Sentence Similarity
sentence-transformers
Safetensors
Persian
bert
feature-extraction
loss:CachedMultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use PartAI/Tooka-SBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PartAI/Tooka-SBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PartAI/Tooka-SBERT") sentences = [ "درنا از پرندگان مهاجر با پاهای بلند و گردن دراز است.", "درناها با قامتی بلند و بالهای پهن، از زیباترین پرندگان مهاجر به شمار میروند.", "درناها پرندگانی کوچک با پاهای کوتاه هستند که مهاجرت نمیکنند.", "ایران برای بار دیگر توانست به مدال طلا دست یابد." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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# SentenceTransformer
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This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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- PartAI/TookaBERT-Large
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> [!warning] **Important**
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> We recently released the next generation of this model available at [Tooka-SBERT-V2-Small](https://huggingface.co/PartAI/Tooka-SBERT-V2-Small), [Tooka-SBERT-V2-Large](https://huggingface.co/PartAI/Tooka-SBERT-V2-Large)
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# SentenceTransformer
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This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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