Feature Extraction
sentence-transformers
Safetensors
Transformers
Arabic
bert
mteb
sentence-similarity
Generated from Trainer
dataset_size:947818
loss:SoftmaxLoss
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Omartificial-Intelligence-Space/GATE-AraBert-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Omartificial-Intelligence-Space/GATE-AraBert-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Omartificial-Intelligence-Space/GATE-AraBert-v1") sentences = [ "امرأة تكتب شيئاً", "مراهق يتحدث إلى فتاة عبر كاميرا الإنترنت", "امرأة تقطع البصل الأخضر.", "مجموعة من كبار السن يتظاهرون حول طاولة الطعام." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Omartificial-Intelligence-Space/GATE-AraBert-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Omartificial-Intelligence-Space/GATE-AraBert-v1")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Omartificial-Intelligence-Space/GATE-AraBert-v1") model = AutoModel.from_pretrained("Omartificial-Intelligence-Space/GATE-AraBert-v1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!