This model is a fine-tuned version of mcsabai/huBert-fine-tuned-hungarian-squadv2 on the milqa dataset.

How to use:
```py
from transformers import pipeline
qa_pipeline = pipeline(
  "question-answering",
  model = "ZTamas/hubert-qa-milqa-impossible-long-answer",
  tokenizer = "ZTamas/hubert-qa-milqa-impossible-long-answer",
  device = 0,                      #GPU selection, -1 on CPU
  handle_impossible_answer = True,
  max_answer_len = 1000            #This can be modified, but to let the model's
                                   #answer be as long as it wants so I
                                   #decided to add a big number
  )

predictions = qa_pipeline({
    'context': context,            
    'question': question           
})

print(predictions)

```
{"language": ["hu"], "pipeline_tag": "question-answering"}
transformers pytorch bert question-answering hu endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #hu #endpoints_compatible #region-us 

This model is a fine-tuned version of mcsabai/huBert-fine-tuned-hungarian-squadv2 on the milqa dataset.

How to use:

TAGS
#transformers #pytorch #bert #question-answering #hu #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #question-answering #hu #endpoints_compatible #region-us 
