Datasets:
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---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: langs
dtype: string
splits:
- name: s1_ar_ar
num_bytes: 2368220
num_examples: 11512
- name: s2_en_en
num_bytes: 1615474
num_examples: 11512
- name: s3_multilingual_1
num_bytes: 1917019
num_examples: 5756
- name: s4_multilingual_2
num_bytes: 1917019
num_examples: 5756
download_size: 3993518
dataset_size: 7817732
configs:
- config_name: default
data_files:
- split: s1_ar_ar
path: data/s1_ar_ar-*
- split: s2_en_en
path: data/s2_en_en-*
- split: s3_multilingual_1
path: data/s3_multilingual_1-*
- split: s4_multilingual_2
path: data/s4_multilingual_2-*
license: apache-2.0
task_categories:
- sentence-similarity
language:
- ar
- en
size_categories:
- 10K<n<100K
---
# SILMA STS Arabic/English Dataset - v1.0
## Overview
The **SILMA STS Arabic/English Dataset - v1.0** is a dataset designed for training and evaluating sentence embeddings for Arabic and English tasks. It consists of five different splits that cover monolingual and multilingual sentence pairs, with human-annotated similarity scores. The dataset includes both Arabic-to-Arabic and English-to-English pairs, as well as cross-lingual Arabic-English pairs, making it a valuable resource for multilingual and cross-lingual semantic similarity tasks.
## Dataset Structure
The dataset is divided into five splits, each containing sentence pairs and similarity scores.
### Split 1: ar_ar
- **Description:** Contains Arabic-to-Arabic sentence pairs with similarity scores.
- **Size:** 11,512 examples
- **JSON Sample:**
```json
{
"sentence1": "رجلين يلعبان الشطرنج",
"sentence2": "ثلاثة رجال يلعبون الشطرنج",
"score": 0.52,
"langs": "ar-ar"
}
### Split 2: en_en
- **Description:** Contains English-to-English sentence pairs with similarity scores.
- **Size:** 11,512 examples
- **JSON Sample:**
```json
{
"sentence1": "A plane is taking off.",
"sentence2": "An air plane is taking off.",
"score": 1.0
}
### Split 3: multilingual_1
- **Description:** Contains sentence pairs from both Arabic and English, with similarity scores. The sentences are aligned cross-lingually.
- **Size:** 5,756 examples
- **JSON Sample:**
```json
{
"sentence1": "The man is playing the guitar. | الرجل يعزف على الغيتار",
"sentence2": "The man is playing the piano. | الرجل يعزف على البيانو",
"score": 0.32
}
### Split 4: multilingual_2
- **Description:** Similar to Split 3, but with reversed language pairs.
- **Size:** 5,756 examples
- **JSON Sample:**
```json
{
"sentence1": "رجل يدخن | A man is smoking.",
"sentence2": "رجل يتزلج | A man is skating.",
"score": 0.1
}
## Column Descriptions
Each split in the dataset contains the following columns:
- *sentence1:* The first sentence in the pair. It can be in Arabic or English depending on the split.
- *sentence2:* The second sentence in the pair. It can also be in Arabic or English depending on the split.
- *score:* A floating-point number between 0 and 1 representing the semantic similarity between the two sentences, where 1 indicates maximum similarity.
- *langs:* Indicates the language pair of the sentences. The possible values are:
- ar-ar (Arabic-Arabic)
- en-en (English-English)
- Multilingual-1 (Multilingual, English-Arabic)
- Multilingual-2 (Multilingual, Arabic-English)
## Use Cases
The **SILMA STS Arabic/English Dataset - v1.0** can be used in various NLP tasks, including but not limited to:
1. **Sentence Embedding Training:** The dataset is well-suited for training models that generate sentence embeddings, enabling effective comparison of sentence-level semantics in both Arabic and English.
2. **Multilingual and Cross-Lingual STS:** This dataset can be used for evaluating the performance of multilingual and cross-lingual sentence transformers, as it includes both monolingual and multilingual sentence pairs.
3. **Semantic Similarity Tasks:** The dataset can be utilized in semantic similarity benchmarks, particularly for Arabic and English language pairs.
4. **Cross-Lingual Transfer Learning:** The multilingual sentence pairs provide a good opportunity for training models in cross-lingual transfer learning, where knowledge from one language can be transferred to another.
This dataset is a useful resource for researchers and developers working on NLP tasks that involve sentence semantics across different languages, especially for Arabic and English.
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