Topic Modelling for Humans
- gensim
- topic-modeling
- information-retrieval
- machine-learning
47,133 repositories indexed
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Topic Modelling for Humans
A very simple framework for state-of-the-art Natural Language Processing (NLP)
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
Retrieval and Retrieval-augmented LLMs
Translated100+ Chinese Word Vectors
Source code for Twitter's Recommendation Algorithm
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
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.
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
The repository host gives no description.
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.