Questions tagged [word-embedding]
For questions about word embedding, a language modelling technique in natural language processing. Questions can concern particular methods, such as Word2Vec, GloVe, FastText, etc, or word embeddings and their use in machine learning libraries in general.
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How to get multimodal embeddings from CLIP model?
I'm hoping to use CLIP to get a single embedding for rows of multimodal (image and text) data.
Say I have the following model:
from PIL import Image
import torch
from transformers import CLIPProcessor,...
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Models for getting similarity scores between categories and keywords [closed]
I want to get a similarity score between a category like vehicles and a list of words like headphone, water, truck, and green. The goal would be for each score to be low on words outside the category ...
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Recreating Text Embeddings From An Example Dataset
I have a list of sentences, and a list of their ideal embeddings on a 25-dimensional vector. I am trying to use a neural network to generate new encodings, but I am struggling. While the model runs ...
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Is there a way to use CodeBERT to embed source code without natural language in input?
On CodeBERTS github they provide an example of using a NL-PL pair with the pretrained base model to create an embedding. I am looking to create an embedding using just source code which does not have ...
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Small corpus, want to find associations. Word2Vec?
I'm a psychologist, and I'm diving into the field of AI. I could really use some help for a project. This semester, I discovered Word2Vec and was mesmerized by its capability to find associations. So, ...
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Retreive a Metadata from the Chroma DB vector Store
I want to build a LLM application using Langchain, Ollama, RAG and Streamlit. My problem is: In streamlit application, after uploading the PDF, it takes so much time to generate and deliver the answer....
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Calculation of document word vector in python. Sum or average word2vec?
I have some questions about generating a dissimilarity matrix of a bunch of text documents using word vectors. Here I tokenise the text, remove OOV and then sum the word vectors of each word to use as ...
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Getting GloVe embeddings using gensim, triu not found in scipy.linalg
I am trying to build a sentiment analysing model, using the GloVe word embeddings...
I found multiple sources on how to import the embeddings into python, this one seemed to be the simplest...
Trying ...
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Vector Embedding using Spark for compute
I have some large parquet files of data in Iceberg (which I have stored using Spark). My objective now is to pull these down using Spark, convert them into a spark dataframe, perform vector embedding ...
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Question about encode_multi_process method of the SentenceTransformer
How can I leverage the encode_multi_process method of the SentenceTransformer class to encode a large list of sentences using multiple GPUs?
I tried using the encode_multi_process method of the ...
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Skip-Gram Model description in word2vec explanation article
In his article word2vec Parameter Learning Explained Xin Rong says (page 7):
Each output is computed using the same hidden->output matrix:
Looking into the word2vec source code I don’t see any “...
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Cannot access embeddings endpoint on vLLM hosting llama3-8b-instruct
I'm using vllm to run llama3-8b-instruct on a machine, I can access the chat endpoint, but when I access the embedding endpoint using following code I get NotFoundError: Error code: 404 - {'detail': '...
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Fasttext pre-trained model is not producing OOV word vectors when using gensim downloader
I am having A LOT of trouble when trying to use all the fasttext libraries (in Jupyter with Anaconda3 on Windows 11) that I have found so far but this question is mainly about gensim's implementation. ...
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"Deadline" error when embedding video with Google Vertex AI multimodal embedding modal
I am currently using Vertex AI's Multimodal Embedding Model (https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-multimodal-embeddings)
I was able to get the Image and Text examples ...
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How to Convert HTML to Text Suitable for Vector Embedding Models
I would like to convert html files to plain text in a manner that preserves logical structuring of the html in terms of title <h1> subtitles <h2> sub sub titles <h3> and let's not ...
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