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Data Labeling by Human Judgment and ML/LLM Automation
While AI is rapidly advancing, there are still critical tasks where human judgment and automation remain essential. Our data labeling services provide the nuanced accuracy necessary for complex projects. We understand that high-quality, precisely labeled datasets are the foundation for successful machine learning models. Our automated solutions and team of labelers meticulously annotate data across various modalities like text, images, and video.
Customers Rely on Our Data Labeling for Diverse Use Cases
Self-Driving Car: to recognize pedestrians, cars, and other objects.
Search Relevance: to evaluate if recommended entities are relevant to ecommerce keywords, brands and products.
Generative Text: to identifying grammatical errors, assessing coherence, detecting inappropriate content or bias, and evaluating factual accuracy. Also, to evaluate the relevance of the generated text to the seed entity.
Generative Image and Video: to capture the visual content, objects, scenes, and concepts depicted in the images, and their relevance to the seed entity.
Medical Image: to interpret medical images accurately and confidently, preparing them for real-world clinical practice.
Geospacial Analysis: to accomplish tasks related to satellite image analysis, land cover classification, geographic information systems (GIS), and location-based services.
Our Approach
Human Labeling: At Forteque, we employ a rigorous and meticulous approach to data labeling, ensuring the highest standards of accuracy and consistency. Our team of annotators undergoes comprehensive training to understand the specific labeling guidelines and criteria for each project. Whether it is image annotation, text classification, object detection, sentiment analysis, or any other task, we tailor our labeling process to meet the unique requirements of our clients.
Automated Labeling: We meet scalability and time efficiency with automated labeling. We leverage the power of trained large language models (ensemble of LLMs), cutting-edge machine learning models (e.g. classifiers), and advanced clustering techniques to automate data labeling tasks at scale. This intelligent automation unlocks significant time and cost savings while maintaining a high degree of accuracy. For example, in the retail sector, our automated labeling solutions can analyze massive volumes of customer reviews, extracting keywords and sentiment with precision. This allows businesses to gain real-time insights into product perception, market trends, and customer preferences, empowering them to make informed decisions for product development and marketing strategies. Our automated labeling expertise extends to diverse and complex domains. In self-driving car development, we meticulously label vast amounts of image and video data captured by sensors, aiding in the accurate identification of road signs, pedestrians, other vehicles, and potential hazards. This labeled data fuels machine learning models, enabling the autonomous vehicle to navigate safely and reliably. Similarly, in image recognition applications, our solutions automatically classify and tag images based on content, subject matter, and style. This streamlines image organization, search, and ultimately enhances user experience. Whether we employ ML models or LLMs to automate data labeling, we develop advanced metrics to measure the quality of the labels. Our stringent quality control measures and feedback mechanisms guarantee the consistency and accuracy of labeled data, mitigating risks and ensuring compliance with industry standards.
Our Strategy
Human Labeling Strategy: Our labeling services feature native-level expertise in English, Spanish, Chinese, Japanese, Arabic, and Persian. We offer flexible labeling strategies, including configurations of 1 labeler + 1 judge, 2 labelers + 1 judge, and 3 labelers + 1 judge, tailored to meet your specific requirements. Initially, we provide a pilot sample of the labeled dataset for your review. Once you approve the labels, we proceed with the full dataset. Our process doesn’t stop there. We also conduct a final review with you and provide comprehensive statistical reports on the labels, along with tailored recommendations based on your needs. We can quickly scale up to handle datasets that are up to 10 times larger, ensuring we meet your deadlines.
Automated Labeling: We utilize various methods for selecting a pilot dataset. For instance, we can select a random sample for labeling. Once both parties agree on the labels, we proceed with labeling the entire dataset. At your request, we will provide full transparency about our approach, ensuring that you are fully informed throughout the process. We will finally pass you the entire labeled dataset with tailored statistics.
First Batch is Free: To demonstrate our performance, we offer to label a batch of data free of charge for new customers. This allows our customers to experience firsthand our accuracy, timely responses, output quality, and level of communication.
Feedback Loop
Continuous feedback is integral to the data labeling process to maintain consistency and improve quality over time. We establish clear channels of communication with our clients to gather feedback on labeled data and address any concerns or revisions promptly. Additionally, our quality assurance measures include regular audits and reviews to identify and rectify any discrepancies or errors in the labeled data. This iterative feedback loop ensures that our labeling services consistently meet the evolving needs and expectations of our clients, resulting in superior outcomes for their machine learning initiatives.