100+ Chinese Word Vectors 上百种预训练中文词向量
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Updated
Oct 30, 2023 - Python
100+ Chinese Word Vectors 上百种预训练中文词向量
Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
Personalizing LLM Responses
💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Retrieval and Retrieval-augmented LLMs
text2vec, text to vector. 文本向量表征工具,把文本转化为向量矩阵,实现了Word2Vec、RankBM25、Sentence-BERT、CoSENT等文本表征、文本相似度计算模型,开箱即用。
A library for transfer learning by reusing parts of TensorFlow models.
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
A python library for self-supervised learning on images.
Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).
Basic Utilities for PyTorch Natural Language Processing (NLP)
A robust, all-in-one GPT interface for Discord. ChatGPT-style conversations, image generation, AI-moderation, custom indexes/knowledgebase, youtube summarizer, and more!
[ACL 2023] One Embedder, Any Task: Instruction-Finetuned Text Embeddings
中文长文本分类、短句子分类、多标签分类、两句子相似度(Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short),字词句向量嵌入层(embeddings)和网络层(graph)构建基类,FastText,TextCNN,CharCNN,TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert, Attention, DeepMoji, HAN, 胶囊网络-CapsuleNet, Transformer-encode, Seq2seq, SWEM, LEAM, TextGCN
Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.
A fast, efficient universal vector embedding utility package.
Data augmentation for NLP, presented at EMNLP 2019
Implementation of the node2vec algorithm.
A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021 (Bianchi et al.).
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