ilmariky/bert-base-finnish-cased-squad2-fi

bert-base-finnish-cased-v1 for QA

This is the bert-base-finnish-cased-v1 model, fine-tuned using an automatically translated Finnish version of the SQuAD2.0 dataset in combination with the Finnish partition of the TyDi-QA 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 .

Overview

Language model: bert-base-finnish-cased-v1
Language: Finnish Downstream-task: Extractive QA
Training data: Finnish SQuAD 2.0 + Finnish partition of TyDi-QA Eval data: Finnish SQuAD 2.0 + Finnish partition of TyDi-QA

Usage

In Transformers

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.

{
  "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
}