using openai's text-embedding-ada-002 embeddings model to do a semantic search on a database of articles to find articles that are most related to a given ...
Stefan's user avatar
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2 votes
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123 views

Nearest neighbour search in high dimension retrieves certain points too often

We represent some catalogue items (documents, music tracks, videos, whatever) as vectors embedded in R^d and use them to retrieve nearest neighbours to users query. The typical scenario is that users ...
Peter Franek's user avatar
1 vote
2 answers
757 views

How can I send vectors as a chat context?

Since the context/memory of a chat or question for LLMs more precisely GPT is limited to a token length I struggle about how to provide own data that the model got not trained on. A very common ...
dc10's user avatar
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How to inspect a dimension in SBERT embeddings

I am using SBERT transformers to vectorize text. I want to use the sentence embeddings in a linear regression to predict a value or a classification. Some dimensions are highly colinear and result in ...
miguelmorin's user avatar
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1 answer
123 views

What do different embedding functions do?

Embeddings are just arrays of numbers that represent some data (sentences or images). How do embedding functions differ. For example what advantage does OpenAI's embeddings API have over others?
felipe57387's user avatar
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38 views

How to perform graph embedding on a DAG graph with point weights and restore the resulting embedding vector to the DAG with point weights?

How to perform graph embedding on a directed acyclic graph with point weights and restore the resulting embedding vector to the directed acyclic graph with point weights?
user68072's user avatar
1 vote
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117 views

Is the input embedding split along the embedding dimension so that every head of the multi-head-attention module just gets a part of the input data?

So I found two contradictory explanations of the MHA (multi-head-self-attention-module): In the first approach, the input embedding (= the input matrix) is split along the embedding dimension and all ...
AAT's user avatar
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2 votes
1 answer
625 views

Creating a support chat bot for my business

I am trying to create a kind of support bot to answer questions from my clients about specific technical details about WordPress plugins that I sell. The goal is that the ...
digitalzoomstudio's user avatar
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147 views

Using a VQ-VAE encoder's output to condition another model

I've trained a VQ-VAE on images that I want to use to condition another model (latent diffusion). The shape of my encoder's output is (4, 256, 56, 56) and I'm getting it using: ...
jbm's user avatar
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1 answer
189 views

Are there methods which represent entire knowledge graphs in one embedding vector?

In a knowledge graph, embedding vectors can be learned for nodes (node embedding) and edges (edge embeddings). Is there a method to learn one single embedding vector for the entire knowledge graph?
skumaravel's user avatar
1 vote
1 answer
793 views

How to get meaningful vector embeddings for (lat, long) points and also GPS trajectories?

I have a data that consists of approx. 1.5M taxi trips in Porto, Portugal. (from: https://www.kaggle.com/competitions/pkdd-15-taxi-trip-time-prediction-ii/overview) Each of these trips have it's GPS ...
I am not a robot's user avatar
1 vote
0 answers
100 views

Privacy implications of storing and transmitting GPT-3 embeddings?

We are exploring implementing a feature where a user might enter "which product has optional all wheel drive" into a search input, which would be transformed to GPT-3 embeddings, and ...
jahghAAB's user avatar
1 vote
0 answers
146 views

Use multiple embeddings with the Transformer architecture

In their article about their system "MuseNet" OpenAI state the following: Embeddings We added several different kinds of embeddings to give the model more structural context. In addition to ...
Felix Schön's user avatar
1 vote
1 answer
78 views

How to embed quantitative variables?

I have 3 data types that I want to feed into a neural net. One, is a time series, which I am going to feed into the neural net directly. The second, are categorical variables that I am going to embed ...
Mattpats's user avatar
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3 votes
3 answers
2k views

How embeddings learned from one model can be used in another?

In the website the following explanation is provided about Embedding layer: The Embedding layer is initialized with random weights and will learn an embedding for all of the words in the training ...
Oculu 's user avatar
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4 votes
2 answers
2k views

What is the difference between representation and embedding?

As I searched about this two terms, I found they are somehow like each other, both try to create a vector from raw data as I understood. But, what is the difference of this two term?
aliiiiiiiiiiiiiiiiiiiii's user avatar