Named entity recognition with OpenVINO™¶
This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. See the installation guide for instructions to run this tutorial locally on Windows, Linux or macOS. To run without installing anything, click the “Open in Colab” button.
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-dev>=2023.0.0" "nncf>=2.5.0" "gradio" "onnx>=1.11.0" "onnxruntime>=1.14.0" "optimum-intel>=1.9.1" "transformers>=4.31.0"
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
audiocraft 0.0.2a2 requires xformers, which is not installed.
audiocraft 0.0.2a2 requires torch>=2.0.0, but you have torch 1.13.1+cpu which is incompatible.
audiocraft 0.0.2a2 requires torchaudio>=2.0.0, but you have torchaudio 0.13.1+cpu which is incompatible.
deepfloyd-if 1.0.2rc0 requires accelerate~=0.15.0, but you have accelerate 0.22.0.dev0 which is incompatible.
deepfloyd-if 1.0.2rc0 requires diffusers~=0.16.0, but you have diffusers 0.18.2 which is incompatible.
deepfloyd-if 1.0.2rc0 requires transformers~=4.25.1, but you have transformers 4.30.2 which is incompatible.
paddleclas 2.5.1 requires faiss-cpu==1.7.1.post2, but you have faiss-cpu 1.7.4 which is incompatible.
paddleclas 2.5.1 requires gast==0.3.3, but you have gast 0.4.0 which is incompatible.
ppgan 2.1.0 requires librosa==0.8.1, but you have librosa 0.9.2 which is incompatible.
ppgan 2.1.0 requires opencv-python<=4.6.0.66, but you have opencv-python 4.7.0.72 which is incompatible.
pytorch-lightning 1.6.5 requires protobuf<=3.20.1, but you have protobuf 3.20.3 which is incompatible.
spacy 3.5.2 requires pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4, but you have pydantic 2.0.3 which is incompatible.
thinc 8.1.10 requires pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4, but you have pydantic 2.0.3 which is incompatible.
visualdl 2.5.2 requires gradio==3.11.0, but you have gradio 3.36.1 which is incompatible.
[notice] A new release of pip is available: 23.1.2 -> 23.2
[notice] To update, run: pip install --upgrade pip
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)
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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)
2023-07-17 14:40:49.402855: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0. 2023-07-17 14:40:49.442756: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-07-17 14:40:50.031065: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda' comet_ml is installed but COMET_API_KEY is not set.
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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 INFO:nncf:Not adding activation input quantizer for operation: 30 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[0]/__add___0 INFO:nncf:Not adding activation input quantizer for operation: 31 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[0]/NNCFLayerNorm[sa_layer_norm]/layer_norm_0 INFO:nncf:Not adding activation input quantizer for operation: 35 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[0]/__add___1 INFO:nncf:Not adding activation input quantizer for operation: 36 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[0]/NNCFLayerNorm[output_layer_norm]/layer_norm_0 INFO:nncf:Not adding activation input quantizer for operation: 46 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[1]/MultiHeadSelfAttention[attention]/__truediv___0 INFO:nncf:Not adding activation input quantizer for operation: 55 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[1]/MultiHeadSelfAttention[attention]/matmul_1 INFO:nncf:Not adding activation input quantizer for operation: 60 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[1]/__add___0 INFO:nncf:Not adding activation input quantizer for operation: 61 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[1]/NNCFLayerNorm[sa_layer_norm]/layer_norm_0 INFO:nncf:Not adding activation input quantizer for operation: 65 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[1]/__add___1 INFO:nncf:Not adding activation input quantizer for operation: 66 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[1]/NNCFLayerNorm[output_layer_norm]/layer_norm_0 INFO:nncf:Not adding activation input quantizer for operation: 76 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[2]/MultiHeadSelfAttention[attention]/__truediv___0 INFO:nncf:Not adding activation input quantizer for operation: 85 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[2]/MultiHeadSelfAttention[attention]/matmul_1 INFO:nncf:Not adding activation input quantizer for operation: 90 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[2]/__add___0 INFO:nncf:Not adding activation input quantizer for operation: 91 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[2]/NNCFLayerNorm[sa_layer_norm]/layer_norm_0 INFO:nncf:Not adding activation input quantizer for operation: 95 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[2]/__add___1 INFO:nncf:Not adding activation input quantizer for operation: 96 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[2]/NNCFLayerNorm[output_layer_norm]/layer_norm_0 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 INFO:nncf:Not adding activation input quantizer for operation: 120 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[3]/__add___0 INFO:nncf:Not adding activation input quantizer for operation: 121 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[3]/NNCFLayerNorm[sa_layer_norm]/layer_norm_0 INFO:nncf:Not adding activation input quantizer for operation: 125 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[3]/__add___1 INFO:nncf:Not adding activation input quantizer for operation: 126 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[3]/NNCFLayerNorm[output_layer_norm]/layer_norm_0 INFO:nncf:Not adding activation input quantizer for operation: 136 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[4]/MultiHeadSelfAttention[attention]/__truediv___0 INFO:nncf:Not adding activation input quantizer for operation: 145 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[4]/MultiHeadSelfAttention[attention]/matmul_1 INFO:nncf:Not adding activation input quantizer for operation: 150 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[4]/__add___0 INFO:nncf:Not adding activation input quantizer for operation: 151 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[4]/NNCFLayerNorm[sa_layer_norm]/layer_norm_0 INFO:nncf:Not adding activation input quantizer for operation: 155 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[4]/__add___1 INFO:nncf:Not adding activation input quantizer for operation: 156 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[4]/NNCFLayerNorm[output_layer_norm]/layer_norm_0 INFO:nncf:Not adding activation input quantizer for operation: 166 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[5]/MultiHeadSelfAttention[attention]/__truediv___0 INFO:nncf:Not adding activation input quantizer for operation: 175 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[5]/MultiHeadSelfAttention[attention]/matmul_1 INFO:nncf:Not adding activation input quantizer for operation: 180 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[5]/__add___0 INFO:nncf:Not adding activation input quantizer for operation: 181 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[5]/NNCFLayerNorm[sa_layer_norm]/layer_norm_0 INFO:nncf:Not adding activation input quantizer for operation: 185 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[5]/__add___1 INFO:nncf:Not adding activation input quantizer for operation: 186 DistilBertForTokenClassification/DistilBertModel[distilbert]/Transformer[transformer]/ModuleList[layer]/TransformerBlock[5]/NNCFLayerNorm[output_layer_norm]/layer_norm_0 INFO:nncf:Collecting tensor statistics |█ | 4 / 38 INFO:nncf:Collecting tensor statistics |███ | 8 / 38 INFO:nncf:Collecting tensor statistics |█████ | 12 / 38 INFO:nncf:Compiling and loading torch extension: quantized_functions_cpu... huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using tokenizers before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using tokenizers before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using tokenizers before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using tokenizers before the fork if possible - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false) INFO:nncf:Finished loading torch extension: quantized_functions_cpu WARNING:nncf:You are setting forward on an NNCF-processed model object. NNCF relies on custom-wrapping the forward call in order to function properly. Arbitrary adjustments to the forward function on an NNCFNetwork object have undefined behaviour. If you need to replace the underlying forward function of the original model so that NNCF should be using that instead of the original forward function that NNCF saved during the compressed model creation, you can do this by calling: model.nncf.set_original_unbound_forward(fn) if fn has an unbound 0-th self argument, or with model.nncf.temporary_bound_original_forward(fn): ... if fn already had 0-th self argument bound or never had it in the first place.
/home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/nncf/torch/nncf_network.py:938: FutureWarning: Old style of accessing NNCF-specific attributes and methods on NNCFNetwork objects is deprecated. Access the NNCF-specific attrs through the NNCFInterface, which is set up as an `nncf` attribute on the compressed model object.
For instance, instead of `compressed_model.get_graph()` you should now write `compressed_model.nncf.get_graph()`.
The old style will be removed after NNCF v2.5.0
warning_deprecated(
/home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:338: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
return self._level_low.item()
/home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/nncf/torch/quantization/layers.py:346: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
return self._level_high.item()
/home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/nncf/torch/dynamic_graph/wrappers.py:81: 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)
/home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/nncf/torch/quantization/quantize_functions.py:140: FutureWarning: 'torch.onnx._patch_torch._graph_op' is deprecated in version 1.13 and will be removed in version 1.14. Please note 'g.op()' is to be removed from torch.Graph. Please open a GitHub issue if you need this functionality..
output = g.op(
/home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/torch/onnx/_patch_torch.py:81: UserWarning: The shape inference of org.openvinotoolkit::FakeQuantize type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)
_C._jit_pass_onnx_node_shape_type_inference(
/home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/torch/onnx/utils.py:687: UserWarning: The shape inference of org.openvinotoolkit::FakeQuantize type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)
_C._jit_pass_onnx_graph_shape_type_inference(
/home/ea/work/notebooks_convert/notebooks_conv_env/lib/python3.8/site-packages/torch/onnx/utils.py:1178: UserWarning: The shape inference of org.openvinotoolkit::FakeQuantize type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function. (Triggered internally at ../torch/csrc/jit/passes/onnx/shape_type_inference.cpp:1884.)
_C._jit_pass_onnx_graph_shape_type_inference(
WARNING:nncf:You are setting forward on an NNCF-processed model object. NNCF relies on custom-wrapping the forward call in order to function properly. Arbitrary adjustments to the forward function on an NNCFNetwork object have undefined behaviour. If you need to replace the underlying forward function of the original model so that NNCF should be using that instead of the original forward function that NNCF saved during the compressed model creation, you can do this by calling: model.nncf.set_original_unbound_forward(fn) if fn has an unbound 0-th self argument, or with model.nncf.temporary_bound_original_forward(fn): ... if fn already had 0-th self argument bound or never had it in the first place.
Configuration saved in quantized_ner_model/openvino_config.json
Compiling the model...
Prepare demo for Named Entity Recognition OpenVINO Runtime ⇑¶
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)
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=True)
except Exception:
demo.launch(share=True, debug=True)
# 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/
Thanks for being a Gradio user! If you have questions or feedback, please join our Discord server and chat with us: https://discord.gg/feTf9x3ZSB Running on local URL: http://127.0.0.1:7860 To create a public link, set share=True in launch().
Keyboard interruption in main thread... closing server.
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 ⇑¶
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: 0.008888308017048985