# Example: Token Classification Token classification is a natural language understanding task in which a label is assigned to some tokens in a text **Named Entity Recognition (NER)** and **Part-of-Speech (PoS)** tagging are two popular token classification subtasks. NER models could be trained to recognize specific entities in a text, such as dates, individuals, and locations, while PoS tagging would identify which words in a text are verbs, nouns, and punctuation marks. This guide will walk you through an example of NER model monitoring using spacy. Let's start by creating a dummy model: ```python import spacy NER = spacy.load("en_core_web_sm") ``` And let’s assume this is how our prediction function looks like (maybe it’s part of an http server, for example): ```python def predict(request_id: str, raw_text: str): return { entity.text: entity.label_ for entity in NER(raw_text).ents } ``` Each entity will include the text, the embedding, and the prediction as follow: * text (raw input) - `entity.text` * embedding - `entity.vector` * prediction - `entity.label` ## 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](https://docs.aporia.com/storing-your-predictions) section. For example, you could use a Parquet file on S3 or a Postgres table that looks like this:
idraw_text (text)embeddings (embedding)prediction (categorical)timestamp (datetime)
1I love cookies and Aporia[0.77, 0.87, 0.94, ...]Positive2021-11-20 13:41:00
2This restaurant was really bad[0.97, 0.82, 0.13, ...]Negative2021-11-20 13:45:00
3

Hummus is a

type of food

[0.14, 0.55, 0.66, ...]Natural2021-11-20 13:49:00
To integrate this type of model follow our [Quickstart](https://docs.aporia.com/introduction/quickstart). Check out the [data sources section](https://docs.aporia.com/data-sources) for more information about how to connect from different data sources. ### Schema mapping This type of model is a [multiclass model](https://docs.aporia.com/model-types/multiclass-classification), 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
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