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Llm2vec Sheared LLaMA Mntp Supervised
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Llm2vec Sheared LLaMA Mntp Supervised

Developed by McGill-NLP
LLM2Vec-Sheared-LLaMA-supervised is a supervised learning model based on the Sheared-LLaMA architecture, focusing on sentence similarity tasks and providing functions such as text embedding, information retrieval, and text classification.
Text Embedding EnglishOpen Source License:MIT#Multi-task text embedding#High-precision semantic retrieval#Academic text clustering
Downloads 648
Release Time : 4/4/2024

Model Overview

This model is mainly used to handle tasks related to sentence similarity, including text embedding, information retrieval, and text classification, and has demonstrated good performance in multiple datasets and tasks.

Model Features

Multi-task support
Supports a variety of natural language processing tasks, including text embedding, information retrieval, and text classification.
High performance
Has demonstrated good performance in multiple datasets and tasks, especially in classification and retrieval tasks.
Supervised learning
Trained using a supervised learning method to optimize the model's performance on specific tasks.

Model Capabilities

Text embedding
Information retrieval
Text classification
Text clustering
Text semantic similarity
Text evaluation
Text re-ranking
Feature extraction

Use Cases

Information retrieval
Question-answering system
Used to build an efficient question-answering system to improve the ability to retrieve relevant answers.
Performs well in the MTEB CQADupstack series of tasks.
Document retrieval
Used for large-scale document retrieval to quickly find relevant documents.
The MAP@10 reaches 41.919 in the MTEB ArguAna task.
Text classification
Sentiment analysis
Used to analyze the sentiment tendency of text, such as positive or negative reviews.
The accuracy reaches 82.0527 in the MTEB AmazonPolarityClassification task.
Topic classification
Used to classify text into predefined topic categories.
The accuracy reaches 40.806 in the MTEB AmazonReviewsClassification task.
Text clustering
Academic paper clustering
Used to cluster academic papers by topic.
The V-measure reaches 43.472 in the MTEB ArxivClusteringP2P task.
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