Sentence Similarity
sentence-transformers
PyTorch
Turkish
turkish-sentence-encoder
sentence-embeddings
feature-extraction
turkish
contrastive-learning
mteb
Eval Results (legacy)
Instructions to use Basar2004/turkish-sentence-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Basar2004/turkish-sentence-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Basar2004/turkish-sentence-encoder") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files
mteb_results/no_model_name_available/no_revision_available/model_meta.json
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{"name": "no_model_name_available", "revision": "no_revision_available", "release_date": null, "languages": null, "n_parameters": null, "memory_usage_mb": null, "max_tokens": null, "embed_dim": null, "license": null, "open_weights": true, "public_training_code": null, "public_training_data": null, "framework": [], "reference": null, "similarity_fn_name": null, "use_instructions": null, "training_datasets": null, "adapted_from": null, "superseded_by": null, "is_cross_encoder": null, "modalities": ["text"], "loader": null}
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mteb_results/results_summary.json
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{
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"repo_name": "Basar2004/turkish-sentence-encoder",
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"timestamp": "2026-01-19T19:53:30.406145",
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"tasks": [
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"MassiveIntentClassification",
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"MassiveScenarioClassification",
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"STS22"
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]
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}
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