Search - metacurate.io

Glint-Research/Glint-Embed · Hugging Face

Glint-Embed is a compact text embedding model for retrieval, with strong CPU performance and hybrid scoring advantages. It provides a self-contained quick-start, benchmark results, and guidance on when to use the smaller Glint-Embed-Flash or larger configurations, including a two-stage RAG workflow and dimension trade-offs.

6 mins read Listed 27 days ago in reddit/huggingface/Four models. One lab. 12 days.

flowingdata.com Tutorial

Primer for geospatial embeddings – FlowingData

The document explains embeddings as numerical vectors in a high-dimensional latent space, highlighting the challenge for humans to interpret them and the ease for computers. It notes that geospatial data use may serve as a helpful guide, with crochet-themed illustrations to aid intuition.

1 min read Listed 22 days ago in RSS: FlowingData

docs.lightly.ai Tutorial

Understand Image and Video Embeddings in LightlyStudio - LightlyStudio

LightlyStudio automatically computes and reuses embeddings for images, videos, and objects, enabling search and embedding plots; users can disable embedding for faster imports, and videos can embed both whole videos and frames unless configured otherwise.

12 mins read Listed 19 days ago in reddit/computervision/Text axes for embedding plots: visualize your dataset by concepts like “night ↔ day” (oss, apache 2.0)

huggingface.co Documentation

perplexity-ai/pplx-embed-v2-context-9b · Hugging Face

The document describes the perplexity-ai pplx-embed-v2-context-9b model, a multimodal embedding system for text, images, and visual documents. It explains contextual vs. non-contextual embedding, cross-model compatibility, image chunking for retrieval, and practical encoding methods, with notes on setup and requirements.

4 mins read Listed 11 days ago in Bluesky

huggingface.co Documentation

Glint-Research/Glint-Embed-Flash · Hugging Face

A compact 1.07M-parameter text-embedding model for CPU-based retrieval, with a small 4.3 MB footprint and fast encoding. It performs well on SciQ with hybrid scoring, trades off SQuAD performance due to lack of attention, and offers a larger sibling for higher accuracy; setup and usage are provided via a self-contained Python inference workflow.

5 mins read Listed 27 days ago in reddit/huggingface/Four models. One lab. 12 days.

huggingface.co Tools and repositories

facebook/meta-encoder · Hugging Face

MetaEncoder-v1 matches natural-language tasks with text, image, or video candidates, and supports cached candidate representations for efficient search. The Hugging Face page includes benchmark results, setup instructions, and Python examples.

5 mins read Listed 2 days ago in X

arxiv.org Scientific research

[2611.13612] Earth Embeddings in Environmental Remote Sensing

The paper reviews Earth embeddings and their use in environmental remote sensing, then tests them on four downstream tasks. It finds that embeddings can provide stable information across space and time but do not always outperform conventional imagery or hand-crafted features.

2 mins read Listed 3 days ago in Bluesky

arxiv.org Tutorial

[2611.07298] Reducing Representational Interference in Retrieval Embeddings with Trainable Prefix Tokens

The document introduces a method to improve retrieval embeddings by adding trainable [DUMP] prefix tokens during training, without changing model architecture or loss. These tokens interact with regular tokens to reduce uninformative signals, yielding improved retrieval performance and altered attention for low-information tokens.

1 min read Listed 9 days ago in Hugging Face Daily Papers

arxiv.org Scientific research

[2606.23475] Multi-Vector Embeddings are Provably More Expressive than Single Vector Embeddings

The paper proves MV embeddings can outperform SV embeddings in expressiveness, showing a strong separation in required representation size and providing a hard-instance construction via the Pattern Matrix Method that encodes NAND_k in Chamfer similarity.

2 mins read Listed 15 days ago in reddit/learnmachinelearning/What research papers is your code built on?

github.com Documentation

GitHub - spinal-labs/spinal-privacy · GitHub

The document explains a privacy-preserving inference approach where a client transforms text into vectors via a learned embedding and a small network, then sends transformed vectors to a host running a frozen Qwen model; results show how transformation strength affects accuracy and token hiding, with setup, training, and evaluation details.

4 mins read Listed 19 days ago in reddit/LocalLLaMA/Qwen3.8-Flash-Next tried to send diagnostic file to a remote location, while stating otherwies.