---
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, **including unanswerable questions**, for the task of question answering.

When the model classifies the question as unanswerable, it outputs "[CLS]". There is also a QA model available that does not try to identify unanswerable questions, [
bert-base-finnish-cased-squad1-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:** [Finnish SQuAD 2.0](https://huggingface.co/datasets/ilmariky/SQuAD_v2_fi) + Finnish partition of TyDi-QA
**Eval data:** [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-squad2-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": 55.53157042633567,
  "f1": 61.869335312255835,
  "total": 7412,
  "HasAns_exact": 51.26503525508088,
  "HasAns_f1": 61.006950090095565,
  "HasAns_total": 4822,
  "NoAns_exact": 63.47490347490348,
  "NoAns_f1": 63.47490347490348,
  "NoAns_total": 2590
}
```