Instructions to use aleksahet/BERTic-squad-sr-lat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aleksahet/BERTic-squad-sr-lat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="aleksahet/BERTic-squad-sr-lat")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("aleksahet/BERTic-squad-sr-lat") model = AutoModelForQuestionAnswering.from_pretrained("aleksahet/BERTic-squad-sr-lat") - Notebooks
- Google Colab
- Kaggle
BERTić-SQuAD-sr-lat
BERTić-SQuAD-sr-lat is a Question Answering neural network for Serbian. It is obtained by fine-tuning BERTić on a synthetically generated Serbian QA dataset based on SQuAD v1.1.
Usage
from transformers import pipeline
model_name = 'aleksahet/BERTic-squad-sr-lat'
pipe = pipeline('question-answering', model=model_name, tokenizer=model_name)
sample = {
'question': 'Kojim sportom se bavi Novak Đoković?',
'context': 'Novak Đoković (Beograd, 22. maj 1987) srpski je teniser. Mnogi teniski kritičari, bivši igrači i saigrači smatraju Đokovića jednim od najboljih tenisera u istoriji.'
}
res = pipe(sample)
Performance
The model vas evaluated on XQuAD samples translated to Serbian. We report the following results:
- Exact Match:
73.91% - F1:
82.97%
- Downloads last month
- 3