BERT Named Entity Recognition Python* Demo¶
This README describes the Named Entity Recognition (NER) demo application that uses a CONLL2003-tuned BERT model for inference.
How It Works¶
On startup the demo application reads command line parameters and loads a network to Inference engine. It also fetch data from the user-provided url to populate the “context” text. The text is then used to search named entities.
Preparing to Run¶
The list of models supported by the demo is in
<omz_dir>/demos/bert_named_entity_recognition_demo/python/models.lst file. This file can be used as a parameter for Model Downloader and Converter to download and, if necessary, convert models to OpenVINO Inference Engine format (*.xml + *.bin).
An example of using the Model Downloader:
python3 <omz_dir>/tools/downloader/downloader.py --list models.lst
An example of using the Model Converter:
python3 <omz_dir>/tools/downloader/converter.py --list models.lst
Running the application with the
-h option yields the following usage message:
usage: bert_named_entity_recognition_demo.py [-h] -v VOCAB -m MODEL -i INPUT [--input_names INPUT_NAMES] [-d DEVICE] Options: -h, --help Show this help message and exit. -v VOCAB, --vocab VOCAB Required. path to the vocabulary file with tokens -m MODEL, --model MODEL Required. Path to an .xml file with a trained model -i INPUT, --input INPUT Required. URL to a page with context --input_names INPUT_NAMES Optional. Inputs names for the network. Default values are "input_ids,attention_mask,token_type_ids" -d DEVICE, --device DEVICE Optional. Target device to perform inference on. Default value is CPU
The application reads text from the HTML page at the given URL. The model and its parameters (inputs and outputs) are also important demo arguments. Notice that since order of inputs for the model does matter, the demo application checks that the inputs specified from the command-line match the actual network inputs.
The application outputs recognized named entities (
LOC - location,
PER - person,
ORG - organization,
MISC - miscellaneous) for each sentence in input text.
Example Demo Cmd-Line¶
You can use the following command to try the demo (assuming the model from the Open Model Zoo, downloaded and converted with the Model Downloader executed with “–name bert*”):
python3 bert_named_entity_recognition_demo.py.py --vocab=<models_dir>/models/public/bert-base-ner/vocab.txt --model=<path_to_model>/bert-base-ner.xml --input_names="input_ids,attention_mask,token_type_ids" --input="https://en.wikipedia.org/wiki/Bert_(Sesame_Street)"
Classifying Documents with Long Texts¶
Notice that when the original “context” (text from the url) do not fit the model input (128 for the Bert-Base), the demo reshapes model to maximum sentence length in the “context”.
Even though the demo reports inference performance (by measuring wall-clock time for individual inference calls), it is only baseline performance, as certain tricks like batching, throughput mode can be applied. Please use the full-blown Benchmark C++ Sample for any actual performance measurements.