experiments of some semantic matching models and comparison of experimental results.
- Updated
Oct 27, 2025 - Python
experiments of some semantic matching models and comparison of experimental results.
天池阿里灵杰问天引擎电商搜索算法赛非官方 baseline,又名 NLP 从入门到 22/2771。
Transformer-based models implemented in tensorflow 2.x(using keras).
文本相似度,语义向量,文本向量,text-similarity,similarity, sentence-similarity,BERT,SimCSE,BERT-Whitening,Sentence-BERT, PromCSE, SBERT
Exploring Japanese SimCSE
Implementation of "RankCSE: Unsupervised Sentence Representation Learning via Learning to Rank" (ACL 2023)
A TensorFlow 2 Keras implementation of SimCSE with unsupervised and supervised.
Extract Molecular SMILES embeddings from language models pre-trained with various objectives architectures.
A Keras-based and TensorFlow-backend NLP Models Toolkit.
Reinforcement Calibration SimCSE, combining contrastive learning, artificial potential fields, perceptual loss, and RLHF to achieve improved Semantic Textual Similarity (STS) embeddings. PyTorch-based implementations of PerceptualBERT and ForceBasedInfoNCE, along with fine-tuning capabilities via RLHF and evaluation using SentEval.
Sentence Embeddings using Deep Nerual Networks in PRODUCTION!
CRLT: A Unified Contrastive Learning Toolkit for Unsupervised Text Representation Learning
Domain adaption for an embedding model using unsupervised and supervised finetuning on scientific texts for the SciFact retrieval task.
LLM4uVis:基于大模型的历史人物不确定性推理可视分析系统 LLM4uVis是一款针对历史人物不确定性数据,可以进行多轮LLM自动推理,并对回答结果进行综合聚类和可视分析的系统。它针对时间、地点、人物和事件四类核心不确定性数据,对LLM各轮回答之间的相似推理逻辑进行语义聚类,并支持以桑基图形式展现多条推理路径及其逻辑差异,并可以选择一条或多条具体的推理链条进行进一步语义理解或横/纵向对比分析,专家可以对推理过程与结果进行交叉验证与标注。
Data pipelines for both TensorFlow and PyTorch!
This project presents a Graph Neural Network (GNN)-based framework for predicting Drug-Drug Interactions (DDIs) using pretrained SMILES embeddings and Graph Attention Networks (GAT). Given the limitations of clinical studies and traditional computational methods in detecting complex DDIs.
Text Embedding Model Fine-Tuning: Generating Data & Fine-Tuning Methods
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