Instructions to use lrex93497/bert_qa_classifier_pt_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lrex93497/bert_qa_classifier_pt_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lrex93497/bert_qa_classifier_pt_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lrex93497/bert_qa_classifier_pt_3") model = AutoModelForSequenceClassification.from_pretrained("lrex93497/bert_qa_classifier_pt_3") - Notebooks
- Google Colab
- Kaggle
This is a model for bert QA to classify answerable/unanswerable question for SQuAD 2.0. It trained on SQuAD 2.0 train dataset, epoch 4 (start from 1).
For details please see https://github.com/lrex93497/fine-tune-BERT-base-uncased-QA-SQuAD2.0/
In our system, we achieved Exact Match (EM): 53.0784, f1: 59.1615
This classifier model can have 72.6899% on distinguishing answerable and unawserable questions.
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