thinkhong/RAIC
tabular-classification model by thinkhong
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Model card & documentation
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A DistilBERT-style transformer adapted to tabular clinical data. Each row (participant) is treated as a "sentence" whose "tokens" are its features: the (min-max normalized) value (numinputval variableweightsembeddings[numinputids]). (variableweightsembeddings[catinputids]). token corresponds to, acting like a position embedding (variablebiasembeddings[numvariableids | catvariableids]). The model was trained to predict 5-year incident cardiovascular disease (CVD) from UK Biobank baseline assessment data (YEAR5CVD, binary), using 69 continuous and 76 categorical features. This checkpoint corresponds to the local experiment 202406031UKBCVDclshpsearch. hidden dim 128, feed-forward dim 1024) automatically via trustremotecode=True (see automap in config.json). The model's forward() does not take inputids/text. Instead it expects: See testupload.py for a full worked (dummy-value) example, including…
Read the full model card ↗ · Preview checked 2026-11-03T22:16:22.927Z
Inside the original model card — Document outline
- RAIC — FTT (Feature Tokenizer Transformer) for UK Biobank CVD risk classification
- Model details
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- Usage
- Testing
- Verification
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Model overview
thinkhong/RAIC is a tabular-classification model repository published by thinkhong on Hugging Face. A library was not reported.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- tabular-classification
- Library
- Not reported
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 2,852,482
- Architecture
- FTTForSequenceClassification
- License
- unknown
- Access
- Not gated by Hugging Face
- Created
- 2026-09-22T06:15:12.000Z
- Last modified
- 2026-09-22T06:20:52.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-22T06:37:17.952Z. Daily imports are snapshots, not real-time monitoring.
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