Instructions to use timkmecl/sloberta-esnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use timkmecl/sloberta-esnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="timkmecl/sloberta-esnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("timkmecl/sloberta-esnli") model = AutoModelForSequenceClassification.from_pretrained("timkmecl/sloberta-esnli") - Notebooks
- Google Colab
- Kaggle
SloBERTa model, fine-tuned for natural language inference on 50,000 samples from ESNLI dataset, machine translated to Slovene.
Usage
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("timkmecl/sloberta-esnli")
model = AutoModelForMaskedLM.from_pretrained("timkmecl/sloberta-esnli")
Expected inputs are of the form
Premisa: {premise}
Hipoteza: {hypothesis}
with strings {premise} and {hypothesis} being replaced with premise and hypothesis in Slovene.
Class 0 is entailment, class 1 neutral and 2 contradiction.
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