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This is a sentence-transformers model finetuned from dbmdz/bert-base-turkish-uncased on the cleaned_turkish_embedding_model_training_data_colab dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. - **Model Type:** Sentence Transformer - **Base model:** dbmdz/bert-base-turkish-uncased - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - cleaned_turkish_embedding_model_training_data_colab - **Documentation:** Sentence Transformers Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Sentence Transformers on Hugging Face

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Re-exports of perplexity-ai/pplx-embed-v1-0.6B (revision `2c4d510d`) with **standard ONNX operators**, usable by Transformers.js / ONNX Runtime Web (WASM and WebGPU) and ONNX Runtime Node. The official ONNX files use `GatherBlockQuantized`, which ORT Web cannot load. Pooling: masked mean over `last_hidden_state` (identical to the official `pooler_output`), then L2, then Matryoshka truncation to 256 dims and L2 again, as in the Deposium stack. Measurements: 38 texts in 6 languages (10 to 218 tokens) with ONNX Runtime 1.25 CPU, plus 8 texts (up to 512 tokens) in Chrome 152 with Transformers.js 4.3.0 / ORT Web 1.31-dev on an RTX 5060 (WebGPU) and on WASM. Full report, scripts and raw results: deposium-turbov3 `docs/2026-09-17-browser-embedding-t1-weight-only-reexport.md`.

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Finnish Sentence BERT trained from FinBERT. A demo on retrieving the most similar sentences from a dataset of 400 million sentences can be found here. The same as in the HuggingFace documentation of the English Sentence Transformer. Either through `SentenceTransformer` or `HuggingFace Transformers`

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