Popular GitHub Repositories for Term Frequency

The Memory layer for AI Agents

Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.

InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions

About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

[ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

Profile-Based Long-Term Memory for AI Applications

List of awesome resources for machine learning-based algorithmic trading

? Must-read papers and blogs on LLM based Long Context Modeling ?

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

Visualization Toolbox for Long Short Term Memory networks (LSTMs)

Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;

Official implementation of our NeurIPS 2023 paper "Augmenting Language Models with Long-Term Memory".

[CVPR 2025?] Identity-Preserving Text-to-Video Generation by Frequency Decomposition

[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis

An AI memory layer with short- and long-term storage, semantic clustering, and optional memory decay for context-aware applications.

Claude Memory: Long-term memory for Claude

Self-supervised contrastive learning for time series via time-frequency consistency

PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).