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README.md
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---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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---
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# jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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## Usage (Sentence-Transformers)
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print(sentence_embeddings)
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```
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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned)
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## Training
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The model was trained with the parameters:
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---
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language: nl
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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- robbert
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datasets:
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- clips/mqa
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---
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# jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base). It was fine-tuned on 1,000,000 rows of Dutch FAQ question-answer pairs from [clips/mqa](https://huggingface.co/datasets/clips/mqa).
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## Usage (Sentence-Transformers)
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print(sentence_embeddings)
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```
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## Training
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The model was trained with the parameters:
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