YPYogeshwar Peelainexploitnotes.hashnode.dev·5d ago · 4 min readBroncoCTF : Spot the difference WriteupChallenge We're given two files, file1.txt and file2.txt, each containing what looks like a long, random blob of letters, digits, and symbols — one character per line. At a glance the two files look i00
BBuraqinburaqwrites.hashnode.dev·May 11 · 19 min readEveryday Text Problems and How to Fix ThemIntroduction: You Are Not Alone Let's be honest. Text is simple, right? You type letters, they appear on screen. Done. Except when it's not. You paste something from a website and suddenly there are w10
BBuraqinburaqwrites.hashnode.dev·May 10 · 9 min readStop Doing These 5 Text-Cleaning Tasks Manually (There's a Better Way)Hey there. Let me guess what you've been doing. You copy something from a website. Maybe a product description. Maybe an article you're quoting. Maybe some code from Stack Overflow. And then... the ni10
SSShivankur Sharmainshivankur018.hashnode.dev·Apr 30 · 4 min readInfosys Springboard: PaperIQOverview As part of the Infosys Springboard DSAI Virtual Internship, I worked on building PaperIQ, an end-to-end intelligent system designed to analyze research papers and convert them into structured00
FCFederico Calòinfedericocalo.hashnode.dev·Mar 20 · 1 min read10 - Monitoring NLP: Detecting Drift and Scheduling RetrainingComplete guide to Monitoring NLP: Detecting Drift and Scheduling Retraining: architecture, practical implementation and best practices for developers and technical teams. What you'll learn Types of Drift in NLP Models Structured Prediction Loggi...00
FCFederico Calòinfedericocalo.hashnode.dev·Mar 20 · 1 min read09 - Semantic Similarity: Measuring Text RelevanceComplete guide to Semantic Similarity: Measuring Text Relevance: architecture, practical implementation and best practices for developers and technical teams. What you'll learn Use SBERT instead of standard BERT for semantic similarity (Pearson 0.87...00
FCFederico Calòinfedericocalo.hashnode.dev·Mar 20 · 1 min read07 - HuggingFace Transformers: Models, Datasets, TrainingComplete guide to HuggingFace Transformers: Models, Datasets, Training: architecture, practical implementation and best practices for developers and technical teams. What you'll learn Use AutoClass to load any architecture with the same code The Pip...00
FCFederico Calòinfedericocalo.hashnode.dev·Mar 20 · 1 min read06 - Text Classification: Single and Multi-label ApproachesComplete guide to Text Classification: Single and Multi-label Approaches: architecture, practical implementation and best practices for developers and technical teams. What you'll learn Text Classification Taxonomy Multi-class Classification wit...00
FCFederico Calòinfedericocalo.hashnode.dev·Mar 20 · 1 min read05 - Named Entity Recognition: Extracting Information from TextComplete guide to Named Entity Recognition: Extracting Information from Text: architecture, practical implementation and best practices for developers and technical teams. What you'll learn 1.1 The BIO Format 1.2 NER Benchmarks and Datasets 2.1 Out-...00
FCFederico Calòinfedericocalo.hashnode.dev·Mar 20 · 1 min read04 - Italian Sentiment Analysis: BERT Models and ChallengesComplete guide to Italian Sentiment Analysis: BERT Models and Challenges: architecture, practical implementation and best practices for developers and technical teams. What you'll learn Use feel-it as a starting point for Italian sentiment and emoti...00
