# text-classification Published articles for text-classification. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How we think about text classification in the LLM era DevFeed: [How we think about text classification in the LLM era]() Original publisher: [Read original article]() Author: Raphael Montaud Published: 2026-08-19T20:00:37Z Content type: article Language: en Sources: [Medium]() Topics: [Machine Learning & Artificial Intelligence](), [Model Development](), [LLM Techniques](), [Artificial Intelligence](), [AI Inference]() Tags: [ai](), [classification](), [inference](), [llm](), [llms](), [machine-learning](), [model](), [models](), [recommendation-system](), [text-classification]() ### AI overview Medium explains how it is evaluating LLM-based text classification for updating its aging NSFW model while retaining task-specific machine-learning models. The article states that Snowflake LLM tools were used for inference only and that Medium's user data was not used to train the models. ### Source excerpt Why we think LLMs can be useful and why we will not replace all of our models with themContext At Medium, we have many Machine Learning models that we use to label stories automatically. These affect what stories we recommend to readers. Here's some examples: a few of our text classification models. All diagrams and charts made by the authorSome Clarifications on our Machine Learning policy Before we go deep on this project, I just wanted to clarify a few things about how we stand regarding AI in general. Medium has been training internal models with user and post data for a long time now. We train models with specific tasks. For example, models that power our recommendations algorithm, or text classification models like the ones presented in this story. All in the goal to improve our product. With the LLM approach I describe in this story, we ARE NOT sharing these models with other companies. And we ARE NOT allowing anyone to train on our users' data and content. Here we used Snowflake LLM tools for inference only (no LLM training was done here) and they are actually hosting all of the models inside their own infrastructure and guarantee that they are not using any of this for training. Shoutout to the Snowflake team for making it so easy and safe to use LLMs on our data! If you want to read more about Medium's stance on AI, I definitely recommend giving these a read: Default No to AI Training on Your Stories Finally, an internet standard for writers' rights vs. AI companies We want your feedback: How can writers use AI to tell human stories? Problem During our roadmap planning we decided that our NSFW model was out of date and it was time to revamp it. This model labels stories as "Not Safe for Work" if they have sexually explicit content, lots of profanity, or basically anything you wouldn't want to read on your big monitor in the middle of an open space! As you can imagine it's a pretty important model. We really need it to make sure our most "interesting" conte ## Fine-tuning a DistilBERT classifier with numerical and text inputs DevFeed: [Fine-tuning a DistilBERT classifier with numerical and text inputs]() Original publisher: [Read original article]() Author: Paloma Jol Published: 2026-04-10T10:04:33Z Content type: tutorial Language: en Sources: [FreeAgent]() Topics: [Fine-tuning](), [Transformer](), [foundation-models](), [Training AI Models](), [datasets](), [Kaggle](), [scikit-learn]() Tags: [ai](), [aws](), [data](), [data-ml](), [data-science](), [dataset](), [encoding](), [fine-tuning](), [hugging-face](), [kaggle](), [llms](), [machine-learning](), [model](), [models](), [nlp](), [performance](), [text-classification](), [training-data](), [validation]() ### AI overview This tutorial explains how to fine-tune a DistilBERT text classifier while incorporating a numerical feature into the same network. It uses the Kaggle wine reviews dataset, combining review descriptions with price and preprocessing the data for training, validation, and testing. ### Source excerpt Text classification is often done through fine-tuning of a pretrained foundation model with domain-specific data. In FreeAgent we use transformer based models to automatically classify incoming bank transactions. Specifically we use a DistilBERT model that is fine-tuned on hundreds of millions of bank transactions with customer-labelled accounting categories. The model inputs are currently text-based, built from a combination of bank transaction descriptions and amounts. In this post we describe an approach to fine-tuning the DistilBERT model and training the classifier including the numerical amount feature as a single network. ## Category Prediction for Search Query Understanding DevFeed: [Category Prediction for Search Query Understanding]() Original publisher: [Read original article]() Author: music and waves Published: 2024-04-21T12:40:48Z Content type: article Language: en Sources: [Myntra]() Topics: [Query (disambiguation)](), [Training AI Models](), [datasets](), [data]() Tags: [ai](), [classification](), [data](), [deep-learning](), [ecommerce](), [information-retrieval](), [model](), [neural](), [nlp](), [search](), [text-classification](), [train](), [training]() ### AI overview This article describes Myntra's multi-label product-category classification model for understanding ambiguous search queries. It covers preparing query-and-category training data and training a neural text classifier to predict relevant categories for live searches. ### Source excerpt Navigating through online shopping platforms can sometimes feel like finding your way through a maze. Take the search bar, for example. You type in "winter upper wear," hoping to find the perfect jacket or cozy sweatshirt. But here's the tricky part: the search engine has to decipher what you mean. Is it jackets you're after? Or maybe sweatshirts? Or both? It gets even more confusing when you consider the overlapping categories. Kurtas can be standalone articles or part of kurta sets. And loafers? They could belong to formal shoes or casual shoes and certainly not sports shoes. See the challenge? To tackle this, Myntra uses a multi-label search to product category classification model. It's like having an assistant that can understand possible intents from your search query. So when you type in something like "whey," the model knows you might be looking for protein or health supplements. But here's the catch: search queries can be short and vague, and they often use words that don't directly match category names. People might search using different terms or even regional variations. So, the model needs to be clever enough to map those words to the right categories internally. The goal is to capture all possible intents without cluttering your search results with irrelevant stuff. After all, nobody likes sifting through pages of irrelevant products. It's a delicate balance between covering all bases and keeping things tidy. Solution The solution has 2 major components. I. Data preparation We prepare ( search query : categories ) data points to be consumed in training by the neural classifier. Ex. ( ethnic wear : kurta, sarees ) II. Training a neural model We train a neural multi-label text classifier that consumes the prepared training data which is used to predicts categories for search queries live. I. Data Preparation We generate the supervised text classification training data in form of a search query and its product category(s) as its labels. This set is enrich