all-mpnet-base-v2-negation Open-Source Sentence Embedding Model - Precise Processing of Negative Sentences to Aid Similarity Calculation

All Mpnet Base V2 Negation

Developed by dmlls

This is a sentence embedding model based on the MPNet architecture, specifically optimized for handling negation sentences, suitable for sentence similarity calculation tasks.

Text Embedding

Safetensors

EnglishOpen Source License:Apache-2.0 #Negation Handling Optimization #Multi-domain Sentence Similarity #High-precision Semantic Matching

Downloads 4,996

Release Time : 4/7/2023

Model Overview

This model is a sentence transformer primarily used for feature extraction and sentence similarity calculation, with particular expertise in processing sentences containing negation words.

Model Features

Negation Handling Optimization

Specifically optimized for negation sentences, capable of accurately identifying and processing sentences containing negation words.

Multi-task Training

Trained on multiple datasets including s2orc, stackexchange, ms_marco, etc., enhancing the model's generalization capability.

High Performance

Outstanding performance in multiple benchmark tests, especially in sentence similarity and classification tasks.

Model Capabilities

Sentence Similarity Calculation

Feature Extraction

Text Classification

Cluster Analysis

Use Cases

Text Analysis

Q&A Systems

Used to calculate the similarity between questions and candidate answers, improving the accuracy of Q&A systems.

Achieved an average precision of 65.57 in the MTEB AskUbuntu duplicate question task.

Sentiment Analysis

Identifies emotional tendencies in text, particularly capable of accurately processing emotional expressions containing negation words.

Achieved an accuracy of 45.63 in the MTEB sentiment classification task.

Information Retrieval

Document Clustering

Clusters semantically similar documents or sentences together for information organization and retrieval.

Achieved a V-measure of 45.73 in the MTEB arXiv clustering task.

🚀 all-mpnet-base-v2-negation

This is a model for sentence similarity tasks. It can effectively measure the similarity between sentences and has been tested on multiple datasets with various tasks, showing good performance.

✨ Features

Sentence Similarity: Specifically designed for measuring the similarity between sentences.
Multiple Datasets: Trained on a wide range of datasets, including s2orc, ms_marco, and many others.
Diverse Tasks: Tested on various tasks such as Classification, Clustering, Reranking, and STS.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Model Information

Pipeline Tag: sentence - similarity
Inference: Enabled
Tags: sentence - transformers, feature - extraction, sentence - similarity
Language: English
License: Apache - 2.0

Datasets

The model is trained on the following datasets:

s2orc
flax - sentence - embeddings/stackexchange_xml
ms_marco
gooaq
yahoo_answers_topics
code_search_net
search_qa
eli5
snli
multi_nli
wikihow
natural_questions
trivia_qa
embedding - data/sentence - compression
embedding - data/flickr30k - captions
embedding - data/altlex
embedding - data/simple - wiki
embedding - data/QQP
embedding - data/SPECTER
embedding - data/PAQ_pairs
embedding - data/WikiAnswers
tum - nlp/cannot - dataset

Model Index

The model named all - mpnet - base - v2 - negation has the following results on different tasks and datasets:

Task Type	Dataset	Accuracy	AP	F1	MAP	MRR	V - Measure	Cos Sim Pearson	Cos Sim Spearman	Euclidean Pearson	Euclidean Spearman	Manhattan Pearson	Manhattan Spearman
Classification	MTEB AmazonCounterfactualClassification (en)	72.6268656716418	36.40585820220466	67.06383995428979	-	-	-	-	-	-	-	-	-
Classification	MTEB AmazonPolarityClassification	85.11834999999999	79.72843246428603	85.08938287851875	-	-	-	-	-	-	-	-	-
Classification	MTEB AmazonReviewsClassification (en)	37.788000000000004	-	37.40475118737949	-	-	-	-	-	-	-	-	-
Clustering	MTEB ArxivClusteringP2P	-	-	-	-	-	45.73138953773995	-	-	-	-	-	-
Clustering	MTEB ArxivClusteringS2S	-	-	-	-	-	39.13609863309245	-	-	-	-	-	-
Reranking	MTEB AskUbuntuDupQuestions	-	-	-	65.56639026991134	77.8122938926263	-	-	-	-	-	-	-
STS	MTEB BIOSSES	-	-	-	-	-	-	72.27098152643569	71.13475338373253	70.48545151074218	69.49917394727082	69.2653740752147	68.59192435931085
Classification	MTEB Banking77Classification	84.7012987012987	-	84.61766470772943	-	-	-	-	-	-	-	-	-
Clustering	MTEB BiorxivClusteringP2P	-	-	-	-	-	37.61314886948818	-	-	-	-	-	-
Clustering	MTEB BiorxivClusteringS2S	-	-	-	-	-	34.496442588205205	-	-	-	-	-	-
Classification	MTEB EmotionClassification	45.63	-	40.24119129248194	-	-	-	-	-	-	-	-	-
Classification	MTEB ImdbClassification	74.73479999999999	68.80435332319863	74.66014345440416	-	-	-	-	-	-	-	-	-
Classification	MTEB MTOPDomainClassification (en)	93.06429548563612	-	92.91686969560733	-	-	-	-	-	-	-	-	-
Classification	MTEB MTOPIntentClassification (en)	78.19197446420428	-	61.50020940946492	-	-	-	-	-	-	-	-	-
Classification	MTEB MassiveIntentClassification (en)	73.86684599865502	-	72.11245795864379	-	-	-	-	-	-	-	-	-
Classification	MTEB MassiveScenarioClassification (en)	77.53866845998655	-	77.51746806908895	-	-	-	-	-	-	-	-	-
Clustering	MTEB MedrxivClusteringP2P	-	-	-	-	-	33.66744884855605	-	-	-	-	-	-
Clustering	MTEB MedrxivClusteringS2S	-	-	-	-	-	31.951900966550262	-	-	-	-	-	-
Reranking	MTEB MindSmallReranking	-	-	-	29.34485636178124	30.118035109577022	-	-	-	-	-	-	-
Clustering	MTEB RedditClustering	-	-	-	-	-	47.14306531904168	-	-	-	-	-	-
Clustering	MTEB RedditClusteringP2P	-	-	-	-	-	51.59878183893005	-	-	-	-	-	-
STS	MTEB SICK - R	-	-	-	-	-	-	78.5530506834234	77.45787185404667	76.37727601604011	77.14250754925013	75.85855462882735	76.6223895689777
STS	MTEB STS12	-	-	-	-	-	-	83.1019526956277	72.98362332123834	78.42992808997602	70.79569301491145	77.96413528436207	70.34707852104586
STS	MTEB STS13	-	-	-	-	-	-	85.09200805966644	85.52497834636847	84.20407512505086	85.35640946044332	83.79425758102826	84.9531731481683
STS	MTEB STS14	-	-	-	-	-	-	82.43419245577238	79.87215923164575	80.99628882719712	79.2671186335978	80.47076166661054	78.82329686631051
STS	MTEB STS15	-	-	-	-	-	-	84.67294508915346	85.34528695616378	83.65270617275111	84.64456096952591	83.26416114783083	84.26944094512996
STS	MTEB STS16	-	-	-	-	-	-	80.70172607906416	81.96031310316046	82.34820192315314	82.72576940549405	81.93093910116202	82.25431799152639
STS	MTEB STS17 (en - en)	-	-	-	-	-	-	90.43640731744911	90.16343998541602	89.49834342254633	90.17304989919288	89.32424382015218	89.91884845996768
STS	MTEB STS22 (en)	-	-	-	-	-	-	62.06205206393254	60.920792876665885	60.49188637403393	60.73500415357452	59.94692152491976	60.215426858338994

🔧 Technical Details

No technical details are provided in the original document, so this section is skipped.

📄 License

The model is released under the Apache - 2.0 license.

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