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
model = SentenceTransformer("Omartificial-Intelligence-Space/GATE-AraBert-v0")
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)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
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 |