readme: ---
  language: fi
  datasets:
  - SQuAD_v2_fi + Finnish partition of TyDi-QA
  license: gpl-3.0
  ---
  
  # bert-base-finnish-cased-v1 for QA 
  
  This is the [bert-base-finnish-cased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model, fine-tuned using an automatically translated [Finnish version of the SQuAD2.0 dataset](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) in combination with the Finnish partition of the [TyDi-QA](https://github.com/google-research-datasets/tydiqa) dataset. It's been trained on question-answer pairs, **excluding unanswerable questions**, for the task of question answering.
  
  Another QA model that has been fine-tuned with also unanswerable questions is also available: [bert-base-finnish-cased-squad2-fi](https://huggingface.co/ilmariky/bert-base-finnish-cased-squad1-fi).
  
  
  ## Overview
  **Language model:** bert-base-finnish-cased-v1  
  **Language:** Finnish
  **Downstream-task:** Extractive QA  
  **Training data:** Answerable questions from [Finnish SQuAD 2.0](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) + Finnish partition of TyDi-QA
  **Eval data:** Answerable questions from [Finnish SQuAD 2.0](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) + Finnish partition of TyDi-QA
  
  
  ## Usage
  
  ### In Transformers
  ```python
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
  
  model_name = "ilmariky/bert-base-finnish-cased-squad1-fi"
  
  # a) Get predictions
  nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
  QA_input = {
      'question': 'Mikä tämä on?',
      'context': 'Tämä on testi.'
  }
  res = nlp(QA_input)
  
  # b) Load model & tokenizer
  model = AutoModelForQuestionAnswering.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  ```
  
  ## Performance
  Evaluated with a slightly modified version of the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
  
  ```
  {
      "exact": 58.00497718788884,
      "f1": 69.90891092523077,
      "total": 4822,
      "HasAns_exact": 58.00497718788884,
      "HasAns_f1": 69.90891092523077,
      "HasAns_total": 4822
  }
  ```
  
  