GATE-AraBert-v1

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
• 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-v1")
# 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 EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8391
spearman_cosine	0.841
pearson_manhattan	0.8277
spearman_manhattan	0.8361
pearson_euclidean	0.8274
spearman_euclidean	0.8358
pearson_dot	0.8154
spearman_dot	0.818
pearson_max	0.8391
spearman_max	0.841

Semantic Similarity

• Dataset: sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.813
spearman_cosine	0.8173
pearson_manhattan	0.8114
spearman_manhattan	0.8164
pearson_euclidean	0.8103
spearman_euclidean	0.8158
pearson_dot	0.7908
spearman_dot	0.7887
pearson_max	0.813
spearman_max	0.8173