STS Benchmark comprises a selection of the English datasets used in the STS tasks organized in the context of SemEval between 2012 and 2017. The selection of datasets include text from image captions, news headlines and user forums.

Last update: Nov 05, 2021

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Overview

stsb_multi_mt_en

STS Benchmark comprises a selection of the English datasets used in the STS tasks organized in the context of SemEval between 2012 and 2017. The selection of datasets include text from image captions, news headlines and user forums. (source)

How to use this model from the sentence transformers library:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased")

Find the evaluation results here

Owner

Abhijith Neil Abraham

AI Research Engineer| NLP|Deep Learning

GitHub Repository

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STS Benchmark comprises a selection of the English datasets used in the STS tasks organized in the context of SemEval between 2012 and 2017. The selection of datasets include text from image captions, news headlines and user forums.

Related tags

Overview

stsb_multi_mt_en

Owner

Abhijith Neil Abraham

Under the hood working of transformers, fine-tuning GPT-3 models, DeBERTa, vision models, and the start of Metaverse, using a variety of NLP platforms: Hugging Face, OpenAI API, Trax, and AllenNLP

An attempt to map the areas with active conflict in Ukraine using open source twitter data.

:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.

Code for the paper "Language Models are Unsupervised Multitask Learners"

I label phrases on a scale of five values: negative, somewhat negative, neutral, somewhat positive, positive

SEJE is a prototype for the paper Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering.

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