Working with pre-trained models
Watson Natural Language Processing provides pre-trained models in over 20 languages. They are curated by a dedicated team of experts, and evaluated for quality on each specific language. These pre-trained models can be used in production environments without you having to worry about license or intellectual property infringements.
Loading and running a model
To load a model, you first need to know its name. Model names follow a standard convention encoding the type of model (like classification or entity extraction), type of algorithm (like SVM or transformers), language code, and details of the type system.
To find the model that matches your needs, use the task catalog. See Watson NLP task catalog.
You can find the expected input for a given block class (for example to the Entity Mentions model) by using help() on the block class run() method:
import watson_nlp
help(watson_nlp.blocks.keywords.TextRank.run)
Watson Natural Language Processing encapsulates natural language functionality through blocks and workflows. Each block or workflow supports functions to:
load(): load a modelrun(): run the model on input argumentstrain(): train the model on your own data (not all blocks and workflows support training)save(): save the model that has been trained on your own data
Blocks and workflows
Blocks operate directly on the input document. An example of a block that operates directly on the input document is the Syntax block, which performs natural language processing operations such as tokenization, lemmatization, part of speech tagging or dependency parsing.
Example: running syntax analysis on a text snippet:
import watson_nlp
# Load the syntax model for English
syntax_model = watson_nlp.load('syntax_izumo_en_stock')
# Run the syntax model and print the result
syntax_prediction = syntax_model.run('Welcome to IBM!')
print(syntax_prediction)
Workflows are predefined end-to-end pipelines from a raw document to a final block, where all necessary blocks are chained as part of the workflow pipeline.
For an example of how to call the Entity Mentions workflow, refer to this sample:
import watson_nlp
# Load the workflow model
mentions_workflow = watson_nlp.load('entity-mentions_transformer-workflow_multilingual_slate.153m.distilled')
# Run the entity extraction workflow on the input text
mentions_workflow.run('IBM announced new advances in quantum computing', language_code="en")
Parent topic: Watson Natural Language Processing library