Ihechi Anita Membere

Key Skills

Software

Appen

Clickworker

Data Annotation Tech

OneForma

Remotasks

Scale AI

Telus

Other

Don't disclose

Top Subject Matter

Chatbot prompt generation in English and Spanish

Image Preference and Segmentation

Audio/Video Transcription

Top Data Types

Audio

Document

Text

Top Task Types

Audio Recording

Data Collection

Evaluation Rating

Segmentation

Text Generation

Freelancer Overview

I have extensive experience in data labeling and AI training data, particularly through my roles as a Data Annotator/Reviewer at E2f Inc. and as an Independent Contractor with APPEN. In these positions, I was responsible for analyzing and categorizing diverse datasets to support machine learning models, focusing on maintaining high accuracy rates while adhering to tight deadlines. My work involved labeling text, images, and audio data according to specific project guidelines, which honed my attention to detail and ability to collaborate effectively with diverse teams. Additionally, my role as a Transcriber at Sigma.ai further enhanced my skills in data handling and quality assurance, where I consistently exceeded client expectations by delivering accurate transcriptions within established timeframes. My educational background in pharmacy equips me with strong analytical skills and a methodical approach to problem-solving, making me well-suited for projects requiring precision and critical thinking. Coupled with my ability to communicate clearly and implement feedback effectively, I am well-prepared to contribute to high-quality AI training data initiatives.

Labeling Experience

Data Evaluation Annotator/Reviewer

Don T DiscloseImageClassificationQuestion Answering

In this context, Image annotation involved selecting and assessing images based on specific criteria to enhance the quality and effectiveness of machine learning models. This process included determining the relevance of images for a given task, such as object recognition or segmentation, and ensuring that the annotated data aligns with project goals. Key techniques used in this evaluation included object detection, spatial relationships and PII's which identified and labelled objects within images using methods like bounding boxes. I assessed the quality of annotations by reviewing accuracy, consistency, and adherence to guidelines. This comprehensive approach not only ensured that the training data was robust but also enhanced the model's ability to generalize across various real-world applications.

In this context, Image annotation involved selecting and assessing images based on specific criteria to enhance the quality and effectiveness of machine learning models. This process included determining the relevance of images for a given task, such as object recognition or segmentation, and ensuring that the annotated data aligns with project goals. Key techniques used in this evaluation included object detection, spatial relationships and PII's which identified and labelled objects within images using methods like bounding boxes. I assessed the quality of annotations by reviewing accuracy, consistency, and adherence to guidelines. This comprehensive approach not only ensured that the training data was robust but also enhanced the model's ability to generalize across various real-world applications.

2024 - 2024

Audio Annotation

OtherAudioSegmentationEmotion Recognition

The scope of an audio transcription and segmentation project involved converting unstructured audio data, such as recordings of human speech, into structured text while also identifying and labeling different segments of the audio. This process involved accurately converting spoken language into written text, which also involved distinguishing between different speakers and including timestamps for each segment. Also, Identifying the onset and offset of speech segments, which include words and categorizing audio into relevant units such as speech, music, or silence. Quality measures adhered to during this project include: feedback mechanisms and several accuracy checks.

The scope of an audio transcription and segmentation project involved converting unstructured audio data, such as recordings of human speech, into structured text while also identifying and labeling different segments of the audio. This process involved accurately converting spoken language into written text, which also involved distinguishing between different speakers and including timestamps for each segment. Also, Identifying the onset and offset of speech segments, which include words and categorizing audio into relevant units such as speech, music, or silence. Quality measures adhered to during this project include: feedback mechanisms and several accuracy checks.

2023 - 2024

Image Segmentation

Scale AIImageBounding BoxPolygon

The project scope for image annotation and segmentation of vehicles and medical images involved a detailed process aimed at enhancing machine learning models through precise labeling. The project focused on annotating images to identify and classify various objects such as cars, road signs, and pedestrians. In the context of medical images, the project encompassed annotating X-rays, MRIs, and CT scans for tasks such as organ segmentation. Techniques like bounding boxes and polygonal segmentation were used.

The project scope for image annotation and segmentation of vehicles and medical images involved a detailed process aimed at enhancing machine learning models through precise labeling. The project focused on annotating images to identify and classify various objects such as cars, road signs, and pedestrians. In the context of medical images, the project encompassed annotating X-rays, MRIs, and CT scans for tasks such as organ segmentation. Techniques like bounding boxes and polygonal segmentation were used.

2021 - 2022

Education

Work History