Datasetpublic

Omartificial-Intelligence-Space/Arab3M-Triplets

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:… See the full description on the dataset page: https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arab3M-Triplets.

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Dataset Card

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')