Here's how we fine-tuned Qwen3 0.6B to beat FinBERT and match GPT-5.1 accuracy. Complete with open-source models, datasets, and training scripts. Spoiler alert: active learning is all you need.
We introduce the ensemble and distil data pattern and use it to fit an ordinary least squares linear regression that outperforms GPT-4 at financial news sentiment classification using sentence transformer embeddings as features.
A comparison of sentiment classifications made by TextBlob, VADER, Flair, SigmaFSA, FinBERT, FinBERT-Tone, Text-Bison, Text-Unicorn, Gemini-Pro, GPT-3.5, GPT-4, and GPT-4-Turbo. We look at accuracy, time, and cost and include a dataset of 10,368 labelled news stories (with code) for our followers.
