Tasks and inputs
MertOezberk95/tabfm-1.0.0-pytorch is listed for Tabular classification. It accepts table and produces label. Use the publisher’s input format and preprocessing for your chosen deployment.
SyncDev model directory · Profile updated Oct 7, 2026
Hugging Face repository for Tabular classification.
The specification score summarizes recorded features and limits. It is not a benchmark result.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
MertOezberk95/tabfm-1.0.0-pytorch is a Hugging Face repository returned by the public Tabular classification task feed. Its recorded download count is 25 and it has 0 likes when the repository snapshot was collected. Popularity is useful for discovery, but it is not a performance ranking or a guarantee of suitability.
This record is categorized for Tabular classification. A task category describes the intended machine-learning problem; it does not prove that every version, checkpoint, quantization, or deployment method produces the same quality. Teams should read the model card and test representative inputs before choosing it.
The public repository lists tabfm as its library metadata. Its recorded tags include tabfm, safetensors, tabular, tabular-regression, zero-shot, in-context-learning, pytorch, foundation-model. These source details are retained so readers can trace this directory entry back to the repository instead of relying on copied descriptions.
Split by time or group when rows are related. Check leakage, imbalanced classes, calibration and the preprocessing required for categorical and missing values.
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Compare the model on the work you need to complete.
MertOezberk95/tabfm-1.0.0-pytorch is listed for Tabular classification. It accepts table and produces label. Use the publisher’s input format and preprocessing for your chosen deployment.
Measure a completed task using the prompts, documents and tools your application will use. Include reasoning and retries in the result.
Use the model identifier and account requirements for the selected API route. Confirm its request limits and output format with one complete task before connecting a larger workflow.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
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