NLP Onboarding
This page walks through the basics of setting up a natural language processing (NLP) model and onboarding it to
Arthur Scope to monitor language-specific performance.
Getting Started
The first step is to import functions from the arthurai
package and establish a connection with Arthur Scope.
# Arthur imports
from arthurai import ArthurAI
from arthurai.common.constants import InputType, OutputType, Stage
arthur = ArthurAI(url="https://app.arthur.ai",
login="<YOUR_USERNAME_OR_EMAIL>")
Registering an NLP Model
Each NLP model is created with a name and with input_type = InputType.NLP
. Here, we register a classification model on text specifying a text_delimiter
of NOT_WORD
:
arthur_nlp_model = arthur.model(name="NLPQuickstart",
input_type=InputType.NLP,
model_type=OutputType.Multiclass,
text_delimiter=TextDelimiter.NOT_WORD)
The different OutputType
values currently supported for NLP models are classification, multi-labeling, and regression.
Text Delimiter
NLP models optionally allow specifying a text_delimiter
, which specifies how a raw document is split into tokens.
If a text delimiter is not provided, a default text_delimiter
will be TextDelimiter.NOT_WORD
. This delimiter will ignore punctuation and tokenize text based only on the words present. However, suppose punctuation and non-word text needs to be considered by your model. In that case, you should consider using other options for a delimiter to ensure those other pieces of text are processed by your NLP model.
For a full list of available text delimiters with examples, see the
TextDelimiter constant documentation in our SDK reference.
Additionally, Arthur supports sending the pre-tokenized text. For steps on registering tokens with Arthur, see our generative text walkthrough.
Formatting Reference/Inference Data
Column names can contain only alphanumeric and underscore characters. The rest of the string values can have
additional characters as raw text.
text_attr pred_value ground_truth non_input_1
0 'Here-is some text' 0.1 0 0.2
1 'saying a whole lot' 0.05 0 -0.3
2 'of important things!' 0.02 1 0.7
3 'With all kinds of chars?!' 0.2 0 0.1 ...
4 'But attribute/column names' 0.6 1 -0.6
5 'can only use underscore.' 0.9 1 -0.9
...
Reviewing the Model Schema
Before you register your model with Arthur by calling arthur_model.save()
, you can call arthur_model.review()
the model schema to check that your data is parsed correctly.
For an NLP model, the model schema should look like this:
name stage value_type categorical is_unique
0 text_attr PIPELINE_INPUT UNSTRUCTURED_TEXT False True
1 pred_value PREDICTED_VALUE FLOAT False False ...
2 ground_truth GROUND_TRUTH INTEGER True False
3 non_input_1 NON_INPUT_DATA FLOAT False False
...
Finishing Onboarding
Once you have finished formatting your reference data and your model schema looks correct using arthur_model.review()
, you are finished registering your model and its attributes - so you are ready to complete onboarding your model.
To finish onboarding your NLP model, the following steps apply, which is the same for NLP models as it is for models
of any InputType
and OutputType
:
Enrichments
For an overview of configuring enrichments for NLP models, see the {doc}/user-guide/walkthroughs/enrichments
guide.
For a step-by-step walkthrough of setting up the explainability Enrichment for NLP models, see
{ref}nlp_explainability
.
Updated 10 months ago