Upload README.md

README.md

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+---
+datasets:
+- cjvt/si_nli
+language:
+- sl
+---
+
+# CrossEncoder for Slovene NLI
+The model was trained using [SentenceTransformers](https://sbert.net/) [CrossEncoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
+It is based on [SloBerta](https://huggingface.co/EMBEDDIA/sloberta), a monolingual Slovene model.
+
+## Training
+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.
+More details and the training script are available here: [repo](https://github.com/jacinthes/slovene-nli-benchmark)
+
+## Performance
+-Accuracy on the SI-NLI validation set: 77.51
+
+## Usage
+The model can be used for inference using the below code:
+```python
+from sentence_transformers import CrossEncoder
+
+model = CrossEncoder('jacinthes/cross-encoder-sloberta-si-nli-snli-mnli')
+premise = 'Pojdi z menoj v toplice.'
+hypothesis = 'Bova lepa bova fit.'
+prediction = model.predict([premise, hypothesis])
+int2label = {0: 'entailment', 1: 'neutral', 2:'contradiction'}
+print(int2label[prediction.argmax()])
+```