Questions tagged [embeddings]
The embeddings tag has no usage guidance.
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Why is T5 often used in text-to-data for text prompt encoders?
In the text-to-data(music, image, audio, etc.) generative AI field, one method of encoding text prompts is to use pre-trained language models. Such an approach was used in research on Moûsai [1] and ...
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Combining Textual, Categorical and Numerical data for Semantic Search using SentenceTransformers model
I'm building a food semantic search model and I want to use a pre-trained SentenceTransformers model with cosine similarity. I'm using Epicurious dataset for the corpus which consists of textual (&...
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Using text embeddings directly to compute similarity vs using them as features for a model that predicts similariy
Say you have a problem where you have a query and a set of result documents and you want to rank the result documents according to the query. Say also you have embeddings for the query and for the ...
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CLIP Visual Transformer image encoder
I was doing some experiments with the CLIP's visual transformer encoder output (clip-ViT-B-32). So basically given the same scene or image, it should output almost ...
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Are the terms "vector embedding" and "embedding vector" interchangeable?
Googling "vector embedding" gives me ~40k results, including from Pinecone, Weaviate, and OpenAI.
Googling ...
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Are Vector embeddings position or direction vectors?
I've seen statements about embeddings being positions on a hypersphere. I've also seen statements about using dot product to determine similarity of these positions. This implies to me that we care ...
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200
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How to add knowledge to the LLM using LangChain (at a high level)?
At a super high level, I would like to create a fantasy language AI tutor. For this question, however, I would like to better understand how, generally speaking, you add your own custom data/knowledge ...
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Why cant we use normalise position encodings instead of the cos and sine encodings used in the Transformer paper?
I'm working with Transformer models for sequence-to-sequence tasks and I'm trying to fully understand the use of positional encodings in these models.
In the original "Attention is All You Need&...
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121
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Recommended way to embed a text thousands of tokens long?
I've split the text up sections each 512 tokens long and created embeddings for each of them.
I want to combine them into 1 embedding for the full text. How do I do that? Is this even recommended?
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35
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Why are similarities all close to 1 in my two tower recommender?
I am trying to build a simple two tower recommender system on the MovieLens 100k dataset. The user tower is just a simple embedding layer. The item tower uses an embedding layer and concats that with ...
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How do I interpret low dimentional embeddings of high dimentional embeddings?
I am trying to understand what I am supposed to learn about a problem when using dimensionality reduction methods. In particular, I am referring to methods like t-SNE and UMAP.
For the most part I am ...
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High-Cardinality Categorical Feature with frequency score
I'm working with a really high-cardinality feature as one of the inputs to my model and I'm using hash-encoded feature embedding rather than one-hot encoding. However, this method is ignoring the ...
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Otimization of similarity search for multiple embeddings by creating a weighted artificial embedding
I have embeddings of text created with a BERT model. A group of these embeddings should be used to find similar embeddings corresponding to this group. I know that you can use average or max (or ...
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Max word embedding
I'm trying to understand the concept behind Max Word / Sentence embeddings also in contrast to mean word embedding. They used it for example in this article. So far I didn't find good resources which ...
- 113
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Distance Metric for a dataset with embeddings and numerical columns
I'm trying to build an Approximate Nearest Neighbours model that can fetch similar records in a dataset, that are contextually similar. For example, for a record of job=Software Engineer and age=25, a ...
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Align sub-sentences with sentences using embeddings
I would like ask for ideas
So I have sub-sentences, embedded in the context of their respective full-sentences. Then, I have other full-sentences and I would like to find
a) if they have similar sub-...
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How to generate embedding for Image Dataset with multiple features?
My dataset has Jewellery images. The jewellery is of 4 types:
Type
Color
Shape
Material
Now I am aware of converting images to embeddings. I need to group similar images like two plain gold rings ...
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How the embedding model (x-vectors) trained?
I read this paper: X-Vectors: Robust DNN Embeddings for Speaker Recognition which describes how PyAnnote embedding block works.
I'm not sure I understand how the X-Vector model was trained and tested:
...
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141
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SBERT Embeddings from Conversations
I have a dataset consisting of text-based conversations between two humans. One conversation has on average 20 turns and can look as follows:
...
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In contrastive loss, why do we sample negatives from the document, not the query?
From what I read, for image search contrastive training, we give one query in text, and 1 positive image, and (N-1) sampled negative images.
However, for video search contrastive training, we give 1 ...
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Does word2vec skip gram involves softmax in the output layer
I was going through various pytorch and from-scratch implementations of skip-gram. I found following:
This implementaiton does not seem to use softmax
...
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Visualizing convolutional neural networks embedding
In this article, the author creates a graph (at the end of the post) from the embeddings of different words found by transformer model. I would like to do a similar thing for a convolutional neural ...
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Inductive embedding technique for nodes without features
I am working on a project that involves graph embeddings. I have an homogeneous directed graph where the nodes have no features. The only available information are the edges between nodes. I need to ...
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Is there a way to embed curves into smaller vectors?
I know about embeddings for words, but I would like to know if it is possible to do something similar for curves. What I mean by curves is a curve of a function. Say I have 1000 points corresponding ...
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Select Topic Words from Clusters
I am following this solution for clustering: https://towardsdatascience.com/clustering-contextual-embeddings-for-topic-model-1fb15c45b1bd
For step four "Select Topic Words from Clusters", I ...
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Which pre-trained model to select to generate embeddings from shop names written in English?
Good afternoon!
I have a dataset with thousands of shop names written in English. Several shop names might belong to one business entity, for instance, shops with names "KFC 001", "WWW....
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Embedding: Can i use it in a time series problem?
I'm trying to do feature extraction in some discretized time series with a variable length, doing that i'm creating an RNN auto encoder. My main problem is to find a way to let the model train with ...
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Are there methods to represent entire knowledge graphs using a single 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?
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Machine learning with mixed variables dataset (numerical, categorical and embeddings)
I'm working on a machine learning project where I'm trying to predict the revenue of a movie.
My dataset contains mixed data types. There are numerical features (rating, number of votes, release year,....
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Embedding performing unexpectedly worse than multi-hot encoding
I'm pretty new to ML and have set up a Keras model with a number of categorical features of users & items to predict positive reactions.
Each feature is a list of IDs from a database - i.e. ...
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Embedding layers/entities in openAI's Hide and seek paper
I've recently come across a youtube video about openAI's hide and seek paper (https://openai.com/blog/emergent-tool-use/) and got really fascinated about the paper itself. But as I digging in the ...
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hypothesis testing for difference between groups of embedding vectors
In a machine learning application, I have two generators G₁ and G₂ that generate descriptions of some input z in the form of embedding vectors X and Y:
Xᵢ = [x₁, x₂, ...] = G₁(zᵢ)
Yⱼ = [y₁, y₂, ...] =...
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How to use a Swin Transformer with metric learning?
Using timm's implementation of Swin Transformer, how does one generate an embedding vector?
I would like to use timm's ...
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How to find top k most co-related time series among a large set of time-series
Background: I am doing a project on epidemiological time series fore-castings, in which I am required to make predictions of a disease based on several symptoms.
About the dataset: The dataset of ...
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Global threshold for clustering after training a Person-Reid neural network with triplet Loss?
Assume that I have "N" classes in my training data, I train a Person-Reid Neural Network using triplet Loss. In the inference stage I compute scores (using euclidean distance) as follows:
$$
...
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Are there applications where you don’t need positional embeddings for transformers?
Are there applications where you don’t need positional embeddings for transformers?
Applications using positional embeddings with transformers: machine translation, image classification, etc.
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Practical application of denoising autoencoders
I have been reading into autoencoders for the purpose of denoising data. In the examples i found (eg. [1, 2, 3], which are the first few google results) they have the following input/output:
Input ...
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Understand the reason of embedding and the size inside it in Pytorch
I'm very new to pytorch - taking a course in udemy.
There is something I find hard to understand and would like to get explaination about, in a bit simpler words than what I can find in the ...
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Biometrics identification with embeddings comparison and "unknown"/"other" class/label
This is a general or more conceptual questions about biometric classification models, based on deep learning neural networks.
The goal of the system is to take a set of features (e.g. voice recording, ...
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Product embeddings
Hei, I have a list of purchase baskets from customers and would like to build embeddings for the products.
For example:
BASKET1 = ['PRODUCT234', 'PRODUCT214', 'PRODUCT768']
BASKET2 = ['PRODUCT2', '...
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Is there a sensible notion of 'character embeddings'?
There are several popular word embeddings available (e.g., Fasttext and GloVe); In short, those embeddings are a tool to encode words along with a sensible notion of semantics attached to those words (...
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How are the embedding and context matrices created and updated in word embedding?
I am struggling to understand how word embedding works, especially how the embedding matrix $W$ and context matrix $W'$ are created/updated. I understand that in the Input we may have a one-hot ...
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Transformer time series classification using time2vec positional embedding
I want to use a transformer model to do classification of fixed-length time series. I was following along this tutorial using keras which uses time2vec as a positional embedding. According to the ...
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Do we perform text embedding before or after train-test splitting?
Do we perform text embedding before or after train-test splitting?
I know that for encoding variables, usually done after the split. However, I'm not sure if that's also the case for text processing?
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Dicussion for X-vectors
I am posting this question to ask some questions regarding the X-vector embedding proposed by Synder et al.
The paper is X-VECTORS: ROBUST DNN EMBEDDINGS FOR SPEAKER RECOGNITION
With reference to the ...
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What is the meaning of two embedding layers in a row?
I've noticed in one deep pre-trained textual neural network that there are two embedding layers in the beginning and I don't quite understand why there are two of them. As far as I understand (correct ...
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Graph Neural Network | How node embeddings are learned from several graphs?
I am reading paper on MEGnet which is a GNN. The objective is that we have several molecules that share same elements such as molecules $C0_2$ and $COOH$ share $C$ and $O$. Now if we learn the node ...
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How can I get the output of a Keras LSTM layer?
I want to get the output (that is a vector) of a LSTM layer of a network built in Python using Keras and that is trained to classify sentences (i.e. sequences). How can I do it ?
My attempt has been ...
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Latent space vs Embedding space | Are they same?
I am going through variational autoencoders and it is mentioned that:
continuity (two close points in the latent space should not give two
completely different contents once decoded) and completeness ...
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Categorical Variable Embedding
I have a categorical variable in my labeled dataset. I trained one-hot encoded version of it in another neural network having embedding layer with a larger labeled dataset. I have obtained the weights ...
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