Instructions to use ZTamas/hubert-qa-milqa-impossible with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZTamas/hubert-qa-milqa-impossible with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ZTamas/hubert-qa-milqa-impossible")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ZTamas/hubert-qa-milqa-impossible") model = AutoModelForQuestionAnswering.from_pretrained("ZTamas/hubert-qa-milqa-impossible") - Notebooks
- Google Colab
- Kaggle
This model is a fine-tuned version of mcsabai/huBert-fine-tuned-hungarian-squadv2 on the milqa dataset.
How to use:
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model = "ZTamas/hubert-qa-milqa-impossible",
tokenizer = "ZTamas/hubert-qa-milqa-impossible",
device = 0, #GPU selection, -1 on CPU
handle_impossible_answer = True,
max_answer_len = 50
)
predictions = qa_pipeline({
'context': context,
'question': question
})
print(predictions)
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