1.笔者发布了全链路的RAG检索微调代码库:RAG-Retrieval,支持微调任意开源的RAG检索模型。 2.支持全链路的RAG检索模型微调:包括向量(embedding、图a)、迟交互式模型(colbert、图d)、交互式模型(cross encoder、图c)。 3.支持任意的开源RAG检索模型微调,例如bge(bge-embedding,bge-reranker,bge-m3)模型,bce(bce-em...
RAG-Retrieval 提供了全链路的RAG检索微调(train)和推理(infer)代码。 对于微调,支持微调任意开源的RAG检索模型,包括向量(embedding、图a)、迟交互式模型(colbert、图d)、交互式模型(cross encoder、图c)。 对于推理,RAG-Retrieval专注于排序(reranker),开发了一个轻量级的python库rag-retrieval,提供统一的方式调用任...
Like decoders, encoder models take in a sequence of tokens. But instead of generating tokens, they generate an embedding vector that represents the value of the sequence based on the task the model has been trained for. For example, inretrieval-augmented generation(RAG), encoders are used to...
This study explores the impact of diverse pre-training objectives on the performance of encoder-decoder pre-trained language models across generation and question answering tasks, with a focus on commonsense knowledge retrieval and completion. We highlight the benefits of incorporating multiple objectives...
RAG-Retrieval 使用统一的方式推理不同的RAG Reranker模型|微调全链路的RAG检索模型|实验结果|License RAG-Retrieval 提供了全链路的RAG检索微调(train)和推理(infer)代码。对于微调,支持微调任意开源的RAG检索模型,包括向量(embedding、图a)、迟交互式模型(colbert、图d)、交互式模型(cross encoder、图c)。对于推理...
This is the component that encodes a sentence into fixed-length 512-dimension embedding. In the paper, there are two architectures proposed based on trade-offs in accuracy vs inference speed. Variant 1: Transformer Encoder In this variant, we use the encoder part of the original transformer arch...
Finally, the unmasked tokens are fed into a video autoencoder (Φenc, Φdec) for reconstructing the masked pixels. Specif- ically, VideoMAE is composed of three core components: cube embedding, encoder, and decoder. First, cube embed- ding encodes the local spatio...
Abstract Applying conventional autoencoders for textual data often results in learning trivial and redundant representations due to high text dimensionality, sparsity, and following power-law word distribution. To address these challenges, we propose two novel autoencoders, SCAT (Second Chance Autoencoder...
Given any task spe- cific downstream dataset Ddown = {vj, yj}Nj=1, we deploy linear fully-connected (FC) layers with embedding parame- ters θ to align the latent space to the downstream task spe- cific label space on top of encoder module FϕE . For ...
BERT: bidirectional encoder representations from transformers; C: dTrm-dimensional representation; [CLS]: classifier token; E: demb-dimensional embedding; T: dTrm-dimensional vector; Trm: bidirectional transformer. After a few layers of bidirectional transformers, Trm, each word piece, wn, corresponds...