---
language:
- am
- ha
- en
- es
- te
- ar
- af
license: mit
size_categories:
- 10K<n<100K
tags:
- Semantic Textual Relatedness
dataset_info:
  features:
  - name: PairID
    dtype: string
  - name: Language
    dtype: string
  - name: Sentence1
    dtype: string
  - name: Sentence2
    dtype: string
  - name: Length
    dtype: int64
  - name: Score
    dtype: float64
  splits:
  - name: train
    num_bytes: 4248215
    num_examples: 15123
  - name: dev
    num_bytes: 460985
    num_examples: 1390
  download_size: 2400795
  dataset_size: 4709200
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
---

# Dataset Card for Dataset Name

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
Each instance in the training, development, and test sets is a sentence pair. The instance is labeled with a score representing the degree of semantic textual relatedness between the two sentences. The scores can range from 0 (maximally unrelated) to 1 (maximally related). These gold label scores have been determined through manual annotation. Specifically, a comparative annotation approach was used to avoid known limitations of traditional rating scale annotation methods. This comparative annotation process (which avoids several biases of traditional rating scales) led to a high reliability of the final relatedness rankings. Further details about the task, the method of data annotation, how STR is different from semantic textual similarity, applications of semantic textual relatedness, etc. can be found in this paper.

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** [https://github.com/semantic-textual-relatedness/Semantic_Relatedness_SemEval2024/tree/main]