Creating your own models
Certain algorithms in Watson Natural Language Processing can be trained with your own data, for example you can create custom models based on your own data for entity extraction and to classify data.
- Detecting entities with a custom dictionary
- Detecting entities with regular expressions
- Classifying text with a custom classification model
Language support for custom models
You can create custom models and use the following pretrained dictionary and classification models for the shown languages. For a list of the language codes and the corresponding languages, see Language codes.
Custom model | Supported language codes |
---|---|
Dictionary models | af, ar, bs, ca, cs, da, de, el, en, es, fi, fr, he, hi, hr, it, ja, ko, nb, nl, nn, pl, pt, ro, ru, sk, sr, sv, tr, zh_cn, zh_tw (all languages supported in the Syntax part of speech tagging) |
Regexes | af, ar, bs, ca, cs, da, de, el, en, es, fi, fr, he, hi, hr, it, ja, ko, nb, nl, nn, pl, pt, ro, ru, sk, sr, sv, tr, zh_cn, zh_tw (all languages supported in the Syntax part of speech tagging) |
SVM classification with TFIDF | af, ar, ca, cs, da, de, el, en, es, fi, fr, he, hi, hr, it, ja, ko, nb, nl, nn, pl, pt, ro, ru, sk, sr, sv, tr, zh_cn, zh_tw |
SVM classification with USE | ar, de, en, es, fr, it, ja, ko, nl, pl, pt, ru, tr, zh_cn, zh_tw |
CNN classification with GloVe | ar, de, en, es, fr, it, ja, ko, nl, pt, zh_cn |
BERT Multilingual classification | af, ar, ca, cs, da, de, el, en, es, fi, fr, he, hi, hr, it, ja, ko, nb, nl, nn, pl, pt, ro, ru, sk, sr, sv, tr, zh_cn, zh_tw |
Stopword lists | ar, de, en, es, fr, it, ja, ko |
Saving and loading custom models
If you want to use your custom model in another notebook, save it as a Data Asset to your project. This way, you can export the model as part of a project export.
Use the project-lib
library to save and load custom models.
To save a custom model in your notebook as a data asset to export and use in another project:
-
Ensure that you have an access token on the Access control page on the Manage tab of your project. Only project admins can create access tokens. The access token can have viewer or editor access permissions. Only editors can inject the token into a notebook.
-
Add the project token to a notebook by clicking More > Insert project token from the notebook action bar and then run the cell.
By running the inserted hidden code cell, a project object is created that you can use for functions in the
project-lib
library. For details on the availableproject-lib
functions, see Usingproject-lib
for Python. -
Run the
train()
method to create a custom dictionary, regular expression, or classification model and assign this custom model to a variable. For example:custom_block = CNN.train(train_stream, embedding_model.embedding, verbose=2)
-
If you want to save a custom dictionary or regular expression model, convert it to a RBRGeneric block. Converting a custom dictionary or regular expression model to a RBRGeneric block is useful if you want to load and execute the model using the API for Watson Natural Language Processing for Embed. To date, Watson Natural Language Processing for Embed supports running dictionary and regular expression models only as RBRGeneric blocks. To convert a model to a RBRGeneric block, run the following commands:
# Create the custom regular expression model custom_regex_block = watson_nlp.resources.feature_extractor.RBR.train(module_folder, language='en', regexes=regexes) # Save the model to the local file system custom_regex_model_path = 'some/path' custom_regex_block.save(custom_regex_model_path) # The model was saved in a file "executor.zip" in the provided path, in this case "some/path/executor.zip" model_path = os.path.join(custom_regex_model_path, 'executor.zip') # Re-load the model as a RBRGeneric block custom_block = watson_nlp.blocks.rules.RBRGeneric(watson_nlp.toolkit.rule_utils.RBRExecutor.load(model_path), language='en')
-
Save the model as a Data Asset to your project using
project_lib
:project.save_data("<model name>", custom_block.as_file_like_object(), overwrite=True)
To load a custom model to a notebook that was imported from another project:
-
Ensure that you have an access token on the Access control page on the Manage tab of your project. Only project admins can create access tokens. The access token can have viewer or editor access permissions. Only editors can inject the token into a notebook.
-
Add the project token to a notebook by clicking More > Insert project token from the notebook action bar and then run the cell.
By running the inserted hidden code cell, a project object is created that you can use for functions in the
project-lib
library. For details on the availableproject-lib
functions, see Usingproject-lib
for Python. -
Load the model using
project-lib
andwatson-nlp
:custom_block = watson_nlp.load(project.get_file("<model name>"))
Parent topic: Watson Natural language Processing library