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Textual Entailment Dataset

Textual Entailment Dataset with Labelled Text Pairs

By SetFit (From Huggingface) [source]

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About this dataset

> The SetFit/mnli dataset is a comprehensive collection of textual entailment data designed to facilitate the development and evaluation of models for natural language understanding tasks. This dataset includes three distinct files: validation.csv, train.csv, and test.csv, each containing valuable information for training and evaluating textual entailment models. > > In these files, users will find various columns providing important details about the text pairs. The text1 and text2 columns indicate the first and second texts in each pair respectively, allowing researchers to analyze the relationships between these texts. Additionally, the label column provides a categorical value indicating the specific relationship between text1 and text2. > > To further aid in understanding the relationships expressed by these labels, there is an accompanying label_text column that offers a human-readable representation of each categorical label. This allows practitioners to interpret and analyze the labeled data more easily. > > Moreover, all three files in this dataset contain an additional index column called idx, which assists in organizing and referencing specific samples within the dataset during analysis or model development. > > It's worth noting that this SetFit/mnli dataset has been carefully prepared for textual entailment tasks specifically. To ensure accurate evaluation of model performance on such tasks, researchers can leverage validation.csv as a dedicated set of samples specifically reserved for validating their models' performance during training. The train.csv file contains ample training data with corresponding labels that can be utilized to effectively train reliable textual entailment models. Lastly, test.csv includes test samples designed for evaluating model performance on textual entailment tasks. > > By utilizing this extensive collection of high-quality data provided by SetFit/mnli dataset, researchers can develop powerful models capable of accurately understanding natural language relationships expressed within text pairs across various domains

How to use the dataset

> > > - text1: This column contains the first text in a pair. > - text2: This column contains the second text in a pair. > - label: The label column indicates the relationship between text1 and text2 using categorical values. > - label_text: The label_text column provides the text representation of the labels. > > To effectively use this dataset for your textual entailment task, follow these steps: > > ## 1. Understanding the Columns > > Start by familiarizing yourself with the different columns present in each file of this dataset: > > - text1: The first text in a pair that needs to be evaluated for textual entailment. > - text2: The second text in a pair that needs to be compared with text1 to determine its logical relationship. > - label: This categorical field represents predefined relationships or categories between texts based on their meaning or logical inference. > - label_text: A human-readable representation of each label category that helps understand their real-world implications. > > ## 2. Data Exploration > > Before building models or applying any algorithms, it's essential to explore and understand your data thoroughly: > > - Analyze sample data points from each file (validation.csv, train.csv). > - Identify any class imbalances within different labels present in your data distribution. > > ## 3. Preprocessing Steps > > > - Handle missing values: Check if there are any missing values (NaNs) within any columns and decide how to handle them. > - Text cleaning: Depending on the nature of your task, implement appropriate text cleaning techniques like removing stop words, lowercasing, punctuation removal, etc. > - Tokenization: Break down the text into individual tokens or words to facilitate further processing steps. > > ## 4. Model Training and Evaluation > > Once your dataset is ready for modeling: > > - Split your data into training and testing sets using the train.csv and test.csv files. This division allows you to train models on a subset of data while evaluating their performance on an unseen portion. > - Utilize machine learning or deep learning algorithms suitable for textual entailment tasks (e.g., BERT

Research Ideas

> - Natural Language Understanding: The dataset can be used for training and evaluating models that perform natural language understanding tasks, such as text classification, semantic similarity, and textual entailment. > - Transfer Learning: Models trained on this dataset can be fine-tuned or used as a pre-training step for other NLP tasks, allowing for transfer learning across different domains and languages. > - Model Evaluation: Researchers and practitioners can use this dataset to compare the performance of different models or algorithms in the field of textual entailment, helping to advance the state-of-the-art in NLP

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source > >

License

> > > License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication > No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: validation.csv

Column name	Description
text1	The first text in each pair. (Text)
text2	The second text in each pair. (Text)
label	The relationship between text1 and text2. (Categorical)
label_text	The corresponding text representation of each label. (Text)

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File: train.csv

Column name	Description
text1	The first text in each pair. (Text)
text2	The second text in each pair. (Text)
label	The relationship between text1 and text2. (Categorical)
label_text	The corresponding text representation of each label. (Text)

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File: test.csv

Column name	Description
text1	The first text in each pair. (Text)
text2	The second text in each pair. (Text)
label	The relationship between text1 and text2. (Categorical)
label_text	The corresponding text representation of each label. (Text)

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit SetFit (From Huggingface).