Named entity recognition with OpenVINO™

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The Named Entity Recognition(NER) is a natural language processing method that involves the detecting of key information in the unstructured text and categorizing it into pre-defined categories. These categories or named entities refer to the key subjects of text, such as names, locations, companies and etc.

NER is a good method for the situations when a high-level overview of a large amount of text is needed. NER can be helpful with such task as analyzing key information in unstructured text or automates the information extraction of large amounts of data.

This tutorial shows how to perform named entity recognition using OpenVINO. We will use the pre-trained model elastic/distilbert-base-cased-finetuned-conll03-english. It is DistilBERT based model, trained on conll03 english dataset. The model can recognize four named entities in text: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The model is sensitive to capital letters.

To simplify the user experience, the Hugging Face Optimum library is used to convert the model to OpenVINO™ IR format and quantize it.

Table of contents:

Prerequisites

%pip install -q "diffusers>=0.17.1" "openvino>=2023.1.0" "nncf>=2.5.0" "gradio" "onnx>=1.11.0" "transformers>=4.33.0" --extra-index-url https://download.pytorch.org/whl/cpu
%pip install -q "git+https://github.com/huggingface/optimum-intel.git"

Download the NER model

We load the distilbert-base-cased-finetuned-conll03-english model from the Hugging Face Hub with Hugging Face Transformers library.

Model class initialization starts with calling from_pretrained method. To easily save the model, you can use the save_pretrained() method.

from transformers import AutoTokenizer, AutoModelForTokenClassification

model_id = "elastic/distilbert-base-cased-finetuned-conll03-english"
model = AutoModelForTokenClassification.from_pretrained(model_id)

original_ner_model_dir = 'original_ner_model'
model.save_pretrained(original_ner_model_dir)

tokenizer = AutoTokenizer.from_pretrained(model_id)

Quantize the model, using Hugging Face Optimum API

Post-training static quantization introduces an additional calibration step where data is fed through the network in order to compute the activations quantization parameters. For quantization it will be used Hugging Face Optimum Intel API.

To handle the NNCF quantization process we use class OVQuantizer. The quantization with Hugging Face Optimum Intel API contains the next steps: * Model class initialization starts with calling from_pretrained() method. * Next we create calibration dataset with get_calibration_dataset() to use for the post-training static quantization calibration step. * After we quantize a model and save the resulting model in the OpenVINO IR format to save_directory with quantize() method. * Then we load the quantized model. The Optimum Inference models are API compatible with Hugging Face Transformers models and we can just replace AutoModelForXxx class with the corresponding OVModelForXxx class. So we use OVModelForTokenClassification to load the model.

from functools import partial
from optimum.intel import OVQuantizer

from optimum.intel import OVModelForTokenClassification

def preprocess_fn(data, tokenizer):
    examples = []
    for data_chunk in data["tokens"]:
        examples.append(' '.join(data_chunk))

    return tokenizer(
        examples, padding=True, truncation=True, max_length=128
    )

quantizer = OVQuantizer.from_pretrained(model)
calibration_dataset = quantizer.get_calibration_dataset(
    "conll2003",
    preprocess_function=partial(preprocess_fn, tokenizer=tokenizer),
    num_samples=100,
    dataset_split="train",
    preprocess_batch=True,
)

# The directory where the quantized model will be saved
quantized_ner_model_dir = "quantized_ner_model"

# Apply static quantization and save the resulting model in the OpenVINO IR format
quantizer.quantize(calibration_dataset=calibration_dataset, save_directory=quantized_ner_model_dir)

# Load the quantized model
optimized_model = OVModelForTokenClassification.from_pretrained(quantized_ner_model_dir)

INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, onnx, openvino

No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'
/home/ea/work/openvino_notebooks/test_env/lib/python3.8/site-packages/datasets/load.py:2089: FutureWarning: 'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.
You can remove this warning by passing 'token=False' instead.
  warnings.warn(
No configuration describing the quantization process was provided, a default OVConfig will be generated.

INFO:nncf:Not adding activation input quantizer for operation: 3 DistilBertForTokenClassification/DistilBertModel[distilbert]/Embeddings[embeddings]/NNCFEmbedding[position_embeddings]/embedding_0
INFO:nncf:Not adding activation input quantizer for operation: 2 DistilBertForTokenClassification/DistilBertModel[distilbert]/Embeddings[embeddings]/NNCFEmbedding[word_embeddings]/embedding_0
INFO:nncf:Not adding activation input quantizer for operation: 4 DistilBertForTokenClassification/DistilBertModel[distilbert]/Embeddings[embeddings]/__add___0
INFO:nncf:Not adding activation input quantizer for operation: 5 DistilBertForTokenClassification/DistilBertModel[distilbert]/Embeddings[embeddings]/NNCFLayerNorm[LayerNorm]/layer_norm_0
INFO:nncf:Not adding activation input quantizer for operation: 6 DistilBertForTokenClassification/DistilBertModel[distilbert]/Embeddings[embeddings]/Dropout[dropout]/dropout_0
INFO:nncf:Not adding activation input quantizer for operation: 16 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[0]/MultiHeadSelfAttention[attention]/__truediv___0
INFO:nncf:Not adding activation input quantizer for operation: 25 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[0]/MultiHeadSelfAttention[attention]/matmul_1
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INFO:nncf:Not adding activation input quantizer for operation: 106 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[3]/MultiHeadSelfAttention[attention]/__truediv___0
INFO:nncf:Not adding activation input quantizer for operation: 115 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[3]/MultiHeadSelfAttention[attention]/matmul_1
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INFO:nncf:Not adding activation input quantizer for operation: 136 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[4]/MultiHeadSelfAttention[attention]/__truediv___0
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INFO:nncf:Compiling and loading torch extension: quantized_functions_cpu...
INFO:nncf:Finished loading torch extension: quantized_functions_cpu

Using framework PyTorch: 2.1.0+cpu
/home/ea/work/openvino_notebooks/test_env/lib/python3.8/site-packages/nncf/torch/dynamic_graph/wrappers.py:82: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
  result = operator(*args, **kwargs)
Configuration saved in quantized_ner_model/openvino_config.json
Compiling the model to CPU ...
Setting OpenVINO CACHE_DIR to quantized_ner_model/model_cache

Compare the Original and Quantized Models

Compare the original distilbert-base-cased-finetuned-conll03-english model with quantized and converted to OpenVINO IR format models to see the difference.

Compare performance

As the Optimum Inference models are API compatible with Hugging Face Transformers models, we can just use pipleine() from Hugging Face Transformers API for inference.

from transformers import pipeline

ner_pipeline_optimized = pipeline("token-classification", model=optimized_model, tokenizer=tokenizer)

ner_pipeline_original = pipeline("token-classification", model=model, tokenizer=tokenizer)

import time
import numpy as np

def calc_perf(ner_pipeline):
    inference_times = []

    for data in calibration_dataset:
        text = ' '.join(data['tokens'])
        start = time.perf_counter()
        ner_pipeline(text)
        end = time.perf_counter()
        inference_times.append(end - start)

    return np.median(inference_times)


print(
    f"Median inference time of quantized model: {calc_perf(ner_pipeline_optimized)} "
)

print(
    f"Median inference time of original model: {calc_perf(ner_pipeline_original)} "
)

Median inference time of quantized model: 0.008135671014315449
Median inference time of original model: 0.108725632991991

Compare size of the models

from pathlib import Path

pytorch_model_file = Path(original_ner_model_dir) / "pytorch_model.bin"
if not pytorch_model_file.exists():
    pytorch_model_file = pytorch_model_file.parent / "model.safetensors"
print(f'Size of original model in Bytes is {pytorch_model_file.stat().st_size}')
print(f'Size of quantized model in Bytes is {Path(quantized_ner_model_dir, "openvino_model.bin").stat().st_size}')

Size of original model in Bytes is 260803668
Size of quantized model in Bytes is 133539000

Prepare demo for Named Entity Recognition OpenVINO Runtime

Now, you can try NER model on own text. Put your sentence to input text box, click Submit button, the model label the recognized entities in the text.

import gradio as gr

examples = [
    "My name is Wolfgang and I live in Berlin.",
]

def run_ner(text):
    output = ner_pipeline_optimized(text)
    return {"text": text, "entities": output}

demo = gr.Interface(run_ner,
                    gr.Textbox(placeholder="Enter sentence here...", label="Input Text"),
                    gr.HighlightedText(label="Output Text"),
                    examples=examples,
                    allow_flagging="never")

if __name__ == "__main__":
    try:
        demo.launch(debug=False)
    except Exception:
        demo.launch(share=True, debug=False)
# if you are launching remotely, specify server_name and server_port
# demo.launch(server_name='your server name', server_port='server port in int')
# Read more in the docs: https://gradio.app/docs/