diff --git a/examples/blog_post_examples/pytorch_ecosystem.py b/examples/blog_post_examples/pytorch_ecosystem.py new file mode 100644 index 000000000..1816d845f --- /dev/null +++ b/examples/blog_post_examples/pytorch_ecosystem.py @@ -0,0 +1,85 @@ +# Copyright 2021 The Forte Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Example code for pytorch blog post +""" +from fortex.spacy import SpacyProcessor + +from forte.data.readers import HTMLReader +from forte.pipeline import Pipeline +from forte.data.data_pack import DataPack +from forte.processors.stave import StaveProcessor + +from ft.onto.base_ontology import Dependency +from ftx.medical import MedicalEntityMention + + +def main(): + + # Create input HTML string + input_html = ( + "

" + "she does not have SVS syndrome from an axillary vein thrombosis." + "

" + ) + + + # Assemble the pipeline: + # A pipeline is consisted of a set of Components (readers and processors). + # The data flows in the pipeline as data packs, and each component will + # use or add information to the data packs. + pipeline = Pipeline[DataPack]() + + # Set HTMLReader as pipeline reader: + # HTMLReader takes in list of html strings, cleans the HTML tags and + # stores the cleaned text in pack. + pipeline.set_reader(HTMLReader()) + + # Add SpacyProcessor to process the datapack: + # SpacyProcessor provides functions including sentence parsing, tokenize, + # POS tagging, lemmatization, NER, and medical entity linking. This + # processor will do user defined tasks according to configs. + pipeline.add(SpacyProcessor(), config={ + "processors": ["sentence", "tokenize", "pos", "ner", "dep", "umls_link"] + }) + + # Add StaveProcessor to visualize the processing results： + # StaveProcessor provides easy visualization for forte users. We can + # visualize datapack with annotations by inserting it into the forte + # pipeline. + pipeline.add(StaveProcessor()) + + # Initialize and run the pipeline + pipeline.initialize() + for datapack in pipeline.process_dataset(input_html): + + # Get the results of dependency parsing + for link in datapack.get(Dependency): + parent = link.get_parent() + child = link.get_child() + # Print out the dependency between parent and child + print( + f"'{child.text}' has dependency '{link.dep_label}'" + f" of parent '{parent.text}'" + ) + + # Retrieve all the MedicalEntityMention in datapack + for medical_entity in datapack.get(MedicalEntityMention): + # Get and print out the UMLS links + for umls_link in medical_entity.umls_entities: + print(umls_link) + + +if __name__ == "__main__": + main()