SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit
Hugging Face
Hugging Face and Intel Labs built SetFitABSA, a tool that spots what people liked or hated about specific product features from just a few examples. It beats Llama2 and T5 in few-shot tests while being way smaller and needing no prompts.
Based on reporting by Hugging Face — read the original for the full story.
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Aspect-based sentiment analysis sounds fancy but the idea is simple: figure out not just whether a review is positive, but positive about what. "The screen is great but the battery dies fast" has two aspects and two opposite opinions. Getting a model to reliably pull that apart has usually meant either painstaking token-level labeling or leaning on giant generative models with hand-tuned prompts. Intel Labs and Hugging Face think they've found a shortcut.
SetFitABSA breaks the job into three steps. First, spaCy scans the text and grabs nouns and noun compounds as candidate aspects. Then a small SetFit classifier decides which of those candidates are actual aspects versus noise, by literally gluing the candidate word onto the full sentence and training a binary yes/no model on that pairing. A second SetFit model does the same trick to assign a sentiment label to whatever survived the first filter. No token-tagging tools, no prompt engineering, just labeled sentence pairs.
The results are the interesting part. Tested on the SemEval14 Laptop and Restaurant datasets against T5-base, GPT2-medium and Llama-2-chat, SetFitABSA held its own or won outright in low-data settings, despite using two 110-million-parameter models stacked together rather than anything close to Llama 2's 7 billion parameters. The gap was especially stark against GPT2 and T5. And oddly, throwing more in-context examples at Llama2 didn't help it catch up, echoing a quirk researchers have noticed with ChatGPT too — more examples doesn't automatically mean better few-shot performance for these generative models.
What makes this practical rather than just a benchmark flex is the training cost. The whole pipeline, both submodels, trains in about ten minutes on a free Colab T4 GPU, using something like 128 labeled examples pulled from a restaurant review dataset. You install setfit with an absa extra, grab spaCy's en_core_web_lg model, format your data as text/span/label/ordinal rows, and run a few lines through AbsaTrainer. Compared to the labeling headache of tagging every token in BIO format, just marking which spans are aspects and what sentiment they carry is a much smaller lift for anyone building a domain-specific model — say, for hotel reviews or software complaints — without a research budget.
My take — AI-written commentary, not fact-checked reporting
This is the kind of release I actually get excited about: not a bigger model, a smarter shortcut. Everyone's chasing billion-parameter generative models for tasks that a 110M-parameter classifier can nail with less data and zero prompt fiddling, and SetFitABSA is a good reminder that efficiency research still has plenty of room to embarrass the scaling-obsessed crowd. If you're a small team building something niche in Europe without GPU clusters to burn, this is the sane way to do it.
Read more about this at: Hugging Face