237 lines
8.0 KiB
ReStructuredText
237 lines
8.0 KiB
ReStructuredText
Sentiment Analysis with OpenVINO™
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=================================
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**Sentiment analysis** is the use of natural language processing, text
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analysis, computational linguistics, and biometrics to systematically
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identify, extract, quantify, and study affective states and subjective
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information. This notebook demonstrates how to convert and run a
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sequence classification model using OpenVINO.
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.. _top:
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**Table of contents**:
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- `Imports <#imports>`__
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- `Initializing the Model <#initializing-the-model>`__
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- `Initializing the Tokenizer <#initializing-the-tokenizer>`__
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- `Convert Model to OpenVINO Intermediate Representation format <#convert-model-to-openvino-intermediate-representation-format>`__
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- `Select inference device <#select-inference-device>`__
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- `Inference <#inference>`__
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- `For a single input sentence <#for single -a- -input-sentence>`__
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- `Read from a text file <#read-from-a-text-file>`__
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Imports `⇑ <#top>`__
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###############################################################################################################################
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.. code:: ipython3
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import warnings
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from pathlib import Path
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import time
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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import numpy as np
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from openvino.tools import mo
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from openvino.runtime import PartialShape, Type, serialize, Core
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Initializing the Model `⇑ <#top>`__
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###############################################################################################################################
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We will use the transformer-based
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`DistilBERT base uncased finetuned SST-2 <https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english>`__
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model from Hugging Face.
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.. code:: ipython3
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checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
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model = AutoModelForSequenceClassification.from_pretrained(
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pretrained_model_name_or_path=checkpoint
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)
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Initializing the Tokenizer `⇑ <#top>`__
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###############################################################################################################################
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Text Preprocessing cleans the text-based input data so it can be fed
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into the model.
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`Tokenization <https://towardsdatascience.com/tokenization-for-natural-language-processing-a179a891bad4>`__
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splits paragraphs and sentences into smaller units that can be more
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easily assigned meaning. It involves cleaning the data and assigning
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tokens or IDs to the words, so they are represented in a vector space
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where similar words have similar vectors. This helps the model
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understand the context of a sentence. Here, we will use
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`AutoTokenizer <https://huggingface.co/docs/transformers/main_classes/tokenizer>`__
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- a pre-trained tokenizer from Hugging Face:
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.. code:: ipython3
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tokenizer = AutoTokenizer.from_pretrained(
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pretrained_model_name_or_path=checkpoint
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)
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Convert Model to OpenVINO Intermediate Representation format. `⇑ <#top>`__
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###############################################################################################################################
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`Model conversion API <https://docs.openvino.ai/2023.1/openvino_docs_model_processing_introduction.html>`__
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facilitates the transition between training and deployment environments,
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performs static model analysis, and adjusts deep learning models for
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optimal execution on end-point target devices.
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.. code:: ipython3
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ir_xml_name = checkpoint + ".xml"
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MODEL_DIR = "model/"
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ir_xml_path = Path(MODEL_DIR) / ir_xml_name
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ov_model = mo.convert_model(model, input=[mo.InputCutInfo(shape=PartialShape([1, -1]), type=Type.i64), mo.InputCutInfo(shape=PartialShape([1, -1]), type=Type.i64)])
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serialize(ov_model, ir_xml_path)
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.. parsed-literal::
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/opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-475/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/models/distilbert/modeling_distilbert.py:223: 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.
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mask, torch.tensor(torch.finfo(scores.dtype).min)
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OpenVINO™ Runtime uses the `Infer Request <https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_Infer_request.html>`__
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mechanism which enables running models on different devices in
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asynchronous or synchronous manners. The model graph is sent as an
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argument to the OpenVINO API and an inference request is created. The
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default inference mode is AUTO but it can be changed according to
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requirements and hardware available. You can explore the different
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inference modes and their usage `in
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documentation. <https://docs.openvino.ai/2023.1/openvino_docs_Runtime_Inference_Modes_Overview.html>`__
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.. code:: ipython3
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core = Core()
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Select inference device `⇑ <#top>`__
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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Select device from dropdown list for running inference using OpenVINO:
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.. code:: ipython3
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import ipywidgets as widgets
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device = widgets.Dropdown(
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options=core.available_devices + ["AUTO"],
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value='AUTO',
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description='Device:',
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disabled=False,
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)
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device
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.. parsed-literal::
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Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
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.. code:: ipython3
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warnings.filterwarnings("ignore")
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compiled_model = core.compile_model(ov_model, device.value)
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infer_request = compiled_model.create_infer_request()
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.. code:: ipython3
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def softmax(x):
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"""
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Defining a softmax function to extract
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the prediction from the output of the IR format
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Parameters: Logits array
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Returns: Probabilities
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"""
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e_x = np.exp(x - np.max(x))
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return e_x / e_x.sum()
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Inference `⇑ <#top>`__
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###############################################################################################################################
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.. code:: ipython3
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def infer(input_text):
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"""
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Creating a generic inference function
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to read the input and infer the result
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into 2 classes: Positive or Negative.
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Parameters: Text to be processed
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Returns: Label: Positive or Negative.
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"""
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input_text = tokenizer(
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input_text,
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truncation=True,
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return_tensors="np",
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)
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inputs = dict(input_text)
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label = {0: "NEGATIVE", 1: "POSITIVE"}
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result = infer_request.infer(inputs=inputs)
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for i in result.values():
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probability = np.argmax(softmax(i))
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return label[probability]
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For a single input sentence `⇑ <#top>`__
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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.. code:: ipython3
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input_text = "I had a wonderful day"
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start_time = time.perf_counter()
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result = infer(input_text)
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end_time = time.perf_counter()
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total_time = end_time - start_time
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print("Label: ", result)
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print("Total Time: ", "%.2f" % total_time, " seconds")
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.. parsed-literal::
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Label: POSITIVE
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Total Time: 0.04 seconds
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Read from a text file `⇑ <#top>`__
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+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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.. code:: ipython3
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start_time = time.perf_counter()
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with open("../data/text/food_reviews.txt", "r") as f:
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input_text = f.readlines()
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for lines in input_text:
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print("User Input: ", lines)
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result = infer(lines)
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print("Label: ", result, "\n")
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end_time = time.perf_counter()
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total_time = end_time - start_time
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print("Total Time: ", "%.2f" % total_time, " seconds")
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.. parsed-literal::
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User Input: The food was horrible.
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Label: NEGATIVE
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User Input: We went because the restaurant had good reviews.
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Label: POSITIVE
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Total Time: 0.02 seconds
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