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robbert-2022-dutch-sentence-transformers

NetherlandsForensicInstitute

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is based on KU Leuven's RobBERT model. It has been finetuned on the Paraphrase dataset, which we (machine-) translated to Dutch. The Paraphrase dataset consists of multiple datasets that consist of duo's of similar texts, for example duplicate questions on a forum. We have released the translated data that we used to train this model on our Huggingface page. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

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This is a ColBERT model finetuned from google/bert_uncased_L-2_H-128_A-2 on the msmarco-bm25 dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. This model is primarily designed for unit tests in limited compute environments such as GitHub Actions. But it does work to an extent for basic use cases. As of Sentence Transformers v6.0.0, this model loads directly as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:

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This model is an export of these GloVe-6B English Vectors (_300d_) for `staticvectors`. `staticvectors` enables running inference in Python with NumPy. This helps it maintain solid runtime performance. _This model is a quantized version of the base model. It's using 10x256 Product Quantization._

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This is a PyLate model finetuned from google/bert_uncased_L-2_H-128_A-2 on the msmarco-bm25 dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. This model is primarily designed for unit tests in limited compute environments such as GitHub Actions. But it does work to an extent for basic use cases.

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Mursit-Large-TR-Retrieval is a large-scale Turkish embedding model pre-trained entirely from scratch on Turkish-dominant corpora and fine-tuned for retrieval tasks. The model is based on ModernBERT-large architecture (403M parameters) and optimized specifically for Turkish legal domain applications. This model achieves strong performance on Turkish retrieval benchmarks with 56.87 MTEB Score and 46.56 Legal Score, ranking among the top Turkish embedding models. **Key Features:** - Pre-trained from scratch on approximately 112.7 billion tokens of Turkish-dominant corpus - Post-trained for embedding tasks using contrastive learning on MS MARCO-TR dataset - Achieves strong performance on Turkish legal retrieval benchmarks - Optimized for Turkish legal domain with custom tokenizer trained on legal documents **Model Type:** Embedding **Parameters:** 403M **Base Model:** newmindai/Mursit-Large **Architecture:** ModernBERT-large **Embedding Dimension:** 1,024 **Max Sequence Length:** 2,048 tokens

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