Anita Adims

Key Skills

Software

Remotasks

Labelbox

Appen

Top Subject Matter

General English-language text

social media content

and instructions

Top Data Types

Text

Image

Audio

Document

Top Task Types

Entity Ner Classification

Bounding Box

Transcription

RLHF

Freelancer Overview

Data Labeling Specialist & AI Training Contributor. Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Remotasks, Labelbox, and Rev. Education includes Bachelor of Arts, Abia State University. AI-training focus includes data types such as Text, Image, and Audio and labeling workflows including Entity (NER) Classification, Bounding Box, and Transcription.

Labeling Experience

Data Labeling Specialist & AI Training Contributor

AppenTextRLHF

Contributed to Reinforcement Learning from Human Feedback (RLHF) projects by ranking and evaluating AI-generated responses. Applied detailed annotation guidelines to assess helpfulness, accuracy, safety, and adherence to provided instructions. Supported model improvement initiatives through precise and consistent response evaluation.• Ranked large batches of ChatGPT and Claude AI outputs for instructional quality. • Applied safety and policy compliance checks during response judgment. • Used efficacy benchmarks for differentiating high and low-quality completions. • Provided real-time feedback that contributed to model iteration cycles.

Contributed to Reinforcement Learning from Human Feedback (RLHF) projects by ranking and evaluating AI-generated responses. Applied detailed annotation guidelines to assess helpfulness, accuracy, safety, and adherence to provided instructions. Supported model improvement initiatives through precise and consistent response evaluation.• Ranked large batches of ChatGPT and Claude AI outputs for instructional quality. • Applied safety and policy compliance checks during response judgment. • Used efficacy benchmarks for differentiating high and low-quality completions. • Provided real-time feedback that contributed to model iteration cycles.

2026 - Present

Data Labeling Specialist & AI Training Contributor

AudioTranscription

Transcribed and labeled over 40 hours of audio data, including multi-speaker interviews and customer service calls. Applied speaker diarisation, timestamp formatting, and punctuation for high-quality dataset preparation. Followed annotation schemes for differentiating speakers and ensuring transcription accuracy for model training purposes.• Processed interviews and call center recordings with accurate speaker tags. • Used Otter.ai and Rev for semi-automatic and manual audio transcription. • Applied detailed guidelines for punctuation and utterance segmentation. • Ensured dataset completeness through structured review workflows.

Transcribed and labeled over 40 hours of audio data, including multi-speaker interviews and customer service calls. Applied speaker diarisation, timestamp formatting, and punctuation for high-quality dataset preparation. Followed annotation schemes for differentiating speakers and ensuring transcription accuracy for model training purposes.• Processed interviews and call center recordings with accurate speaker tags. • Used Otter.ai and Rev for semi-automatic and manual audio transcription. • Applied detailed guidelines for punctuation and utterance segmentation. • Ensured dataset completeness through structured review workflows.

2026 - Present

Data Labeling Specialist & AI Training Contributor

LabelboxImageBounding Box

Executed image annotation tasks by drawing bounding boxes for object detection datasets, encompassing over 1,000 images of vehicles, pedestrians, and street signs. Focused on precise coordinate placement and adherence to platform-specific taxonomy standards. Enhanced dataset utility for machine learning model development through quality-focused annotation practices.• Labeled street scene images with bounding boxes for object detection projects. • Annotated vehicles, pedestrians, and street/traffic signs in large image batches. • Utilized Remotasks and Labelbox tools for annotation and quality review. • Achieved high precision with structured QA and coordinate verification systems.

Executed image annotation tasks by drawing bounding boxes for object detection datasets, encompassing over 1,000 images of vehicles, pedestrians, and street signs. Focused on precise coordinate placement and adherence to platform-specific taxonomy standards. Enhanced dataset utility for machine learning model development through quality-focused annotation practices.• Labeled street scene images with bounding boxes for object detection projects. • Annotated vehicles, pedestrians, and street/traffic signs in large image batches. • Utilized Remotasks and Labelbox tools for annotation and quality review. • Achieved high precision with structured QA and coordinate verification systems.

2026 - Present

Data Labeling Specialist & AI Training Contributor

RemotasksTextEntity Ner Classification

Performed over 500 text annotation tasks on major annotation platforms, focusing on entity, sentiment, intent, and relational labeling within English-language documents. Maintained an average annotation quality score of 97% through rigorous application of guidelines and inter-annotator agreement workflows. Ensured high throughput and accuracy by leveraging taxonomy and ontology knowledge in document labeling.• Completed high-accuracy Named Entity Recognition (NER) and sentiment analysis labeling tasks on Remotasks and Appen. • Applied relationship and intent classification frameworks to diverse text datasets. • Used inter-annotator agreement and quality assurance checklists for error reduction. • Maintained Trusted Contributor status via consistent quality and performance benchmarks.

Performed over 500 text annotation tasks on major annotation platforms, focusing on entity, sentiment, intent, and relational labeling within English-language documents. Maintained an average annotation quality score of 97% through rigorous application of guidelines and inter-annotator agreement workflows. Ensured high throughput and accuracy by leveraging taxonomy and ontology knowledge in document labeling.• Completed high-accuracy Named Entity Recognition (NER) and sentiment analysis labeling tasks on Remotasks and Appen. • Applied relationship and intent classification frameworks to diverse text datasets. • Used inter-annotator agreement and quality assurance checklists for error reduction. • Maintained Trusted Contributor status via consistent quality and performance benchmarks.

2026 - Present

Education

Work History