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README.md
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library_name: transformers
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---
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# Model Card
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Question-Answer model for polish.
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Model name:** `radlab/polish-roberta-large-v2-qa`
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- **Developed by:** [radlab.dev](https://radlab.dev)
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- **Shared by:** [radlab.dev](https://radlab.dev)
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- **Model type:** QA
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- **Language(s) (NLP):** PL
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- **License:**
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- **Finetuned from model:** [sdadas/polish-roberta-large-v2](https://huggingface.co/radlab/polish-roberta-large-v2-sts)
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library_name: transformers
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---
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# Model Card
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Extractive Question-Answer model for polish. Extractive means, that the most relevant
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chunk of the text is returned as answer from t he context for the given question.
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## Model Details
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- **Model name:** `radlab/polish-roberta-large-v2-qa`
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- **Developed by:** [radlab.dev](https://radlab.dev)
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- **Shared by:** [radlab.dev](https://radlab.dev)
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- **Model type:** QA
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- **Language(s) (NLP):** PL
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- **License:** CC-BY-4.0
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- **Finetuned from model:** [sdadas/polish-roberta-large-v2](https://huggingface.co/radlab/polish-roberta-large-v2-sts)
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- **Maxiumum context size:*** 512 tokens
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## Model Usage
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Simple model usage with huggingface library:
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```python
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from transformers import pipeline
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model_path = "radlab/polish-roberta-large-v2-qa"
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question_answerer = pipeline(
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"question-answering",
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model=model_path
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)
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question = "Co będzie w budowanym obiekcie?"
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context = """Pozwolenie na budowę zostało wydane w marcu. Pierwsze prace przygotowawcze
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na terenie przy ul. Wojska Polskiego już się rozpoczęły.
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Działkę ogrodzono, pojawił się również monitoring, a także kontenery
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dla pracowników budowy. Na ten moment nie jest znana lista sklepów,
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które pojawią się w nowym pasażu handlowym."""
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print(
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question_answerer(
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question=question,
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context=context.replace("\n", " ")
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)
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)
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```
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with the sample output:
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```json
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{
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'score': 0.3472374677658081,
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'start': 259,
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'end': 268,
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'answer': ' sklepów,'
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}
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```
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