Efficient and Scalable Estimation of Tool Representations in Vector Space

Finetuning the DeBERTa v3 model for the emotion recognition task

Detect or classify input sentences as grammatically correct or incorrect by fine-tuning pre-trained DeBERTa-v3 model

Application for training the pretrained transformer model DeBERTaV3 on an Aspect Based Sentiment Analysis task

Official repository for the EMNLP 2024 paper "How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics"

Challenge to distinguish whether a sentence from a news article expresses the subjective view of the author behind it or presents an objective view on the covered topic

Finding the source code hidden in the text.

Educational workshop for NLP engineers. Fine-tuning DeBERTa-v3 for CEFR level prediction on serverless Modal GPUs.

Submodular Subset Selection for Long-Document Question Answering

Backbone: 5 x DeBerta and Head: Rapids SVR

Tactical next-action + reasoning prediction on 348 football match contexts (Shipd Project Eris). 4-component ensemble with task-coupling: DeBERTa-v3-base / large, cross-encoder MCQ scorer, zero-shot NLI, and a three-pass Qwen3.5-35B-A3B-Int4 + Gemma-4-26B-A4B-it MoE fusion with PRM rerank. W&B-instrumented. Target combined ≥ 0.80

This repo details code for building a text classifier for predicting Bank Transaction categories. I finetune a base version of a DeBERTaV3 model purely on text data, as well as another version using a combination of text and non-text (e.g., categorical, datetime, etc.) data.

IELTS Automated Essay Scoring (AES) – Multi-Task DeBERTa-based Architecture

Tools for DataScience and AI

Sentiment Classification on IMDb Reviews Using Transformer Fine‑Tuning (DeBERTaV3), Bidirectional RNNs with GloVe Pretrained Embeddings, BiGRU with WordPiece, and Classic ML

Teknofest 2023 Doğal Dil İşleme Yarışması

A generative AI project that builds a self-improving reasoning agent using multiple AI models and agentic workflows. The system generates answers to complex tasks, evaluates its own reasoning through a critic model, and refines responses iteratively to produce more reliable outputs.

ViBERTa is a fine-tuned DeBERTa model for sentiment analysis of McDonald's customer reviews, classifying sentiments as positive, negative, or neutral.

My pipeline for the Feedback Prize - Predicting Effective Arguments competition on Kaggle