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Supercharging your web apps with ML using MediaPipe.pptx
- 1. Supercharging your web apps with ML using MediaPipe Google I/O Extended, Warri
- 2. The challenge As a web developer, I will like to apply ML to my web applications
- 4. What is MediaPipe ● Created by Google in 2012 ● To be used for processing Youtube videos for compliance ● Eventually expanded to other Google products like Google Home ● First announced publicly in 2019 ● It is a cross-platform, open source framework ● With a low-code API
- 5. MediaPipe solutions Vision Solutions Text Solutions More at https://developers.google.com/mediapipe/solutions
- 6. Using MediaPipe in your web apps Demo app: https://gdg-warri.netlify.app/ Description: A movie rating website, which uses Sentiment Analysis a branch of Text Classification to categorize text with predefined labels, and in this case “positive” or “negative”. Give your review about a movie to try it out!
- 7. Text Classification Text classification is an NLP task where algorithms automatically assign predefined categories or labels to text documents based on their content.
- 8. Creating a Text Classifier in 3 steps 1. Install the @mediapipe/tasks-text package 2. Load WebAssembly files needed for the classification 3. Instantiate the TextClassifier with custom options
- 9. Step one: Install the MediaPipe library just like any other JS library..
- 10. Step two: Import the Web Assembly files MediaPipe needs to work on Text Tasks
- 11. modelAssetPath: Path to the model our classification is going to run on. maxResults: The number of top results to return. scoreThreshold: Percentage at which results should be returned
- 12. Text Classification models ● BERT-classifier model (recommended for performance) ● Average word embedding model ● …and yours! Learn more: https://developers.google.com/mediapipe/solutions/text/text_classifier#models
- 13. About me
- 14. Connect with me Samuel Olaegbe Twitter: @devloader Github: @goodhands LinkedIn: Samuel Olaegbe Telegram: https://t.me/samuelolaegbe Blog: https://devloader.hashnode.dev/
Notes de l'éditeur
- Facial detection Object identification Gesture detection Facial posture Face Landmark Detection Text classification Language detection
- You could use the remote URL to the model instead: https://storage.googleapis.com/mediapipe-models/text_classifier/bert_classifier/float32/latest/bert_classifier.tflite
- BERT-classifier model; This model uses a BERT-based architecture (specifically, the MobileBERT model) and is recommended because of its high accuracy. It contains metadata that allows the task to perform out-of-graph BERT tokenization. - Bidirectional Encoder Representations from Transformers Average word embedding model: This model uses an average word-embedding architecture. This model offers a smaller model size and lower latency at the cost of a lower prediction accuracy compared to the BERT-classifier. Customizing this model through additional training is also faster than doing training of the BERT-based classifier. This model contains metadata that allows the task to perform out-of-graph regex tokenization.