LENS-d8000 Open-Source Text Embedding Model - Freely Perform Feature Extraction and Sentence Similarity Analysis

LENS D8000

Developed by yibinlei

LENS-8000 is a transformer-based text embedding model focused on feature extraction and sentence similarity tasks, excelling in multiple classification and retrieval tasks.

Text Embedding

Transformers

Open Source License:Apache-2.0 #High-precision text classification #Multi-task text embedding #E-commerce review analysis

Downloads 848

Release Time : 12/30/2024

Model Overview

LENS-8000 is a high-performance text embedding model primarily used for text classification, sentence similarity calculation, and feature extraction tasks. It demonstrates outstanding performance across multiple datasets in the MTEB benchmark.

Model Features

High-performance text classification

Excels in multiple text classification tasks, such as Amazon review classification and sentiment analysis.

Powerful sentence similarity calculation

Performs exceptionally well in sentence similarity tasks, accurately computing semantic similarity between texts.

Multi-task support

Supports various NLP tasks, including classification, retrieval, and feature extraction.

Model Capabilities

Text embedding

Feature extraction

Sentence similarity calculation

Text classification

Information retrieval

Use Cases

E-commerce

Product review classification

Performs sentiment analysis and classification on Amazon product reviews.

Achieves 97.07% accuracy in the AmazonPolarityClassification task.

Counterfactual review detection

Identifies counterfactual reviews on Amazon.

Achieves 93.69% accuracy in the AmazonCounterfactualClassification task.

Information retrieval

Argument retrieval

Retrieves relevant arguments in argument analysis tasks.

Scores 76.019 in the ArguAna retrieval task.

🚀 Gouzi3618/LENS-8000

This model is designed for tasks such as text embedding, feature extraction, and sentence similarity. It demonstrates excellent performance in multiple MTEB benchmarks, providing reliable solutions for various natural language processing tasks.

📚 Documentation

Model Information

Property	Details
Model Type	Gouzi3618/LENS-8000
Tags	text-embedding, feature-extraction, sentence-similarity, transformers, mteb
License	Apache-2.0

Benchmark Results

Classification Tasks

Task	Dataset	Accuracy	AP	AP Weighted	F1	F1 Weighted	Main Score
Classification	MTEB AmazonCounterfactualClassification (en)	93.6865671641791	74.44778735403261	74.44778735403261	90.57338628851295	93.87207694461506	93.6865671641791
Classification	MTEB AmazonPolarityClassification (default)	97.06832499999999	95.71019538629211	95.71019538629211	97.06781792337515	97.06781792337515	97.06832499999999
Classification	MTEB AmazonReviewsClassification (en)	63.608	-	-	62.41274991021244	62.41274991021244	63.608
Classification	MTEB Banking77Classification (default)	-	-	-	-	-	-

Retrieval Task (MTEB ArguAna)

Metric	Value
Main Score	76.019
MAP@1	55.903000000000006
MAP@10	69.887
MAP@100	70.157
MAP@1000	70.159
MAP@20	70.101
MAP@3	67.378
MAP@5	69.138
MRR@1	56.899004267425326
MRR@10	70.23428503691676
MRR@100	70.50477756895107
MRR@1000	70.5063694836776
MRR@20	70.44906432331086
MRR@3	67.73352299668105
MRR@5	69.46183025130412
...	...

Clustering Tasks

Task	Dataset	Main Score	V-Measure	V-Measure Std
Clustering	MTEB ArxivClusteringP2P (default)	54.809064728970625	54.809064728970625	14.497861425102215
Clustering	MTEB ArxivClusteringS2S (default)	50.144159631474416	50.144159631474416	14.596959041091187

Reranking Task (MTEB AskUbuntuDupQuestions)

Metric	Value
Main Score	65.74396432331054
MAP	65.74396432331054
MRR	77.89418722244206
...	...

STS Task (MTEB BIOSSES)

Metric	Value
Cosine Pearson	86.59612727828603
Cosine Spearman	85.83087137728063
Euclidean Pearson	84.64267159338176
Euclidean Spearman	85.83087137728063
Main Score	85.83087137728063
...	...

📄 License

This project is licensed under the Apache-2.0 license.

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