Data processing with ML and LLM
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Updated
Jun 12, 2024 - Python
Natural language processing (NLP) is a field of computer science that studies how computers and humans interact. In the 1950s, Alan Turing published an article that proposed a measure of intelligence, now called the Turing test. More modern techniques, such as deep learning, have produced results in the fields of language modeling, parsing, and natural-language tasks.
Data processing with ML and LLM
Code for the paper : Black-Box Word-Level Text Boundary Detection in Partially Machine Generated Texts
Code for WASSA-2024-Task-1
This GitHub repository is a valuable resource for machine learning and Python enthusiasts. It includes a wide range of projects and tools, covering topics like Data Visualization, Data Analysis, ML, DL, Automation, NLP, Web Scraping, and more. Contributors are welcome to join and learn together in this supportive community. Happy coding!
📑 Galician corpus for misogyny detection
Julia implementation of Byte Pair Encoding for NLP
Exercises on Machine Learning
NLP based Twitter data sentiment analysis project
Predicting Orders With Power Of ML
HausaHate is a benchmark dataset for Hausa hate speech detection task. it was extracted from West African Facebook pages and comprises 2,000 comments annotated according to a binary class (offensive and non-offensive) and hate speech targets (race, gender and none).
Generating and applying expanded labelling categories to text data, derived from combinations of original labels to make more specific categories.
Emotion detection involves identifying and classifying emotions expressed in textual data. It combines techniques from Natural Language Processing (NLP) and Machine Learning (ML) to analyze and interpret human emotions, which can be applied in various domains like customer service, social media analysis, and mental health monitoring.
The information gathered and can be used for the upcoming projects of Sentiment analysis
This project aims to compare traditional Machine Learning methods for tabular data classification, such as Ensemble methods, Decision Trees, and Naive Bayes, with NLP classification methods like Multinomial Naive Bayes, RNNs, and Transformers. We are utilizing survey data from the CDC via the Behavioral Risk Factor Surveillance System (BRFSS)
Automatically generate multi-language subtitles using AWS AI/ML services. Machine generated subtitles can be edited to improve accuracy and downstream tracks will automatically be regenerated based on the edits. Built on Media Insights Engine (https://github.com/awslabs/aws-media-insights-engine)
Hello everyone this repo will contain my journey of machine learning and DeepLearning with some exciting projects
A platform enables sharing diverse knowledge, but similarly worded questions are common. We use NLP techniques to identify duplicate questions, enhancing user experience by making it easier to find high-quality answers.
Explore my Codsoft ML Internship tasks
ThreadMind is a sophisticated comment analysis tool that employs fine-tuned RoBERTa and XLNet models for real-time sentiment and emotion analysis on YouTube and Reddit. Built with ReactJS and Django, and deployed on Google Cloud Run, it features interactive HighCharts for metrics display.
KNIME - Text Processing Extension (Labs)
Created by Alan Turing