ffgcc/InfoCSE-bert-base model

This model is based on bert-base-uncased pretrained model.

Model Recycling

Evaluation on 36 datasets using ffgcc/InfoCSE-bert-base as a base model yields average score of 74.28 in comparison to 72.20 by bert-base-uncased.

The model is ranked 1st among all tested models for the bert-base-uncased architecture as of 21/12/2022 Results:

20_newsgroup	ag_news	amazon_reviews_multi	anli	boolq	cb	cola	copa	dbpedia	esnli	financial_phrasebank	imdb	isear	mnli	mrpc	multirc	poem_sentiment	qnli	qqp	rotten_tomatoes	rte	sst2	sst_5bins	stsb	trec_coarse	trec_fine	tweet_ev_emoji	tweet_ev_emotion	tweet_ev_hate	tweet_ev_irony	tweet_ev_offensive	tweet_ev_sentiment	wic	wnli	wsc	yahoo_answers
82.3818	89.3333	66.34	48.2188	71.315	71.4286	83.9885	61	77.1667	90.2891	83.6	90.872	71.7731	84.3267	84.0686	58.6015	75	91.1404	90.6752	85.8349	61.7329	92.5459	54.2534	86.9799	97.2	77.2	36.82	81.14	54.1077	65.4337	85.3488	70.4982	66.9279	50.7042	63.4615	72.2

For more information, see: Model Recycling