A very simple framework for state-of-the-art Natural Language Processing (NLP)
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A blazing fast inference solution for text embeddings models
Representation learning on large graphs using stochastic graph convolutions.
Open-source inference server and production cluster for all the models your agent needs.
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
- machine-learning
- convolutional-networks
- embedding-vectors
- embeddings
Infinity is a high-throughput, low-latency serving engine for text-embeddings, reranking models, clip, clap and colpali
- llm
- text-embeddings
- bert-embeddings
The code used to train and run inference with the ColVision models, e.g. ColPali, ColQwen2, and ColSmol.
- colpali
- information-retrieval
- retrieval-augmented-generation
- vision-language-model
A Toolkit for Industrial Topic Modeling
- topic-modeling
- topic-models
- lda
- sentence-lda
This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These representations can be subsequently used in many natural language processing applications and for further research.
Automatically exported from code.google.com/p/word2vec
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
- deep-learning
- protein-sequences
- protein-structure
- semi-supervised-learning
Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.
- artificial-intelligence
- clip
- pathology
- vision-and-language