This is an uncased question answering model with a BERT Base encoder finetuned on dataset SQuADv2.0 . With Question Answering, or Reading Comprehension, given a question and a passage of content (context) that may contain an answer for the question, the model predicts the span within the text with a start and end position indicating the answer to the question.
Trained or fine-tuned NeMo models (with the file extenstion
.nemo) can be converted to Riva models (with the file extension
.riva) and then deployed. Here is a pre-trained Riva question answering model with a BERT Base encoder finetuned on dataset SQuADv2.0.
The current version of the question answering model The model is based on the architecture presented in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" paper . In this particular instance, the model has 24 Transformer blocks. On top of that it is using a span prediction head, that is equivalent to token classification with 2 classes: one for the start of the span and one for the end of the span. All model parameters are jointly fine-tuned on the downstream task. More specifically, an input text is fed to the BERT encoder model, and the output states are further fed to the span prediction.
The model was trained with NeMo BERT base uncased checkpoint.
The model was trained on SQuADv2.0  corpus for question answering. For datasets like SQuAD 2.0, this model supports cases when the answer is not contained in the content.
Evaluation on the SQuAD2.0 dev set:
Exact Match 75.04%
The model is available for use in the NeMo toolkit , and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
import nemo import nemo.collections.nlp as nemo_nlp model = nemo_nlp.models.question_answering.qa_model.QAModel.from_pretrained(model_name="qa_squadv2.0_bertbase")
python [NEMO_GIT_FOLDER]/examples/nlp/question_answering/question_answering_squad.py do_training=false pretrained_model=qa_squadv2.0_bertbase model.validation_ds.file=[SOURCE_FILE] model.dataset.version_2_with_negative=true model.dataset.do_lower_case=true
The model takes a Json file as input that follows the SQuAD format.
The model outputs a JSON file as output for prediction and n-Best list.
The length of the input text is currently constrained by the maximum sequence length of the encoder model, which is 512 tokens after tokenization.