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# CrossEncoder for Slovene NLI
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The model was trained using [SentenceTransformers](https://sbert.net/) [CrossEncoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
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It is based on [SloBerta](https://huggingface.co/EMBEDDIA/sloberta), a monolingual Slovene model.
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## Training
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This model was trained on the [SI-NLI](https://huggingface.co/datasets/cjvt/si_nli) and the [slovene_mnli_snli](https://huggingface.co/datasets/jacinthes/slovene_mnli_snli) datasets
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More details and the training script are available here: [repo](https://github.com/jacinthes/slovene-nli-benchmark)
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## Performance
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-Accuracy on the SI-NLI validation set: 77.51
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## Usage
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The model can be used for inference using the below code:
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# CrossEncoder for Slovene NLI
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The model was trained using the [SentenceTransformers](https://sbert.net/) [CrossEncoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. <br />
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It is based on [SloBerta](https://huggingface.co/EMBEDDIA/sloberta), a monolingual Slovene model.
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## Training
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This model was trained on the [SI-NLI](https://huggingface.co/datasets/cjvt/si_nli) and the [slovene_mnli_snli](https://huggingface.co/datasets/jacinthes/slovene_mnli_snli) datasets.<br />
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More details and the training script are available here: [repo](https://github.com/jacinthes/slovene-nli-benchmark)
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## Performance
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- Accuracy on the SI-NLI validation set: 77.51
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## Usage
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The model can be used for inference using the below code:
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