Questions tagged [word-embeddings]
Word embedding is the collective name for a set of language modeling and feature learning techniques in NLP where words are mapped to vectors of real numbers in a low dimensional space, relative to the vocabulary size.
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What is the problem with symmetry in GloVE motivation?
I am currently studying GloVe paper about word embeddings. link
In Section 3 The GloVe Model this model is derived from several desiderata, one of which confuses me. It is around Equation 3 which ...
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Combining Embeddings and Ontology (DAG) in Visualisation
How can I visualise a hierarchical ontology of items in embedding space, combining text embeddings with the graphical structure? (Something similar to the example below)
I have a hierarchical ...
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How do I combine multiple texts with mathematical accuracy using specific weights?
In the work I am doing right now, I have multiple (say 5, for purposes of illustration) pieces of text, (which are somewhat close in meaning, let's say for clarity). My objective is to combine these 5 ...
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Best word vector embeddings for preserving linear substructures (e.g. kind - man + woman = queen)
I'm trying to find a way to get word vector embeddings that preserve linear structures/analogies in word relationships. The most well know example of this the vector for "queen" being ...
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Word embedding is not getting better
I created a simple neural network to train the word embeddings.
I have 6 tokens only: ["apple", "banana", "lime", "red", "yellow", "green"].
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Recommendation System: Two-Tower Model Underperforming Simple Embedding Average Baseline
I'm trying to build a recommendation on a dataset of product purchases. The dataset consists of roughly 4 Amazon products that a particular user has bought (in sequence). I want to use the first 3 ...
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Intuition behind g variable calculation in the original word2vec implementation
I am trying to develop the intuition of word2vec training.
Looking into the word2vec source code, I see (for example, in skip-gram):
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Clustering method for frequency embeddings
I have, for example, the following lists of words that I want to cluster. The lists could have different lengths, and the vocabulary is $W = \{a,b,c\}$. The criteria of clustering 2 lists into a same ...
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How OpenAI embeddings work?
I was looking at the Stanford CS224N NLP with Deep Learning lecture, and in the first two videos, we are introduced to word2vec models. The high-level idea mentioned was that we have a 'big corpus' of ...
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"No sentence-transformers model found with name" on huggingface even though it exists
I am trying to use infgrad/stella-base-en-v2 on hugging to generate embeddings using langchain
The model exists on the huggingface hub
The model is listed on the MTEB leaderboard
The model has ...
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word2vec predicts the same word for all inputs
i build word2vec network with 2 linear layers from pytorch. for every word as an input i consistently train model to predict words before and after, for example: i was visiting my grandma's house, for ...
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Insights about W0rd2Vec
As per my knowledge, Word2Vec is belongs to non-contextual embedding technique. this have only semantic relationship between words.
We can implement Word2Vec, either in CBoW or skip-gram model. but i ...
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How to combine two vector embeddings into one?
I want to use OpenCLIP for generating embeddings for each slide in an array of pptx presentations.
To improve the quality of the results, I want to vectorize both slide text content and preview images....
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How can I use contextual embeddings with BERT for sentiment analysis/classification
I have a BERT model which I want to use for sentiment analysis/classification. E.g. I have some tweets that need to get a POSITIVE,NEGATIVE or NEUTRAL label. I can't understand how contextual ...
