SimCSE
Sentence embedder
An open source framework for learning sentence embeddings using contrastive learning.
[EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821
3k stars
29 watching
518 forks
Language: Python
last commit: almost 2 years agoLinked from 1 awesome list
nlpsentence-embeddings
Related projects:
| Repository | Description | Stars |
|---|---|---|
| Provides training and testing code for a CNN-based sentence embedding model | 2 | |
| An unsupervised contrastive learning framework for learning sentence embeddings sensitive to differences between original and edited sentences. | 292 | |
| Code for training universal paraphrastic sentence embeddings and models on semantic similarity tasks | 193 | |
| A toolkit for creating and using natural language prompts to enable large language models to generalize to new tasks. | 2,718 | |
| A Python implementation of a sentence embedding algorithm using the Smooth Inverse Frequency weighting scheme | 1,084 | |
| A toolkit for deploying and serving text embeddings models with high-performance inference capabilities. | 2,932 | |
| An implementation of a word embedding model that uses character n-grams and achieves state-of-the-art results in multiple NLP tasks | 803 | |
| An open-source repository containing lecture slides and course materials for an advanced natural language processing course. | 15,702 | |
| Provides pre-trained ELMo representations for multiple languages to improve NLP tasks. | 1,462 | |
| Develops unified sentence embedding models for NLP tasks | 840 | |
| Trains word embeddings from a paraphrase database to represent semantic relationships between words. | 30 | |
| An explanation of key concepts and advancements in the field of Machine Learning | 7,352 | |
| An NLP project offering various text classification models and techniques for deep learning exploration | 7,881 | |
| Industrial-strength NLP library for Python and Cython | 30,459 | |
| Transforms existing word embeddings into more interpretable ones by applying a novel extension of k-sparse autoencoder with stricter sparsity constraints | 52 |