Omoyeni A.

Key Skills

Software

Other

Top Subject Matter

AI training datasets (text, sentiment/intent/topic, and moderation-style labeling)

AI training datasets (multimodal description/transcription and quality/safety review)

RLHF (Reinforcement Learning from Human Feedback) evaluation labels

Top Data Types

Text

Audio

Image

Top Task Types

Classification

Transcription

RLHF

Freelancer Overview

AI Trainer & Data Labeler (OpenTrain.ai services incl. text annotation/classification). Core strengths include Other. AI-training focus includes data types such as Text and Audio and labeling workflows including Classification, Transcription, and RLHF.

Labeling Experience

AI Trainer & Data Labeler (OpenTrain.ai services incl. RLHF response rating/ranking)

OtherTextRLHF

The labeling work includes RLHF-style evaluation by rating and ranking responses for preference learning. Annotated judgments are produced according to rubric-based instructions to ensure consistent and reliable comparisons. The output supports reinforcement learning from human feedback workflows. • Response rating for preference signals. • Response ranking between candidate outputs. • Following RLHF labeling rubrics and guidelines. • Ensuring label consistency across evaluation items.

Present

AI Trainer & Data Labeler (OpenTrain.ai services incl. audio transcription/description)

OtherAudioTranscription

The labeling work includes generating accurate transcription and descriptive annotations for audio and other media inputs. Tasks are executed against clear instructions to produce consistent, high-quality training data. Quality control is emphasized to minimize errors and maintain dataset reliability. • Audio transcription tasks. • Audio description/annotation. • Description support for media inputs used in training. • Applying quality review checks before submission.

Present

AI Trainer & Data Labeler (OpenTrain.ai services incl. text annotation/classification)

OtherTextClassification

The AI training and data labeling work involves annotating and classifying textual inputs according to provided instructions and dataset guidelines. The labeling tasks support downstream machine learning objectives such as intent/sentiment/topic understanding and training-data quality needs. The process includes careful review to ensure label consistency and correctness. • Sentiment labeling and classification. • Intent labeling and classification. • Topic labeling and classification. • Following annotation guidelines for consistent outputs.

Present

Education

Work History