Train and Infer Powerful Sentence Embeddings with AnglE | 🔥 SOTA on STS and MTEB Leaderboard
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Updated
Jan 3, 2025 - Python
Train and Infer Powerful Sentence Embeddings with AnglE | 🔥 SOTA on STS and MTEB Leaderboard
Neural network toolkit for sentence pair modeling.
PyTorch implementations of various deep learning models for paraphrase detection, semantic similarity, and textual entailment
Semantic Textual Similarity in Python
A simple implementation of paper "HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate semantic textual similarity."
Variants of Multi-Perspective Convolutional Neural Networks
Official Implementation of the ACL2024 Findings paper "Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation"
Sentence Similarity Estimator (SenSim)
Official implementation of: Tha3aroon at NSURL-2019 Task 8: Semantic Question Similarity in Arabic
Embedding Representation for Indonesian Sentences!
Similarity Learning applied to Speaker Verification and Semantic Textual Similarity
Awesome Semantic Textual Similarity: a curated list of Semantic Textual Similarity in Large Language Models and NLP
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.
Performs contextual, fully supervised text normalization
A tool for semantic textual similarity annotation
Semantic Textual Similarity on Persian Language
Natural Launguage Processing ,Text-Mining ,Natural Launguage Understanding
A system to process visual input on timed frames to produce sensible audio aid in accordance with human information processing limits, using image captioning, semantic text comparison and text-to-speech modules.
Categorizing products of an online retailer based on products’ titles using word2vec word-embedding and DBSCAN (density-based spatial clustering of applications with noise) clustering.
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