A Show HN author reports 94.25% accuracy on all 3,080 Banking77 test examples using frozen bge-large-en-v1.5 embeddingA list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.Full definition → plus a trained logistic-regression classifier, close to the 94.86% cited for the state-of-the-art IntenDD.
Only the classifier is trained: it and its scaler total 642 KB and train in about 3 seconds on a CPU. With the much smaller all-MiniLM-L6-v2 encoder (about 91 MB), accuracy is still 93.28%, per the author.
In the author's comparison, the approach edges out fine-tuned ModernBERT (93.99%) and BERT (92.76%), which need GPU fine-tuningTaking a trained model and training it a bit more on your own examples so it gets better at one specific job.Full definition →, and Jev's zero-shot 87.0% through a closed API.
The author calls it a proof of concept and is now exploring a hybrid: local classifiers handle familiar decisions, an LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → is consulted when needed, and its answers are used to improve the classifiers automatically.
Terms in this piece · Glossary
embedding — A list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.
