
## MODEL DESCRIPTION

huBERT base model (cased) fine-tuned on SQuADv2 (NEW!) 

- huBert model + Tokenizer: https://huggingface.co/SZTAKI-HLT/hubert-base-cc
- Hungarian SQUADv2 dataset: Machine Translated SQuAD dataset (Google Translate API)

<p> <i> "SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.[1]" </i> </p> 

## Model in action
- Fast usage with pipelines:

```python
from transformers import pipeline
qa_pipeline = pipeline(
    "question-answering",
    model="mcsabai/huBert-fine-tuned-hungarian-squadv2",
    tokenizer="mcsabai/huBert-fine-tuned-hungarian-squadv2",
    topk = 1,
    handle_impossible_answer = True
)
predictions = qa_pipeline({
    'context': "Máté vagyok és Budapesten élek már több mint 4 éve.",
    'question': "Hol lakik Máté?"
})
print(predictions)
# output:
# {'score': 0.9892364144325256, 'start': 16, 'end': 26, 'answer': 'Budapesten'}
```
Two important parameter:
- <p> <b> topk </b> (int, optional, defaults to 1) — The number of answers to return (will be chosen by order of likelihood). Note that we return less than topk answers if there are not enough options available within the context. </p>
- <p> <b> handle_impossible_answer </b> (bool, optional, defaults to False): Whether or not we accept impossible as an answer. </p>


[1] https://rajpurkar.github.io/SQuAD-explorer/
{"language": "hu", "tags": ["question-answering", "bert"], "widget": [{"text": "Melyik foly\u00f3 szeli kett\u00e9 Budapestet?", "context": "Magyarorsz\u00e1g f\u0151v\u00e1ros\u00e1t, Budapestet a Duna foly\u00f3 szeli kett\u00e9. A XIX. sz\u00e1zadban \u00e9p\u00fclt L\u00e1nch\u00edd a dimbes-dombos budai oldalt k\u00f6ti \u00f6ssze a s\u00edk Pesttel. A V\u00e1rdomb oldal\u00e1ban fut\u00f3 sikl\u00f3val juthatunk fel a budai \u00d3v\u00e1rosba, ahol a Budapesti T\u00f6rt\u00e9neti M\u00fazeum eg\u00e9szen a r\u00f3mai id\u0151kig visszavezetve mutatja be a v\u00e1rosi \u00e9letet. A Szenth\u00e1roms\u00e1g t\u00e9r ad otthont a XIII. sz\u00e1zadi M\u00e1ty\u00e1s-templomnak \u00e9s a Hal\u00e1szb\u00e1stya l\u0151tornyainak, amelyekb\u0151l messzire ell\u00e1tva gy\u00f6ny\u00f6rk\u00f6dhet\u00fcnk a v\u00e1rosban."}, {"text": "Mivel juthatunk fel az \u00d3v\u00e1rosba?", "context": "Magyarorsz\u00e1g f\u0151v\u00e1ros\u00e1t, Budapestet a Duna foly\u00f3 szeli kett\u00e9. A XIX. sz\u00e1zadban \u00e9p\u00fclt L\u00e1nch\u00edd a dimbes-dombos budai oldalt k\u00f6ti \u00f6ssze a s\u00edk Pesttel. A V\u00e1rdomb oldal\u00e1ban fut\u00f3 sikl\u00f3val juthatunk fel a budai \u00d3v\u00e1rosba, ahol a Budapesti T\u00f6rt\u00e9neti M\u00fazeum eg\u00e9szen a r\u00f3mai id\u0151kig visszavezetve mutatja be a v\u00e1rosi \u00e9letet. A Szenth\u00e1roms\u00e1g t\u00e9r ad otthont a XIII. sz\u00e1zadi M\u00e1ty\u00e1s-templomnak \u00e9s a Hal\u00e1szb\u00e1stya l\u0151tornyainak, amelyekb\u0151l messzire ell\u00e1tva gy\u00f6ny\u00f6rk\u00f6dhet\u00fcnk a v\u00e1rosban."}]}
transformers pytorch tf bert question-answering hu endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #bert #question-answering #hu #endpoints_compatible #region-us 


## MODEL DESCRIPTION

huBERT base model (cased) fine-tuned on SQuADv2 (NEW!) 

- huBert model + Tokenizer: URL
- Hungarian SQUADv2 dataset: Machine Translated SQuAD dataset (Google Translate API)

<p> <i> "SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.[1]" </i> </p> 

## Model in action
- Fast usage with pipelines:


Two important parameter:
- <p> <b> topk </b> (int, optional, defaults to 1) — The number of answers to return (will be chosen by order of likelihood). Note that we return less than topk answers if there are not enough options available within the context. </p>
- <p> <b> handle_impossible_answer </b> (bool, optional, defaults to False): Whether or not we accept impossible as an answer. </p>


[1] URL
## MODEL DESCRIPTION

huBERT base model (cased) fine-tuned on SQuADv2 (NEW!) 

- huBert model + Tokenizer: URL
- Hungarian SQUADv2 dataset: Machine Translated SQuAD dataset (Google Translate API)

<p> <i> "SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.[1]" </i> </p> ## Model in action
- Fast usage with pipelines:


Two important parameter:
- <p> <b> topk </b> (int, optional, defaults to 1) — The number of answers to return (will be chosen by order of likelihood). Note that we return less than topk answers if there are not enough options available within the context. </p>
- <p> <b> handle_impossible_answer </b> (bool, optional, defaults to False): Whether or not we accept impossible as an answer. </p>


[1] URL
TAGS
#transformers #pytorch #tf #bert #question-answering #hu #endpoints_compatible #region-us 
 ## MODEL DESCRIPTION

huBERT base model (cased) fine-tuned on SQuADv2 (NEW!) 

- huBert model + Tokenizer: URL
- Hungarian SQUADv2 dataset: Machine Translated SQuAD dataset (Google Translate API)

<p> <i> "SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.[1]" </i> </p> ## Model in action
- Fast usage with pipelines:


Two important parameter:
- <p> <b> topk </b> (int, optional, defaults to 1) — The number of answers to return (will be chosen by order of likelihood). Note that we return less than topk answers if there are not enough options available within the context. </p>
- <p> <b> handle_impossible_answer </b> (bool, optional, defaults to False): Whether or not we accept impossible as an answer. </p>


[1] URL
passage: TAGS
#transformers #pytorch #tf #bert #question-answering #hu #endpoints_compatible #region-us 
## MODEL DESCRIPTION

huBERT base model (cased) fine-tuned on SQuADv2 (NEW!) 

- huBert model + Tokenizer: URL
- Hungarian SQUADv2 dataset: Machine Translated SQuAD dataset (Google Translate API)

<p> <i> "SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.[1]" </i> </p>## Model in action
- Fast usage with pipelines:


Two important parameter:
- <p> <b> topk </b> (int, optional, defaults to 1) — The number of answers to return (will be chosen by order of likelihood). Note that we return less than topk answers if there are not enough options available within the context. </p>
- <p> <b> handle_impossible_answer </b> (bool, optional, defaults to False): Whether or not we accept impossible as an answer. </p>


[1] URL