Top Tweets for #TextClassification
📝 Read #HighlyCitedArticle "Text Classification: How Machine Learning Is Revolutionizing Text Categorization" by Hesham Allam, et al.
See more details at:
https://t.co/z5XqQLaQaj
#TextClassification #MachineLearning #NLP #DataScience #AI
@ComSciMath_Mdpi
📝 Read #HighlyCitedArticle "Text Classification: How Machine Learning Is Revolutionizing Text Categorization" by Hesham Allam, et al.
See more details at:
https://t.co/z5XqQLaQaj
#TextClassification #ML #NLP #DataScience #AI
@ComSciMath_Mdpi
Mapping job-postings to a taxonomy of industrial sectors without finetuning: “No Train, No Pain? Assessing the Ability of #LLMs for #TextClassification with no Finetuning” by R. Fechner, J. Dörpinghaus. ACSIS Vol. 40 p. 9–16; https://t.co/uvIRmAXirC
📘 Day 46 – 100 Days of ML
🧠 Topic: Text Classification using Naive Bayes
📖 Learn with @geeksforgeeks Nation SkillUp
👉Course: https://t.co/WEOoRR2CvF
#100DaysOfML #MachineLearning #NaiveBayes #TextClassification #nationskillup #skillupwithgfg
This paper studies active learning with parameter‑efficient fine‑tuning (adapters), showing AL+PEFT improves PLMs in low‑resource text classification. - https://t.co/V2QAwuqv8O #textclassification #activelearning(al)
🚀 Introducing GLiClass‑V3 – a leap forward in zero-shot classification!
Matches or beats cross-encoder accuracy, while being up to 50× faster. Real-time inference is now possible on edge hardware.
https://t.co/2N5IU6GWOe
#TextClassification #NLP #ZeroShot #GLiClass
Do check out a basic AI agent I built for classifying text sentiment in real-time. https://t.co/sbrYF63ACc… #ArtificialIntelligence #AI #Agent #AgenticAI #HuggingFace #TextClassification #SentimentAnalysis
💡 Working on a Text Classification app using machine learning! Here’s a simple pipeline using scikit-learn to classify text data:
Anyone working on NLP or text classification projects? Let’s exchange ideas! 🚀
#MachineLearning #AI #NLP #TextClassification #AICommunity
"When you finally fix the bug and the model accuracy goes from 65% to 99%:"
#MachineLearning #AI #NLP #TextClassification #AICommunity
Check out the latest blog post on an optimized text classification algorithm based on graph neural networks. Learn how this approach enhances accuracy and efficiency in natural language processing. Read the full paper at: https://t.co/E1w67Pl9Vw #textclassification #GNN #NLP
Can TensorFlow classify my texts into categories like "texts that make me smile" and "texts that make me roll my eyes"?
Source: https://t.co/aplQL5lo9e
#TextClassification #TechTuesday #Influencer #Tech
5/: This dataset is ready for use in various Natural Language Processing tasks, including text classification and regression, offering a valuable resource for future studies. 🛠️🔍 #NLP #TextClassification
https://t.co/2wx3esp29o
Evaluating Large Language Models for Health-Related Text Classification Tasks with Public Social Media Data
#NLProc #textclassification #socialmedia #gpt4 #LLM
What’s the best way to use large language models (#LLMs) for #textclassification? Find out in the wrap-up of our blog series! Learn about supervised vs. unsupervised learning methods (and whether or not you can just use #ChatGPT). https://t.co/VpspLSGFza
#AI #MachineLearning
Learn how to automatically organize, structure, and categorize large amounts of data in minutes, TODAY, for free using Appsmith AI.
#ai #artificialintelligence #artificialintelligenceforbusiness #enterprise #classifytext #textclassification
https://t.co/6iJRNIEnr8
Large language models drive generative #AI. But how do they hold up for text classification? To find out, our R&D team put them to the test. Read our blog post to find out which supervised method reigns supreme: https://t.co/UOyPi8K3gn #TextClassification #LLMs #MachineLearning
I'm happy to share that our paper "Sentiment Analysis in Finance: From Transformers Back to eXplainable Lexicons (XLex)" has just been published in the @IEEEAccess journal.
Paper: https://t.co/b8CmrjcDDR
#SentimentAnalysis #ML #NLP #TextClassification #Finance #Lexicons
Parlez-vous français? The French Web 2023 corpus with 23.8 billion words now available! The texts were carefully cleaned and classified into genres (blog, news, …) and topics (arts, health, …). https://t.co/0HaqiQkhpm
#corpuslinguistics #digitalhumanities #TextClassification
In this article, our #machinelearning team explains the current SOTA approach for Few-Shot fine-tuning of #textclassification models.
Our approach is based on a classification model that was trained to distinguish whether two statements are neutral, contradict each other, or entail. Such a process is called Natural Language Inference.
With just 8 examples per label, it achieves up to 90% accuracy due to our unique few-shot learning technique, making our approach even more effective than Setfit's (previous SOTA).
Simply put, we predict the probability that a given label in a given context is a real class we are looking for. Because, in comparison to bi-encoders, we process classes and text through a single attention mechanism, we can consider a given class as a part of the context itself.
So, actually, from a mathematical standpoint, at some level, we do the same as described for conventional models, but we always have 3 classes: entitlement, neutrality, and contradiction.
The difference and actually where interesting behavior emerges is that we can artificially normalize the probabilities of a label emerging in entailed and contradictory contexts, minimizing a bias of a model to prefer just more frequent labels.
https://t.co/DEKznByqIr
🔔New Research by Mr. Hiromu Nakajima and Dr. Minoru Sasaki :
"Text Classification Based on the Heterogeneous Graph Considering the Relationships between Documents" @ComSciMath_Mdpi
#textclassification #CNN #semisupervisedlearning
Access for Free:
https://t.co/25qPMFoccP
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