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TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations
2025-05-29
Summary
The authors propose TabSTAR, a foundation model that brings together text and
table structure. Similar to TP-BERTa , each cell is
transformed into a single token that encompasses both the cell value and the
column name. The authors introduce a unified classification head that
allows for cross-table training without learning task-specific heads.
Approach
Architecture
Verbalization
Serialize the cells into a some text, e.g. "column_name: cell_value".
For numericals, apply binning (10 quantiles), and use the scale-aware text
for the text part (e.g. Age: 40–50 (Quantile 50–60%)) - Also, learn
magnitude-aware embeddings for each bin.
Text Encoding
Use e5 model to get the embeddings of the tokens and apply attention.
The e5 model is also lightly fine-tuned (top \(N\) layers).
A single layer transformer block is then used to now "fuse" the embeddings
into a single one. (similar to IFA module)
Classification
The sequence, where each token is a cell, is then passed through a 6-layer transformer that maps the "interaction" between the cells.
All possible labels are added to the input sequence after all the row information. This allows each label to contain the "context" of the input.
The label tokens of the resulting sequence are then used as input to a linear classifier head.
The single linear layer outputs the "score" for each label class.
Experiments
Training Data
Manually curated 350 tabular datasets (253 classification, 97 regression
Evaluation Data
AutoML Multimodal Benchmark
Benchmark from CARTE
Baselines
TabPFNv2 (closed-source text version), CARTE
Findings
TabSTAR is strong on classification tasks
TabPFNv2 comes very close on < 10k samples, but in the larger datasets, the
gap is larger.
TabPFNv2 does better on regression compared to other NN-based methods. But
GBDTs are still much better.
Fine-tuning the encoder (e5) leads to better performance.
The results show that unfreezing the top 9 layers actually leads to worse
performance than 6 or 3, but the authors think that this is due to lack of
data.
Pre-training is significantly better than training from scratch.
Adding both the quantile name (string) and quantile vector (learnable param)
is better than using only one of them.
Resources
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