ELECTRA-BASE-DISCRIMINATOR finetuned on SQuADv1

This is electra-base-discriminator model finetuned on SQuADv1 dataset for for question answering task.

Model details

As mentioned in the original paper: ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a GAN. At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the SQuAD 2.0 dataset.

Model training

This model was trained on google colab v100 GPU. You can find the fine-tuning colab here .

Results

The results are actually slightly better than given in the paper. In the paper the authors mentioned that electra-base achieves 84.5 EM and 90.8 F1

Model in Action 🚀

from transformers import pipeline

nlp = pipeline('question-answering', model='valhalla/electra-base-discriminator-finetuned_squadv1')
nlp({
    'question': 'What is the answer to everything ?',
    'context': '42 is the answer to life the universe and everything'
})
=> {'answer': '42', 'end': 2, 'score': 0.981274963050339, 'start': 0}

Created with ❤️ by Suraj Patil