
# CrossEncoder for Slovene NLI
The model was trained using the [SentenceTransformers](https://sbert.net/) [CrossEncoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. <br />
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.<br />
More details and the training script are available here: [repo](https://github.com/jacinthes/slovene-nli-benchmark)

## Performance
The model achieves the following metrics:
- Test accuracy: 77.15
- Dev accuracy: 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()])
```
{"language": ["sl"], "license": "cc-by-sa-4.0", "datasets": ["cjvt/si_nli", "jacinthes/slovene_mnli_snli"]}
transformers pytorch camembert text-classification sl dataset:cjvt/si_nli dataset:jacinthes/slovene_mnli_snli license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #text-classification #sl #dataset-cjvt/si_nli #dataset-jacinthes/slovene_mnli_snli #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 


# CrossEncoder for Slovene NLI
The model was trained using the SentenceTransformers CrossEncoder class. <br />
It is based on SloBerta, a monolingual Slovene model.

## Training
This model was trained on the SI-NLI and the slovene_mnli_snli datasets.<br />
More details and the training script are available here: repo

## Performance
The model achieves the following metrics:
- Test accuracy: 77.15
- Dev accuracy: 77.51

## Usage
The model can be used for inference using the below code:

# CrossEncoder for Slovene NLI
The model was trained using the SentenceTransformers CrossEncoder class. <br />
It is based on SloBerta, a monolingual Slovene model. ## Training
This model was trained on the SI-NLI and the slovene_mnli_snli datasets.<br />
More details and the training script are available here: repo ## Performance
The model achieves the following metrics:
- Test accuracy: 77.15
- Dev accuracy: 77.51 ## Usage
The model can be used for inference using the below code:
TAGS
#transformers #pytorch #camembert #text-classification #sl #dataset-cjvt/si_nli #dataset-jacinthes/slovene_mnli_snli #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # CrossEncoder for Slovene NLI
The model was trained using the SentenceTransformers CrossEncoder class. <br />
It is based on SloBerta, a monolingual Slovene model. ## Training
This model was trained on the SI-NLI and the slovene_mnli_snli datasets.<br />
More details and the training script are available here: repo ## Performance
The model achieves the following metrics:
- Test accuracy: 77.15
- Dev accuracy: 77.51 ## Usage
The model can be used for inference using the below code:
TAGS
#transformers #pytorch #camembert #text-classification #sl #dataset-cjvt/si_nli #dataset-jacinthes/slovene_mnli_snli #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# CrossEncoder for Slovene NLI
The model was trained using the SentenceTransformers CrossEncoder class. <br />
It is based on SloBerta, a monolingual Slovene model.## Training
This model was trained on the SI-NLI and the slovene_mnli_snli datasets.<br />
More details and the training script are available here: repo## Performance
The model achieves the following metrics:
- Test accuracy: 77.15
- Dev accuracy: 77.51## Usage
The model can be used for inference using the below code: