Build a Sarcasm Detector with Transformers, ClearML and Gradio

Table of Contents

1. Introduction
2. The Challenge of Machine Learning Projects
3. Ingesting Data into a ClearML Versioned Dataset
4. Training Machine Learning Models
5. Creating a Gradio App for QA and Annotation
6. Closing the Feedback Loop with New Data
7. Comparing Model Performance
8. Serving the New Models
9. Conclusion

Introduction

In this article, we will explore how to Create a sarcasm detector using Hugging Face Transformers and ClearML. We'll start by ingesting the data into a ClearML versioned dataset, train our machine learning models, create a Gradio app for QA and annotation, and finally, close the feedback loop by involving others in the process. We will also compare the performance of the different models and serve the new models for deployment. Let's get started!

The Challenge of Machine Learning Projects

In machine learning projects, we often encounter the 80/20 rule, where we can achieve 80% of the value relatively quickly with a small team or even by ourselves. However, the remaining 20% can be more challenging and time-consuming to achieve. The goal of this article is to demonstrate how to close the feedback loop and involve other team members, such as QA, product, and annotation teams, to accelerate progress in this last 20% phase.

Ingesting Data into a ClearML Versioned Dataset

To begin, we will ingest the data into a ClearML versioned dataset. This allows us to make the data more accessible using ClearML's Python SDK and CLI tool. We can easily share the data with others, track its history, and make modifications. By organizing the data in a versioned dataset, we can involve multiple team members seamlessly, allowing them to access and contribute to the dataset.

Training Machine Learning Models

Next, we will train two machine learning models: a baseline model and a model using Hugging Face Transformers. We will leverage ClearML's experiment manager to track and monitor the training process. By using ClearML, we can easily compare the performance of different models, track their training time, and view Relevant metrics such as accuracy and loss. This step will help us identify the best performing model for our sarcasm detection task.

Creating a Gradio App for QA and Annotation

After training our models, we will create a Gradio app that serves as a QA tool for internal use. This app will allow other team members, such as labelers and annotators, to test specific datasets and provide feedback on the model's predictions. With the Gradio app, they will be able to label data, test different combinations of data, and directly compare the models' outputs. This app will streamline the QA and annotation process, making it easier for everyone involved.

Closing the Feedback Loop with New Data

To close the feedback loop, we will Gather the labeled samples from the Gradio app and create a new version of our original dataset, incorporating the newly labeled data. This new version will contain the changes made by the annotators and labelers, allowing us to retrain the models with the updated dataset. By involving others in the process, we can iteratively improve the models' performance and adapt to changing requirements.

Comparing Model Performance

With the new dataset version, we can compare the performance of the models trained on the updated data. ClearML's experiment manager allows us to easily compare different models using metrics such as accuracy and training time. We can Visualize the differences in performance and identify the impact of the new data on the models' predictions. This comparison helps us understand how the models have improved and make informed decisions for further improvements.

Serving the New Models

Finally, we will deploy the new models to make them available for use. ClearML makes it easy to serve models by providing model artifacts and deployment options. We can deploy the models in various environments, such as on-premises or cloud servers, and use ClearML's model serving capabilities to handle predictions and serve the models through APIs. This ensures that the new models are accessible and can be integrated into applications or workflows.

Conclusion

In this article, we have explored the process of creating a sarcasm detector using Hugging Face Transformers and ClearML. By leveraging ClearML's features, such as versioned datasets, the experiment manager, and model serving, we have demonstrated how to involve other team members in the machine learning workflow and improve model performance iteratively. The use of ClearML's tools and functionalities enables collaboration, experimentation, and deployment of models, closing the feedback loop and accelerating progress in machine learning projects.

Highlights

• Learn how to create a sarcasm detector using Hugging Face Transformers and ClearML
• Ingest data into a ClearML versioned dataset for easy access and tracking
• Train machine learning models using ClearML's experiment manager and track their performance
• Create a Gradio app for QA and annotation to involve others in the development process
• Close the feedback loop by gathering labeled data from the app and creating a new dataset version
• Compare model performance and understand the impact of new data on predictions
• Serve the new models using ClearML's model serving capabilities for integration and deployment

FAQ

Q: Can ClearML be used for other machine learning projects besides sarcasm detection? A: Yes, ClearML can be used for various machine learning projects. Its features, such as versioned datasets, experiment tracking, and model serving, are applicable to different tasks and industries.

Q: How can I involve others in the annotation and labeling process? A: With ClearML's Gradio app, you can easily involve other team members in the QA and annotation process. They can test specific datasets, label data, and provide feedback on the models' predictions.

Q: Can I deploy the models trained using ClearML in a production environment? A: Yes, ClearML supports model deployment in various environments, including on-premises and cloud servers. You can use ClearML's model serving capabilities to handle predictions and integrate the models into your applications or workflows.

Q: Is ClearML open source? A: Yes, ClearML is an open-source platform that offers free and enterprise versions. You can access the source code and get started with ClearML for your machine learning projects.

Q: How does ClearML handle the tracking and management of multiple versions of datasets and models? A: ClearML's versioning capabilities allow you to manage and track multiple versions of datasets and models. Each new version is linked to the previous ones, enabling you to access the lineage and history of your data and models.

Q: Can I use ClearML with other machine learning frameworks besides Hugging Face Transformers and SKlearn? A: Yes, ClearML is compatible with various machine learning frameworks and libraries. You can integrate ClearML into your existing workflows and use it with frameworks such as TensorFlow, PyTorch, and more.