Update README.md

README.md

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 We maximized the information within the multi-sentence input reviews of the original IMDb dataset (stanfordnlp/imdb) and transformed it into a single-sentence format. This transformation aims to make benchmark studies more compatible with datasets containing single-sentence inputs (SST-2, HateSpeech, Tweet-Emotion, etc.)
 
 Tasks we performed to obtain this dataset:
-- (1) We fine-tuned the RoBERTa model (roberta-base) \cite{roberta} on the raw IMDb dataset, achieving a classification accuracy of 94.6% for sentiment analysis. We published this fine-tuned sentiment classifier on HuggingFace under EgehanEralp/roberta-base-imdb-ft for public access.
+- (1) We fine-tuned the RoBERTa model (roberta-base) on the raw IMDb dataset, achieving a classification accuracy of 94.6% for sentiment analysis. We published this fine-tuned sentiment classifier on HuggingFace under EgehanEralp/roberta-base-imdb-ft for public access.
 - (2) We split the sentences in each train and test sample of the IMDb dataset's input reviews.
 - (3) For each sentence in every review, we queried our fine-tuned RoBERTa sentiment classifier model to obtain sentiment predictions.
 - (4) For positive reviews, we selected the sentences with the highest confidence positive label predictions by the model, retaining these sentences within the original multi-sentence reviews and deleting all other sentences.