# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("jacinthes/cross-encoder-sloberta-si-nli")
model = AutoModelForSequenceClassification.from_pretrained("jacinthes/cross-encoder-sloberta-si-nli")Quick Links
CrossEncoder for Slovene NLI
The model was trained using the SentenceTransformers CrossEncoder class.
It is based on SloBerta, a monolingual Slovene model.
Training
This model was trained on the SI-NLI dataset.
More details and the training script are available here: repo
Performance
The model achieves the following metrics:
- Test accuracy: 75.95
- Dev accuracy: 75.14
Usage
The model can be used for inference using the below code:
from sentence_transformers import CrossEncoder
model = CrossEncoder('jacinthes/cross-encoder-sloberta-si-nli')
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()])
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jacinthes/cross-encoder-sloberta-si-nli")