AI Daily PostNov 18

Want to turn raw reviews, tweets, and feedback into clear sentiment signals? Learn how to fine‑tune BERT with Hugging Face, master tokenization, and build a transformer‑based text classifier in minutes. The guide walks you through every step, from data prep to evaluation. Perfect for open‑source lovers and NLP hobbyists! #BERT #HuggingFace #NLP #TextClassification

🔗 https://aidailypost.com/news/learn-classify-reviews-tweets-feedback-bert-hugging-face

HackerNoonAug 25, 2025
PEFT with AL selects more valuable data, preserves stable early‑layer representations, reduces forgetting, and outperforms FFT in low‑resource settings. https://hackernoon.com/why-peft-beats-fft-in-active-learning-forgetting-dynamics-and-stable-representations #textclassification
Why PEFT Beats FFT in Active Learning: Forgetting Dynamics and Stable Representations | HackerNoon

PEFT with AL selects more valuable data, preserves stable early‑layer representations, reduces forgetting, and outperforms FFT in low‑resource settings.

HackerNoonAug 25, 2025
Experiments show PEFT, especially Prefix‑tuning and UniPELT, outperform FFT in low‑resource text tasks and remain strong in AL setups, boosted further by TAPT. https://hackernoon.com/who-learns-faster-with-less-data-adapters-beat-full-finetuning #textclassification
Who Learns Faster With Less Data? Adapters Beat Full Fine‑Tuning | HackerNoon

Experiments show PEFT, especially Prefix‑tuning and UniPELT, outperform FFT in low‑resource text tasks and remain strong in AL setups, boosted further by TAPT.

HackerNoonAug 25, 2025
This paper studies active learning with parameter‑efficient fine‑tuning (adapters), showing AL+PEFT improves PLMs in low‑resource text classification. https://hackernoon.com/teaching-big-models-with-less-data-how-adapters-active-learning-win #textclassification
Teaching Big Models With Less Data: How Adapters + Active Learning Win | HackerNoon

This paper studies active learning with parameter‑efficient fine‑tuning (adapters), showing AL+PEFT improves PLMs in low‑resource text classification.

Jesus Castagnetto 🇵🇪Aug 16, 2025

About the importance of just one word in biasing text classification

"A new way to test how well #AI systems classify text"

#LLM #TextClassification

https://news.mit.edu/2025/new-way-test-how-well-ai-systems-classify-text-0813

A new way to test how well AI systems classify text

Automated online conversations made by text classifiers are becoming more prevalent. Now, an MIT team led by Kalyan Veeramachaneni has come up with an innovative approach to not only measuring how well these classifiers are doing their job, but going one step further to make them more accurate.

MIT News | Massachusetts Institute of Technology
CoListyJan 16, 2025

Generative AI Using SAS: Explore Machine Learning Techniques | CoListy
Learn the basics of Generative AI with SAS, including SMOTE, GANs, and LLMs to generate synthetic data and improve AI accuracy.
#freeonlinelearning #colisty #courselist #generativeai #machinelearning #datascience #sasviya #gans #smote #largelanguagemodels #bert #ai #syntheticdata #textclassification #rag #sasprogramming.

https://colisty.netlify.app/courses/generative-ai-using-sas-explore-machine-learning-techniques/

Generative AI Using SAS: Explore Machine Learning Techniques

Learn the basics of Generative AI with SAS, including SMOTE, GANs, and LLMs to generate synthetic data and improve AI accuracy.

Axel PichlerSep 14, 2024

Just compared Claude-Sonnet-3.5 with OpenAI's o1 on a CLS task – classifying text inputs from US short stories with regard to focalization. Turns out, Sonnet doesn't recognize zero focalization and achieved an F1-score of 0.47, while o1 performed better with 0.69. Not bad - but problematic, as the hidden tokens of the optimizer (?) from o1 would be of particular interest.

#CLS #AI #ClaudeSonnet #OpenAI's_o1 #TextClassification #Focalization

hexaheximalJul 25, 2024

I've started making a text classification library in Go. It's VERY WIP, but I think that there could be some interesting results once I add things to it. (and maybe redesign the entirety of it if needed in the future)

Source code: https://codeberg.org/autodni/silt

#Go #GoLang #Language #NLP #Classification #TextClassification

silt

A minimalistic text classification library.

Codeberg.org
Data BloggerMar 14, 2024
Generative or Discriminative? How does Multinomial Naive Bayes work for text classification? 📚🐍🤔 #NaiveBayes #TextClassification #DataScience https://towardsdatascience.com/multinomial-naive-bayes-classifier-c861311caff9
Multinomial Naive Bayes Classifier - Towards Data Science

In this new post, we are going to try to understand how multinomial naive Bayes classifier works and provide working examples with Python and scikit-learn. The first important step to understand the…

Towards Data Science
jprbarryApr 16, 2023
Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial #Python #TensorFlow #MachineLearning #NeuralNetwork #TextClassification
https://www.youtube.com/watch?v=VtRLrQ3Ev-U
Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial

YouTube