Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
English
Size:
10K - 100K
License:
| license: apache-2.0 | |
| language: | |
| - en | |
| pretty_name: imdb-single-sentence | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - stanfordnlp/imdb | |
| task_categories: | |
| - text-classification | |
| 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) 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. | |
| - (5) For negative reviews, we selected the sentences with the highest confidence negative label predictions by the model, retaining these sentences within the original multi-sentence reviews and deleting all other sentences. | |
| We created a single-sentence IMDb dataset, representing each multi-sentence review with the sentence containing the most sentiment. This strategy allowed us to select and use the sentences that best represented the sentiment of the original reviews. | |
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