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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 model to OpenVINO™ Runtime plugin. It also fetches data from the user-provided url to populate the "context" text. The text is then used to search named entities.

Model API

The demo utilizes model wrappers, adapters and pipelines from Python* Model API.

The generalized interface of wrappers with its unified results representation provides the support of multiple different named entity recognition model topologies in one demo.

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 IR format (*.xml + *.bin).

An example of using the Model Downloader:

omz_downloader --list models.lst

An example of using the Model Converter:

omz_converter --list models.lst

Supported Models

  • bert-base-ner

NOTE: Refer to the tables Intel's Pre-Trained Models Device Support and Public Pre-Trained Models Device Support for the details on models inference support at different devices.

Running

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
                                             [--adapter {openvino,ovms}]
                                             [--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 or
                        address of model inference service if using OVMS adapter.
  -i INPUT, --input INPUT
                        Required. URL to a page with context
  --adapter {openvino,ovms}
                        Optional. Specify the model adapter. Default is
                        openvino.
  --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
  -nireq NUM_INFER_REQUESTS, --num_infer_requests NUM_INFER_REQUESTS
                        Optional. Number of infer requests.
  -nstreams NUM_STREAMS, --num_streams NUM_STREAMS
                        Optional. Number of streams to use for inference on
                        the CPU or/and GPU in throughput mode (for HETERO and
                        MULTI device cases use format
                        <device1>:<nstreams1>,<device2>:<nstreams2> or just
                        <nstreams>).
  -nthreads NUM_THREADS, --num_threads NUM_THREADS
                        Optional. Number of threads to use for inference on
                        CPU (including HETERO cases).
  --dynamic_shape       Optional. Run model with dynamic input sequence. If
                        not provided, input sequence is padded to max_seq_len

Demo Inputs

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.

Demo Outputs

The application outputs recognized named entities (LOC - location, PER - person, ORG - organization, MISC - miscellaneous) for each sentence in input text. The application reports

  • Latency: total processing time required to process input data (from loading the vocab and processing the context as tokens to displaying the results).

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)"

Running with OpenVINO Model Server

You can also run this demo with model served in OpenVINO Model Server. Refer to OVMSAdapter to learn about running demos with OVMS.

Exemplary command:

    python3 bert_named_entity_recognition_demo.py.py
            --vocab=<models_dir>/models/public/bert-base-ner/vocab.txt
            --model=localhost:9000/models/bert
            --input_names="input_ids,attention_mask,token_type_ids"
            --input="https://en.wikipedia.org/wiki/Bert_(Sesame_Street)"
            --adapter ovms

Classifying Documents with Long Texts

Notice that when the original "context" (text from the url) does not fit the model input (128 for the Bert-Base), the demo reshapes model to maximum sentence length in the "context" and pad all input sequences to maximum sentence length if model executed with static shape.

See Also