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- openai/CLIP35K
readme:
Given a batch of images, returns the image features encoded by the vision portion of the CLIP model.
- facebookresearch/fastTextarchived · 27K
readme:
The previously trained model can be used to compute word vectors for out-of-vocabulary words.
readme:
We now have numpy arrays with the embeddings, one for each text.
readme:
Efficient multicore implementations of popular algorithms, such as online **Latent Semantic Analysis (LSA/LSI/SVD)**, **Latent Dirichlet Allocation (LDA)**, **Random Projections (RP)**,
readme:
Encode text into a 512-dimensional embedding to be used as inputs to natural language processing tasks such as sentiment classification and textual similarity.
readme:
Tutorial 3: Embeddings → how to produce embeddings for words and documents
- neuml/txtai13K
readme:
Create embeddings for text, documents, audio, images and video
readme:
CLIP-as-service is a low-latency high-scalability service for embedding images and text.
readme:
Visualized-BGE can be utilized to generate embeddings for hybrid image-text data.
readme:
This project provides 100+ Chinese Word Vectors (embeddings) trained with different **representations** (dense and sparse), **context features** (word, ngram, character, and more), and **corpora**.
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