Extended Stanford Natural Language Inference

The dataset comprises several columns, including premise and hypothesis texts that are used to evaluate or infer information based on each other. Each sentence is accompanied by a label that classifies the entailment relation into one of three categories: entailment, contradiction, or neutral. Furthermore, there are three annotated explanations provided for each entailment relation to further support and clarify the relationship between the premise and hypothesis.

The validation.csv file within this dataset contains a set of examples specifically designed for validation purposes. It includes premises, hypotheses, labels (entailment classification), and three annotated explanations per entailment relation. Similarly, the train.csv file provides training data for natural language inference tasks using SNLI annotations with premises, hypotheses' texts as well as corresponding labels and multiple annotated explanations supporting their connection.

As part of this extended e-SNLI dataset package from Kaggle,you will also find test.csv file which features additional test data extracted from SNLI database containing various sentences with their contextual background(premises), statements being evaluated(hypotheses), appropriate labels categorizing their relationships(entailments), along with three detailed justifications provided as explanatory notes supporting those relationships.

Summing up important features offered by this comprehensive e-SNLI toolkit enriched with annotation assistance: An extensive range of premises generated from real-world textual data sources paired with well-established matching-oriented authorization encompassing alternate yet applicable hypothetical implications while adhering to one among Entailment/Contradiction/Neutral labeling scheme.Taking advantage of the complete dataset, you can explore nuanced understanding and analysis of entailment relations between linguistic units, emerging from various domains and contexts enhanced by multiple explanations available per relationship

How to use the dataset The Extended Stanford Natural Language Inference (e-SNLI) Dataset with Explanations is a valuable resource for researchers and practitioners working in the field of natural language processing. This dataset builds upon the existing Stanford Natural Language Inference (SNLI) Dataset by including annotated explanations for entailment relations.

Overview of the Dataset The e-SNLI dataset consists of three main files: train.csv, validation.csv, and test.csv. Each file contains a collection of examples, including premises, hypotheses, labels, and three annotated explanations for each entailment relation.

premise: This column represents the sentence or text that serves as the context or background information for the entailment relation. hypothesis: This column contains the sentence or text that is being evaluated or inferred based on the premise. label: The label column indicates whether there is an entailment relation between the premise and hypothesis. It can take one of three categories: entailment, contradiction, or neutral. explanation_1, explanation_2, explanation_3: These columns provide additional annotated explanations or reasons to support the entailment relation between the premise and hypothesis. How to Utilize this Dataset When working with this dataset, there are several steps you can follow:

Importing Data: Load one of the provided CSV files using your preferred programming language or data analysis tool to access its content. Exploring Premises and Hypotheses: Analyze both premises and hypotheses to gain an understanding of their relationship and create insights about how certain statements may lead to specific conclusions. Examining Label Distribution: Observe how labels are distributed across different examples within each file. This analysis will help you understand potential biases in data collection. Investigating Annotations: Read through the annotated explanations provided for each entailment relation. These explanations can offer valuable insights into the underlying reasoning behind each label. Consider using these annotations to build more comprehensive models or improve your existing ones. Model Training and Evaluation: Utilize this dataset to train and evaluate models for natural language inference tasks, such as text classification or sentiment analysis. Evaluate the performance of your models based on the predefined labels. Potential Applications The e-SNLI dataset can be used in various natural language processing tasks, including but not limited to:

Natural Language Inference: Develop models capable of determining if a hypothesis is entailed Research Ideas Natural Language Understanding: The e-SNLI dataset can be used to train and evaluate models for natural language understanding tasks such as textual entailment, contradiction detection, and neutral classification. With the annotated explanations provided for each entailment relation, models can learn to capture the reasoning behind the entailment decisions. Model Explainability: The dataset can also be used to investigate and analyze the explanations provided for each entailment relation. Researchers can study which features or linguistic patterns are important in determining the relationship between premises and hypotheses. Data Augmentation: The e-SNLI dataset can be utilized for data augmentation techniques in natural language processing tasks. By incorporating the additional annotated explanations into existing datasets, models can benefit from more diverse examples and potentially improve their performance on related tasks such as question answering or machine translation

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Original Data Source: Extended Stanford Natural Language Inference