Omartificial-Intelligence-Space/GATE-AraBert-v0

GATE-AraBert-v0

This is a General Arabic Text Embedding trained using SentenceTransformers in a multi-task setup. The system trains on the AllNLI and on the STS dataset.

Model Details

Model Description

• —Model Type: Sentence Transformer
• —Base model: Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2 <!-- at revision 5ce4f80f3ede26de623d6ac10681399dba5c684a -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 tokens
• —Similarity Function: Cosine Similarity
• —Training Datasets:
• —all-nli
• —sts
• —Language: ar

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Omartificial-Intelligence-Space/GATE-AraBert-v0")
# Run inference
sentences = [
    'الكلب البني مستلقي على جانبه على سجادة بيج، مع جسم أخضر في المقدمة.',
    'لقد مات الكلب',
    'شخص طويل القامة',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

• —Dataset: sts-dev
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code>

Semantic Similarity

• —Dataset: sts-test
• —Evaluated with <code>EmbeddingSimilarityEvaluator</code>