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Freelancer

Freelancer

Ai trainer, NLP and conversationalist

Nigeria flagAbuja, Nigeria

Key Skills

Software

No software listed

Top Subject Matter

Matter areas are Artificial intelligence
Machine learning
Data labeling.

Top Data Types

ImageImage
VideoVideo
TextText

Top Task Types

Point/Key PointPoint/Key Point
RelationshipRelationship
ClassificationClassification
Emotion RecognitionEmotion Recognition
Object DetectionObject Detection
Action RecognitionAction Recognition

Freelancer Overview

My experience with AI training and data labeling spans various industries and applications. Ai training and Data labeling are important components of developing an effective machine learning models. Ai training involves feeding large datasets to algorithms, allowing them to learn patterns and relationships within the data. This process requires massive amount of high quality labeled data to ensure models generalize well to new, unseen data. Data Labeling is a process of annotating data. It can text, images, audio or video with relevant tags and classifications. . In NLP, labeled corpora like those used for sentiment analysis or named entity recognition have powered virtual assistants . Some challenges persist like dealing with biased datasets, ensuring label consistency, and scaling labeling efforts. Techniques like active learning especially where models request labels for uncertain samples and transfer learning like leveraging pre-trained models, help optimize the labeling process. As AI expands into specialized domains, expert labeling becomes critical. Balancing accuracy, cost, and speed in data labeling remains a key hurdle and opportunity in AI development.

Labeling Experience

I worked on a project on Sentiment Analysis for Customer Reviews

I worked on a project on Sentiment Analysis for Customer Reviews. I Labeled 10,000 product reviews as Positive, Negative, or Neutral. It was on Amazon product reviews (text data). Annotators received guidelines on sentiment labeling. For example “I love this product" which is Positive Annotators labeled a test batch, discussed edge cases (sarcasm), and aligned on rules. Reviews were split among annotators; each review got 3 labels to ensure agreement. Disagreements (1 Positive, 2 Neutral) were reviewed by a senior annotator. I faced some Challenges like Customers that make use of Sarcasm/subtlety like “Great, just what I needed" (could be Positive or Negative). Mixed sentiment like “The camera is great but battery life sucks" (Neutral). Domain-specific terms "This mic is fire" (Positive slang). High agreement post-discussion; model trained on this data achieved 85% accuracy in predicting sentiment.

Not specified

Education

B

BE.d Education and Political Science

BE.d Education and Political Science

BE.d Education and Political Science
Not specified

Work History

C

Company not specified

AI Data Trainer | Remote / Contract 2024 – Present I Rated and ranked AI model outputs for accuracy, safety, and instru

Location not specified
Not specified