# Example: Question Answering

**Question answering models can retrieve the answer to a question from a given text**, which is useful for searching for an answer in a document. 

Throughout the guide, we will use a simple question answering model based on 🤗 [HuggingFace](https://huggingface.co/):thumbsup:

```python
>>> from transformers import pipeline

>>> qa_model = pipeline("question-answering")
```

This downloads a default pretrained model and tokenizer for Questioning Answering. Now you can use the `qa_model` on your target question / context:

```python
qa_model(
    question="Where are the best cookies?",
    context="The best cookies are in Aporia's office."
)

# ==> {'score': 0.8362494111061096,
#      'start': 24,
#      'end': 39,
#      'answer': "Aporia's office"}
```

## Extract Embeddings 

To extract embeddings from the model, we'll first need to do two things:

1. Pass `output_hidden_states=True` to our model params.
2. When we call `pipeline(...)` it does a lot of things for us - preprocessing, inference, and postprocessing. **We'll need to break all this**, so we can interfere in the middle and get embeddings [😉](https://emojipedia.org/winking-face/)

In other words:

```python
qa_model = pipeline("question-answering", model_kwargs={"output_hidden_states": True})

# Preprocess
model_inputs = next(qa_model.preprocess(QuestionAnsweringPipeline.create_sample(
    question="Where are the best cookies?", 
    context="The best cookies are in Aporia's office."
)))

# Inference
model_output = qa_model.model(input_ids=model_inputs["input_ids"])

# Postprocessing
start, end = model_output[:2]
qa_model.postprocess([{"start": start, "end": end, **model_inputs}])
  # ==> {'score': 0.8362494111061096, 'start': 24, 'end': 39, 'answer': "Aporia's office"}
```

And finally, to extract embeddings for this prediction:

```python
embeddings = torch.mean(model_output.hidden_states[-1], dim=1).squeeze()
```

## Storing your Predictions

The next step would be to store your predictions in a data store, including the embeddings themselves. For more information on storing your predictions, please check out the [Storing Your Predictions](/storing-your-predictions/overview.md) section.

For example, you could use a Parquet file on S3 or a Postgres table that looks like this:

<table><thead><tr><th width="88.33333333333331">id</th><th width="291">question (text)</th><th width="227">context (text)</th><th width="263">embeddings (embedding)</th><th width="187.66666666666674">answer (text)</th><th width="186">score (numeric)</th><th width="207">timestamp (datetime)</th></tr></thead><tbody><tr><td>1</td><td>Where are the best cookies?</td><td>The best cookies are in...</td><td><code>[0.77, 0.87, 0.94, ...]</code></td><td><code>Aporia's Office</code></td><td>0.982</td><td>2021-11-20 13:41:00</td></tr><tr><td>2</td><td>Where is the best hummus?</td><td>The best hummus is in...</td><td><code>[0.97, 0.82, 0.13, ...]</code></td><td><code>Another Place</code></td><td>0.881</td><td>2021-11-20 13:45:00</td></tr><tr><td>3</td><td>Where is the best burger?</td><td>The best burger is in...</td><td><code>[0.14, 0.55, 0.66, ...]</code></td><td><code>Blablabla</code></td><td>0.925</td><td>2021-11-20 13:49:00</td></tr></tbody></table>

To integrate this type of model follow our [Quickstart](/introduction/quickstart.md).

Check out the [data sources section](/data-sources/overview.md) for more information about how to connect from different data sources.

### Schema mapping

This type of model is a [multiclass model](/model-types/multiclass-classification.md), with `text` raw input and a `embedding` feature.

There are 2 unique types in aporia to help you integrate your NLP model - `text`, and `embedding`.

The `text` should be used with your raw\_text column. Note that by default, in the UI every string column will be automatically marked as `categorical`, but you'll have the option to change it to `text` for NLP use cases.

The `embedding` as the name suggested, should be used with your embedding column. Note that by default, in the UI every array column will be automatically marked as `array`, but you'll have the option to change it to `embedding` for NLP use cases.

## Next steps

* **Create a custom dashboard for your model in Aporia** - Drag & drop widgets to show different performance metrics, top drifted features, etc.
* **Visualize NLP drift using Aporia's Embeddings Projector** - Use the Embedding Projector widget within the investigation room, to view drift between different datasets in production, using UMAP for dimension reduction.
* **Set up monitors to get notified for ML issues** - Including data integrity issues, model performance degradation, and model drift. For example:
  * Make sure the distribution of the different entity labels doesn’t drift across time
  * Make sure the distribution of the embedding vector doesn’t drift across time


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.aporia.com/nlp/example-question-answering.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.