question_answering.rst

Question Answering

The Task

Question Answering is the task of being able to answer questions pertaining to some known context. For example, given a context about some historical figure, any question pertaininig to the context should be answerable. In our case the article would be our input context and question, and the answer would be the output sequence from the model.

Note

We currently only support Extractive Question Answering, like the task performed using the SQUAD like datasets.

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Example

Let's look at an example. We'll use the SQUAD 2.0 dataset, which contains train-v2.0.json and dev-v2.0.json. Each JSON file looks like this:

{
    "answers": {
        "answer_start": [94, 87, 94, 94],
        "text": ["10th and 11th centuries", "in the 10th and 11th centuries", "10th and 11th centuries", "10th and 11th centuries"]
    },
    "context": "\"The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave thei...",
    "id": "56ddde6b9a695914005b9629",
    "question": "When were the Normans in Normandy?",
    "title": "Normans"
}
...

In the above, the context key represents the context used for the question and answer, the question key represents the question being asked with respect to the context, the answer key stores the answer(s) for the question. id and title are used for unique identification and grouping concepts together respectively. Once we've downloaded the data using ~flash.core.data.download_data, we create the ~flash.text.question_answering.data.QuestionAnsweringData. We select a pre-trained backbone to use for our ~flash.text.question_answering.model.QuestionAnsweringTask and finetune on the SQUAD 2.0 data. The backbone can be any Question Answering model from HuggingFace/transformers.

Note

When changing the backbone, make sure you pass in the same backbone to the ~flash.text.question_answering.data.QuestionAnsweringData and the ~flash.text.question_answering.model.QuestionAnsweringTask!

Next, we use the trained ~flash.text.question_answering.model.QuestionAnsweringTask for inference. Finally, we save the model. Here's the full example:

../../../flash_examples/question_answering.py

To learn how to view the available backbones / heads for this task, see backbones_heads.

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Accelerate Training & Inference with Torch ORT

Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. Enabling Torch ORT requires a single flag passed to the QuestionAnsweringTask once installed. See installation instructions here.

Note

Not all Transformer models are supported. See this table for supported models + branches containing fixes for certain models.

...

model = QuestionAnsweringTask(backbone="distilbert-base-uncased", max_answer_length=30, enable_ort=True)