Fast State-of-the-Art Static Embeddings
- embeddings
- machine-learning
- model2vec
- nlp
72,862 visible repositories in the index
25–37 of 37 · Most stars
Fast State-of-the-Art Static Embeddings
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
Fast vectorization, topic modeling, distances and GloVe word embeddings in R.
🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
A simple deep learning library for estimating a set of tags and extracting semantic feature vectors from given illustrations.
Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.
Learning Heterogeneous Tactile Representation for General Dexterous Manipulation
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.
UCE is a zero-shot foundation model for single-cell gene expression data
BulkFormer: A large-scale foundation model for human bulk transcriptomes
CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer
This is a german ELMo deep contextualized word representation. It is trained on a special German Wikipedia Text Corpus.