CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
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Topic Modelling for Humans
- gensim
- topic-modeling
- information-retrieval
- machine-learning
Pretrained models for TensorFlow.js
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
- community-detection
- graph-clustering
- deepwalk
- networkx
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.
- word2vec
- text-mining
- natural-language-processing
- glove
🚀 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.
- 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
UCE is a zero-shot foundation model for single-cell gene expression data
This is a german ELMo deep contextualized word representation. It is trained on a special German Wikipedia Text Corpus.
- bilm
- elmo
- tensorflow
- machine-learning