Empfohlen
Weitere ähnliche Inhalte
Ähnlich wie Supercharging your web apps with ML using MediaPipe.pptx
Ähnlich wie Supercharging your web apps with ML using MediaPipe.pptx (20)
Mehr von Samuel Olaegbe
Mehr von Samuel Olaegbe (8)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
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/
Hinweis der Redaktion
- 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.