viswadarshan06/stsb-binary-paraphrase-labelled

Paraphrase Detection Dataset (Derived from SetFit/stsb)

Description:

This dataset originates from the SetFit/stsb dataset, which was initially created for semantic textual similarity (STS) tasks with a label range of 0 to 5. It has been adapted for binary paraphrase detection by leveraging the high-accuracy paraphrase classification model viswadarshan06/pd-robert.

Each sentence pair in the original dataset has been re-labeled according to the following binary scheme:

• —1 → Paraphrase: The two sentences convey the same meaning.
• —0 → Not Paraphrase: The two sentences have different meanings.

This binary labeling makes the dataset directly applicable for paraphrase detection tasks within Natural Language Processing (NLP). It is particularly useful for:

• —Training paraphrase detection models.
• —Evaluating the performance of paraphrase detection models.
• —Facilitating transfer learning for binary classification tasks related to sentence similarity.

Dataset Features:

The dataset contains the following features for each instance:

• —sentence1: The first sentence in English.
• —sentence2: The second sentence in English.
• —label: A binary label indicating whether the two sentences are paraphrases:
• —1: The sentences are paraphrases.
• —0: The sentences are not paraphrases.

Use Cases:

This dataset is suitable for a variety of NLP applications, including:

• —Paraphrase detection model training: Training new models to accurately identify paraphrases.
• —Sentence similarity tasks: Evaluating how well models can determine if two sentences have similar meanings.
• —Fine-tuning binary classification models: Adapting pre-trained binary classification models for the specific task of paraphrase detection.