Open-source alternatives to Pet

This is a TensorFlow implementation of the following paper:

This repository contains the code and pre-trained models for our paper SimCSE: Simple Contrastive Learning of Sentence Embeddings.

This repository is the official implementation of "Learn to Adapt for Generalized Zero-Shot Text Classification" (ACL 2022 main conference). The structure of our repository is based on the template https://github.com/victoresque/pytorch-template.

🌟 2022-10-06 Thrilled to present GLM-130B: An Open Bilingual Pre-trained Model. It is an open-sourced LLM outperforming GPT-3 175B over various benchmarks. Get model weights and do inference and P-Tuning with only 4 RTX 3090 or 8 RTX 2080 Ti FOR FREE!

An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks

This project is a framework for training and deploying transformer-based models that map text, images, audio, and video into dense or sparse vector representations. It functions as a multimodal embedding library and semantic search engine used to retrieve relevant documents by calculating vector similarity between meanings. The framework provides specialized tools for both cross-encoder reranking, which calculates precise similarity scores to refine search results, and vector quantization to compress embedding vectors for reduced memory usage and increased retrieval speed. The project covers

This repository provides code for the paper "tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection" (https://www.aclweb.org/anthology/2020.acl-main.630/).

The source code used for Text Classification Using Label Names Only: A Language Model Self-Training Approach, published in EMNLP 2020.

This project is a collection of educational resources and technical guides focused on the development and implementation of large language models. It provides a comprehensive curriculum covering transformer architectures, training methods, and deployment strategies. The materials provide detailed instructions for building autonomous agents using reasoning loops and tool integration, as well as guides for fine-tuning models through supervised learning and preference optimization. It also includes tutorials for constructing retrieval augmented generation pipelines and implementing transformer m