What is DeCLUTR?
DeCLUTR is an innovative approach to learning universal sentence embeddings without the need for labeled training data. By utilizing a self-supervised objective, DeCLUTR can generate embeddings that represent the meaning of a sentence. These embeddings can then be used in many different natural language processing tasks such as machine translation or text classification.
How Does DeCLUTR Work?
DeCLUTR works by training an encoder to minimize the distance between embeddings o
Introduction to Mirror-BERT: A Simple Yet Effective Text Encoder
Language is the primary tool humans use to communicate, and it is not surprising that advancements in technology have led to great strides in natural language processing. Pretrained language models like BERT (Bidirectional Encoder Representations from Transformers) have been widely adopted and used to improve language-related tasks like language translation, sentiment analysis, and text classification. However, converting such mod
Understanding PAUSE: A Method for Learning Sentence Embeddings
The concept of learning sentence embeddings, or transforming textual data into numerical vectors, has gained significant attention in recent years due to its usefulness in a variety of natural language processing tasks. One approach to learning sentence embeddings is called PAUSE, which stands for Positive and Annealed Unlabeled Sentence Embedding. This method is based on a dual encoder schema, which is widely used in supervised sen
SimCSE: An Unsupervised Learning Framework for Generating Sentence Embeddings
SimCSE is a powerful tool for generating sentence embeddings, which are representations of sentences in a continuous vector space. These embeddings can be used in various natural language processing tasks, such as semantic search or text classification. What sets SimCSE apart is that it is an unsupervised learning framework, which means that it doesn't need labeled data to train. Instead, it uses a contrastive objecti
If you're interested in the field of natural language processing, then you've likely come across the term "Trans-Encoder" before. This topic refers to a specific technique used to distill knowledge from a pre-trained language model into itself through the use of bi- and cross-encoders.
What is Knowledge Distillation?
Before diving into the specifics of Trans-Encoders, we should first discuss what knowledge distillation is. In the field of machine learning, knowledge distillation is the proces
What is TSDAE?
TSDAE stands for "Transformer-based Sentence Denoising AutoEncoder". It is an unsupervised sentence embedding method that can be used to convert text into a fixed-size vector. During training, TSDAE encodes corrupted sentences into these vectors and then requires the decoder to reconstruct the original sentences. TSDAE's architecture is a modified version of the transformer model, which is an artificial neural network designed for natural language processing tasks.
How does TSD