Demos
We are going to use John Snow Labs NLP library with visual features installed to do whatever we did in the first part of the post Named Entity Recognition in Documents with Transformer Models using Visual-NLP 1/2 but just in a single line of code!
Imagine being able to predict with any of the 40,000+ pre-trained models or even train new ones, all in a single line of code! That’s the power of nlp.load().
All we have to do is simply load the model:
You can run the following code from here: Colab
Using John Snow Labs one liner code
Now, to predict on the image:
Now let’s predict on the image!
Loading the model
We have successfully predicted on the image, now let’s look at the results.
Warning::Spark Session already created, some configs may not take. | | entities_confidence | entities_ner_entity | entities_x_location | entities_y_location | path | |---:|----------------------:|:----------------------|----------------------:|----------------------:|:--------------------------------------------------------------------| | 0 | 106 | version | 1027 | 89 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 67 | : | 1027 | 89 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 95 | study | 1020 | 128 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 77 | id | 1088 | 128 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 41 | : | 1088 | 128 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 86 | institution | 407 | 190 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 45 | name | 667 | 190 | 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email | 645 | 402 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 108 | name | 58 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 84 | : | 58 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 108 | dribbler | 143 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 86 | , | 143 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 107 | aaa | 244 | 483 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 106 | bbb | 289 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 107 | study | 431 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 97 | date | 500 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 103 | : | 500 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 105 | 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file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 108 | 120 | 835 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 106 | / | 835 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 106 | 80 | 835 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 89 | mmhg | 926 | 478 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 105 | pm | 432 | 509 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 111 | mrn | 58 | 547 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 101 | : | 58 | 547 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 99 | 12341820060912 | 130 | 547 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 108 | patient | 432 | 547 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 70 | location | 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file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 72 | male | 530 | 586 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 106 | height | 790 | 586 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 95 | : | 790 | 586 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 88 | 123 | 880 | 586 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 77 | cm | 923 | 591 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 80 | yyyy | 56 | 617 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 109 | age | 56 | 655 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 92 | : | 56 | 655 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 101 | 27 | 117 | 655 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 51 | years | 147 | 655 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 103 | weight | 788 | 655 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 98 | : | 788 | 655 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 102 | 25 | 883 | 655 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 57 | kg | 914 | 655 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 110 | reason | 58 | 697 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 76 | for | 146 | 697 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 85 | study | 186 | 697 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 80 | : | 186 | 697 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 102 | mi | 268 | 697 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 102 | bsa | 790 | 697 | 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file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 106 | heparine | 211 | 778 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 108 | , | 211 | 778 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 105 | paracetamol | 323 | 778 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 102 | summary | 56 | 827 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 69 | statements | 181 | 827 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 42 | . | 381 | 866 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 75 | two | 472 | 866 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 82 | - | 472 | 866 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 78 | dimensional | 472 | 866 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 97 | transthoracic | 668 | 866 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 85 | echocardiogram | 821 | 866 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 40 | was | 1009 | 871 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 73 | performed | 57 | 897 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 78 | . | 57 | 897 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 53 | the | 183 | 897 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 38 | study | 230 | 897 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 33 | technically | 344 | 897 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 32 | limited | 472 | 897 | file:/F:/Work/repos/nlu_new/ner/nlu/tests/nlu_ocr_tests/ocr_ner.png | | 0 | 46 | . | 472 | 897 | 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Indeed! With John Snow Labs NLP, NER becomes a breeze. Gone are the days of painstakingly creating a pipeline and making predictions. Now, we can accomplish all of this in a flash with just a single line of code! It’s not only efficient but also a real time-saver.
Summary
In this article, we’ve taken a deep dive into the process of creating a pipeline for NER using John Snow Labs library with visual features installed. We’ve seen how the powerful tools provided by John Snow Labs can simplify this process, allowing us to perform complex tasks with just a single line of code. This not only makes the process more efficient but also saves us a significant amount of time.
