Instructions to use lrex93497/bert_qa_pt_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lrex93497/bert_qa_pt_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="lrex93497/bert_qa_pt_3")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("lrex93497/bert_qa_pt_3") model = AutoModelForQuestionAnswering.from_pretrained("lrex93497/bert_qa_pt_3") - Notebooks
- Google Colab
- Kaggle
This is a model for bert QA to answer question for SQuAD 2.0. It trained on SQuAD 2.0 train dataset, epoch 4 (start from 1). It itself has no ability to answer unawserable question.
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 QA alone only have F1: 32.7591, EM:24.9474
If including answerable question only, this QA alone have F1: 62.0359, EM: 46.3900
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