README.md

---
license: apache-2.0
extra_gated_fields:
  Name: text
  Affilation: text
  Company: text
  Country: country
  Specific date: date_picker
  I want to use this dataset for:
    type: select
    options:
    - Research
    - Education
    - label: Other
      value: other
  I agree to use this dataset for non-commercial use ONLY: checkbox
task_categories:
- sentence-similarity
language:
- ar
tags:
- STS
- Embeddings
- Arabic
pretty_name: Arab3M-Triplets
size_categories:
- 1M<n<10M
---

# Arab3M-Triplets

This dataset is designed for training and evaluating models using contrastive learning techniques, particularly in the context of natural language understanding. The dataset consists of triplets: an anchor sentence, a positive sentence, and a negative sentence. The goal is to encourage models to learn meaningful representations by distinguishing between semantically similar and dissimilar sentences.

## Dataset Overview

- **Format**: Parquet
- **Number of rows**: 3.03 million
- **Columns**:
  - `anchor`: A sentence serving as the reference point.
  - `positive`: A sentence that is semantically similar to the `anchor`.
  - `negative`: A sentence that is semantically dissimilar to the `anchor`.

## Usage

This dataset can be used to train models for various NLP tasks, including:

- **Sentence Similarity**: Training models to identify sentences with similar meanings.
- **Contrastive Learning**: Teaching models to differentiate between semantically related and unrelated sentences.
- **Representation Learning**: Developing models that learn robust sentence embeddings.

### Loading the Dataset

You can load the dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset
dataset = load_dataset('Omartificial-Intelligence-Space/Arab3M-Triplets')
```