all-MiniLM-L6-v2
Hugging Face model with 5209 likes. Tags: sentence-transformers, pytorch, tf, rust, onnx, safetensors, openvino, bert, feature-extraction, sentence-similarity
Repos6
Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
repokeon/awesome-nlp:book: A curated list of resources dedicated to Natural Language Processing (NLP)
repoengineering87/llm-atlasInteractive, in-browser visualization of how a transformer language model works: tokens, attention, quantization, and sa
repoJohnSnowLabs/spark-nlpState of the Art Natural Language Processing
repox-tabdeveloping/turftopicRobust and fast topic models with sentence-transformers.
repohuggingface/transformers🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and
Papers4
Text-to-image (T2I) diffusion models often fail to faithfully render explicit textual descriptions, instead defaulting t
paperReweighting Framewise Attention in Video Transformers for Facial Expression UnderstandingUnderstanding facial expressions in videos requires modeling subtle and localized facial dynamics under unconstrained co
paperVASAE: Naming SAE Dictionary Directions with Vocabulary-Aligned AnchoringSparse autoencoders (SAEs) provide useful decompositions of Transformer residual streams, but their learned features are
paperTranslation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource LanguagesBERT models have revolutionised Natural Language Processing (NLP) through their ability to process unstructured text acr
News3
<img src="https://spectrum.ieee.org/media-library/illustration-of-a-shoulders-up-human-silhouette-with-facial-attributes
newsJ-Wash: A novel way to brainwash and customize large language models based on Anthropic's Jacobian-Lens!<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1uvq1i3/jwash_a_novel_way_to_brainwash_and_custom
newsIdentifying Interactions at Scale for LLMs<!-- twitter --> <p style="text-align: center;"> <!-- <img src="https://bair.berkeley.edu/static/blog/spex/i