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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

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

Metric Value
pearson_cosine 0.8384
spearman_cosine 0.8389
pearson_manhattan 0.8248
spearman_manhattan 0.8329
pearson_euclidean 0.825
spearman_euclidean 0.8337
pearson_dot 0.8072
spearman_dot 0.8098
pearson_max 0.8384
spearman_max 0.8389

Semantic Similarity

Metric Value
pearson_cosine 0.7908
spearman_cosine 0.7893
pearson_manhattan 0.7923
spearman_manhattan 0.7947
pearson_euclidean 0.7904
spearman_euclidean 0.7934
pearson_dot 0.7404
spearman_dot 0.7354
pearson_max 0.7923
spearman_max 0.7947
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Evaluation results