Data labeling is an important step in your data science or machine learning practice. By adding meaningful information to your data through labeling, you can improve the accuracy of your models, identify and remove bias, and improve the efficiency of your machine learning and data science operations.
Label Studio is a data labeling tool for labeling, annotating, and exploring many different data types. Additionally, the tool includes a powerful
-learning interface that can be used for new model training, active learning, supervised learning, and many other training techniques.
Label studio fits into this process:
Data preparation: Clean, format, organize, and label data for training.
Model selection and training: Choose an appropriate model, and train it using prepared data.
Model evaluation: Assess model performance on validation and test data.
Model fine-tuning: Adjust the model based on evaluation results, potentially requiring new labeled data.
Model deployment: Deploy the trained model into production.
Continuous monitoring and updating: Regularly monitor model performance, and update with new labeled data as needed.
Technical Usage Manual
1. After subscribing to the AMI for Label-Studio-AMI from AWS Marketplace Choose the launch through EC2 and launch
2. It redirects to the launch instance page then configures the required details i.e., Name, Instance type, Keypair, and Network Setting, and configures Storage and launches the Instance
3. Go to the EC2 Dashboard and select your created instance. Copy the Public IP
4. Provide the Public IP with :8080in your browser (Eg: http://<Public IP>:8080) Eg:http://3.96.208.170:8080
5. Log in to your Label Studio account and create a new project. Give it a name and describe its purpose
6. Upload your dataset by clicking on the Data Import button, then selecting 'Upload More Files' and choosing your dataset file. After uploading the file choose List of Tasks
Note: I have stored this text and imported it as the file to Label-Studio is
The dog caught the ball.
The cat is chasing the mouse.
The bird flew to its nest.
7. Click 'Labeling setup' and select the appropriate annotation type for your data. In this example, we choose Natural Language Processing and Named Entity Recognition.
8. Delete all the existing (default) labels and create two new ones, the animal and object label
9. Manually label your data by selecting the appropriate label for each data point. For text data, highlight the relevant words and assign them to the correct label category.
10. Return to the main project screen and click on the export button to export your annotations in your preferred format. You can export the annotated data in various formats such as JSON, CSV, TSV, etc.
11. Use the annotated data to train your AI models for tasks such as natural language processing. Ensure you have separate testing datasets for evaluating your model's accuracy.
Insights & Support:
For further details about Label Studio - Data, Image, and Image Annotation Enterprise Solutions and its uses, refer to the Labelstudio.io website
We will do our best to respond to your questions within the next 24 hours in business days. For any technical support or query, you can drop a mail to support@yobitel.com.
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