Tools made for usage alongside artistic style transfer projects
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Updated
Nov 22, 2019 - Python
Tools made for usage alongside artistic style transfer projects
Developed a sophisticated machine learning model capable of generating diverse interview questions aligned with specific topics, ensuring depth of conversation. Integrated advanced Natural Language Processing (NLP) algorithms to analyse spoken responses, identifying grammatical errors & offering accurate corrections after the interview.
A fine-tuned version of Phi-3-mini-4k-instruct for generating SQL queries from natural language prompts, utilizing synthetic datasets and QLoRA for efficient adaptation and deployment.
Fine grained sentiment analysis on App Reviews with deep learning.
Forecasting Energy Consumption with XGBOOST and Optuna Optimizer
series of practice examples on NLP task & workflow from data, embedding, to training & fine tuning
Fine Set is a collection of unique elements maintained as linked list. It uses fine grained locks, allowing pipelined traversal by threads.
This project is designed to classify YouTube comments as toxic or non-toxic using BERT (Bidirectional Encoder Representations from Transformers). By fine-tuning a pre-trained BERT model, we leverage state-of-the-art NLP capabilities to identify harmful content in online conversations.
Development of a Traffic Speed control record application.
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