Tabular classification Models

Model · Tabular classification

mitra-classifier

Autogluon

Mitra classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. To use Mitra classifier, install AutoGluon by running: A minimal example showing how to perform inference using the Mitra classifier: A minimal example showing how to perform fine-tuning using the Mitra classifier: This project is licensed under the Apache-2.0 License. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models

Model · Tabular classification

Nori-30M

Synthefy

Nori-30M is the ~29.2M-parameter variant of Nori, a tabular foundation model for regression via in-context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a single forward pass, with no task-specific training or fine-tuning. The model is trained entirely on synthetic data. Mean and median R² across 96 regression tasks from three public benchmark suites, on the same protocol as the base Nori: Stronger than the ~6M base on every suite. Evaluated with the bundled default inference config and the large-GPU protocol (up to 50k context rows per dataset). Paste this into Claude Code, Cursor, or any AI coding assistant and it will wire python from synthefynori…

Model · Tabular classification

sap-rpt-1-oss

SAP

Go to SAP-RPT Playground ↗ Note: This model and repository were formerly known as ConTextTab. While the code and repository have now been updated in line with the new name sap-rpt-1-oss, the model checkpoint and functionality remain identical. Implementation of the deep learning model with the inference pipeline described in the paper "ConTextTab: A Semantics-Aware Tabular In-Context Learner". Tabular in-context learning (ICL) has recently achieved state-of-the-art (SOTA) performance on several tabular prediction tasks. Previously restricted to classification problems on small tables, recent advances such as TabPFN and TabICL have extended its use to larger datasets. While being…

Model · Tabular classification

Nori

Synthefy

Nori is a tabular foundation model for regression via in-context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a single forward pass, with no task-specific training or fine-tuning. The model is trained entirely on synthetic data. Mean and median R² of the base model across 96 regression tasks from three public benchmark suites (single H200, up to 50K context rows per dataset): Large-N / long-context tables (common in TabArena) are the current focus of the large-table training stages. These numbers are reproducible end-to-end with one command — see Reproducing these numbers. Paste this into Claude Code, Cursor, or any AI coding assistant and it will…

Model · Tabular classification

EXAONE-Tabular

LG AI Research

EXAONE Tabular is a transformer-based foundation model for tabular data that solves classification and regression through in-context learning: you pass the labeled rows to fit and the model predicts new rows in a single forward pass — no gradient updates and no per-dataset training. This repository is the exaonetabular inference runtime — a self-contained package that loads a released checkpoint and serves predictions through a small, scikit-learn-style API. The code here is permissively licensed; the released weights are non-commercial — see Both checkpoints are released: EXAONETabularClassifier and EXAONETabularRegressor each fetch their own weights with a single frompretrained() call.…

Model · Tabular classification

TabSTAR

Alan Arazi

To fit a pretrained TabSTAR model to your own dataset, install the package: Paper: TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations

Model · Tabular classification

TabPFN-v2-clf

Prior Labs

TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular datasets without requiring task-specific training. For detailed usage examples and best practices, check out: - Python ≥ 3.9 - PyTorch ≥ 2.1 - scikit-learn ≥ 1.0 This repository hosts the production TabPFN-v2 base checkpoints. Files matching the pattern tabpfn-v2-classifier-finetuned-.ckpt are content-identical aliases of the corresponding base checkpoints (e.g. tabpfn-v2-classifier-finetuned-gn2p4bpt-xp6f0iqb.ckpt is identical to tabpfn-v2-classifier-gn2p4bpt.ckpt; tabpfn-v2-classifier-finetuned-zk73skhh.ckpt is identical to…

Model · Tabular classification

mitra-classifier-2

Autogluon

Mitra-v2 classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers, including the new Hybrid SCM prior. It is the second generation of the Mitra classifier (autogluon/mitra-classifier), pre-trained with a 10x longer context, three times as many features, and an improved optimizer. On the TabArena and TALENT benchmarks it delivers state-of-the-art accuracy at the level of TabFM and EXAONE Tabular, while surpassing TabPFN-3 by a wide margin. The regression model is at autogluon/mitra-regressor-2, and the inference and fine-tuning code with our evaluation results is at autogluon/mitra-finetune. Mitra-v2 is based on a…

Model · Tabular classification

tabfm-1.0.0-pytorch

Google

TabFM is a zero-shot tabular foundation model from Google Research. It supports classification and regression on structured/tabular data with mixed numerical and categorical columns, requiring no fine-tuning or hyperparameter search - training examples are passed as context and predictions are made in a single forward pass. This repository contains the PyTorch weights. For the JAX/Flax weights see You can also load directly using the HuggingFace Hub API: Developed by the Google Research team. - Tabular data with numerical and/or categorical columns - Binary and multiclass classification (up to 10 classes) TabFM uses alternating row and column attention to capture both feature interactions…

Model · Tabular classification

tabpfn_3

Prior Labs

TabPFN-3 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/TabPFN. More details can be found in the Model Report. Fitting a classifier and predicting looks like this: For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! TabPFN-3 ships with default classification and regression checkpoints, plus a few experimental specialized variants. We recommend starting with the defaults — the variants can be useful in ensembling or HPO setups, or tried manually in the regime they were trained for. Their name…

Model · Tabular classification

tabpfn_2_5

Prior Labs

TabPFN-2.5 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/tabPFN. Fitting a classifier and predicting looks like this: For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! Developed by Prior Labs. Regression and classification tasks with ≤50 000 samples and ≤2000 features in structured tabular format. - Not suitable for unstructured data (text, images); use API version for textual features. - Not tested for >50 000 samples or > 2000 features. Transformer with TabPFNv2-like alternating attention with…

Model · Tabular classification

Nori-100M

Synthefy

Nori-100M is the ~98.3M-parameter variant of Nori, a tabular foundation model for regression via in-context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a single forward pass, with no task-specific training or fine-tuning. The model is trained entirely on synthetic data. It uses a GPU when one is available and falls back to CPU. A one-shot helper skips the object predict follows the TabPFNRegressor.predict contract: pass outputtype="mean" (default), "median", or "mode" to choose the point estimate drawn from the model's predictive distribution. To run from a local checkpoint instead of the Hub, pass a path: NoriRegressor(modelpath="path/to/nori.pt").…

Model · Tabular classification

genomics-tf-prediction

Généreux Akotenou

accuracy pipelinetag: tabular-classification - biology

Model · Tabular classification

tabpfn_3_5

Prior Labs

TabPFN-3.5 is a transformer-based foundation model that uses in-context learning to solve tabular prediction problems in a forward pass. One checkpoint serves both classification and regression. Inference code can be found at https://github.com/PriorLabs/TabPFN. Fitting a classifier and predicting looks like this: TabPFNRegressor works the same way, off the same checkpoint. For more examples, see the github repo: https://github.com/PriorLabs/TabPFN. To use a specific checkpoint, pass its filename via modelpath: Every checkpoint ships with basic text and datetime column handling enabled (TRANSFORMTEXT, TRANSFORMDATES) and a feature limit of 20,000. Developed by Prior Labs. Regression and…

Model · Tabular classification

tabpfn_2_6

Prior Labs

TabPFN-2.6 is a transformer-based foundation model that uses in-context-learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/tabPFN. Fitting a classifier and predicting looks like this: For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! Developed by Prior Labs. Regression and classification tasks with ≤50 000 samples and ≤2000 features in structured tabular format. - Not suitable for unstructured data (text, images); use API version for textual features. - Not tested for >50 000 samples or > 2000 features. Transformer with TabPFNv2-like alternating attention with…

Model · Tabular classification

tabfm-1.0.0-jax

Google

TabFM is a zero-shot tabular foundation model from Google Research. It supports classification and regression on structured/tabular data with mixed numerical and categorical columns, requiring no fine-tuning or hyperparameter search - training examples are passed as context and predictions are made in a single forward pass. This repository contains the JAX/Flax weights stored as Orbax checkpoints. For the PyTorch weights see You can also load directly using the HuggingFace Hub API: Developed by the Google Research team. - Tabular data with numerical and/or categorical columns - Binary and multiclass classification (up to 10 classes) - GPU/TPU acceleration via JAX (use bfloat16 dtype for…

Model · Tabular classification

tabpfn-mix-1.0-classifier

Autogluon

TabPFNMix classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. TabPFNMix is based on a 12-layer encoder-decoder Transformer of 37 M parameters. We use a pre-training strategy incorporating in-context learning, similar to that used by TabPFN and TabForestPFN. To use TabPFNMix classifier, install AutoGluon by running: A minimal example showing how to perform fine-tuning and inference using the TabPFNMix classifier: If you find TabPFNMix useful for your research, please consider citing the associated papers: This project is licensed under the Apache-2.0 License.

This model has been pushed to the Hub using the PytorchModelHubMixin integration

Model · Tabular classification

DataScientst-models

ALİ OKTAY BBUS

Trained models from a comprehensive ML/AI portfolio spanning 10 categories. All models were trained on real Kaggle datasets.

Task. Binary classification of simulated dimuon events: does this event contain an opposite-sign muon pair whose invariant mass falls in (80, 100) GeV? Method. The classifier is trained with Stable-Baselines3 DQN or PPO inside a one-step Gymnasium wrapper. Each reset / step is one labeled event and then terminated=True. There is no horizon, no delayed credit assignment, and no control of a detector. Label. Computed from the same padded muon kinematics the policy sees: at least two muons, opposite charge, invariant mass in (massmin, massmax) with defaults 80–100 GeV. This is not generator-level Z truth and not a CMS official tag. Data for the reported numbers. Pythia8 Monte Carlo only…

Model · Tabular classification

wine-quality

Julien Chaumond

No red wine was drunk (unfortunately) while training this model

Model is avaliable at https://huggingface.co/yzhuang/MetaTree 1. Install metatreelib: 2. Use MetaTree on your datasets to generate a decision tree model We show a complete example of using MetaTree at notebook If you have any questions related to the code or the paper, feel free to reach out to us at [email protected]. If you find our paper and code useful, please cite us

Model · Tabular classification

mitra-classifier-1.1

Autogluon

Mitra classifier is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random classifiers. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. To use Mitra classifier, install AutoGluon by running: A minimal example showing how to perform inference using the Mitra classifier: A minimal example showing how to perform fine-tuning using the Mitra classifier: This project is licensed under the Apache-2.0 License. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models

Predicts which MBTA commuter rail track/platform a train will use, using a small tabular neural-network ensemble trained on historical assignments. This card documents the artifacts in output/ensemble20250906124755. - trackpredictionensemblemodel0final.keras … trackpredictionensemblemodel5final.keras — individual ensemble members - trackpredictionensemblemodelbest.keras — best checkpoints during training (may match final) - trainingreport.md — training configuration and metrics Note: Ensemble training currently does not emit a vocab.json. See “Preprocessing & Vocab” below. Models expect integer indices for stationid and routeid, and raw directionid 0/1. In training, indices are produced by…

Model · Tabular classification

rt-plurel

Stanford Tabular and Relational (STAR) Project

RT-PluRel is a Relational Transformer checkpoint pair for in-context / few-shot entity prediction over multi-table relational databases (no per-task gradient training). Same architecture and file layout as stanford-star/rt-j — drop-in replacement. Architecture: ~85.6M blocks, dmodel 512, 8 heads, dff 2048 · text columns embedded with all-MiniLM-L12-v2 (dtext 384). Each folder contains model.safetensors (weights) and config.json (dims + text-embedding model). The paper/ subdirectory preserves the earlier RT-PluRel release:.pt checkpoints (12 blocks, dmodel 256, dff 1024) pretrained on synthetic relational databases generated by PluRel, plus the continued-pretraining and fine-tuned RelBench…

Model · Tabular classification

wine-quality

Vova Manannikov

No red wine was drunk (unfortunately) while training this model

Custom TabTransformer-style PyTorch model for churn prediction, trained on a synthetic ShopSphere e-commerce dataset for an academic Data-Driven Marketing Analytics project. covering Historical / Update / Post-Update periods. Selected on the validation set by maximizing expected net value under the assumption that a missed churner costs 25x more than an unnecessary retention outreach (₹5,000 lost value vs. ₹200 intervention cost) — not by F1. The dataset is synthetic and does not represent real ShopSphere customers. Results, including the baseline comparison, should not be interpreted as causal evidence that the platform algorithm update caused churn, and the hyperparameter search covered a…

Binary classification model that predicts whether EUR/USD will close higher (UP) or lower (DOWN) the next trading day. The model uses 53 features including: - Williams %R, CCI: Additional momentum indicators See predict.py for a complete inference example. Based on published financial ML literature: This model is for research and educational purposes only. It is NOT financial advice. Forex trading involves significant risk. Past performance does not guarantee future results. Realistic accuracy for daily direction prediction is 52-56% (literature consensus). This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Model · Tabular classification

imdb

Devang Parekh

Problem type: Multi-class Classification; Model ID: 1530155186; CO2 Emissions (in grams): 0.0186; Loss: 0.694; Accuracy: 0.487; Macro F1: 0.218.

Model · Tabular classification

heart_failure_prediction

Abdalla Ahmed

A Keras / TensorFlow neural network that predicts the presence of heart disease in patients using 11 clinical and demographic features. Educational and research purposes only. Explore how clinical features relate to heart disease risk. - Not a medical device. Do not use for real clinical decisions. - Not validated on real-world hospital populations. from huggingfacehub import hfhubdownload import joblib import pandas as pd from tensorflow import keras modelpath = hfhubdownload("abdalla732/heartfailureprediction", "heartmodel.keras") scalerpath = hfhubdownload("abdalla732/heartfailureprediction", "scaler.joblib") colspath = hfhubdownload("abdalla732/heartfailureprediction"…

Model · Tabular classification

DigitalEduTransformers

SnowFlash383935

Модель для соревнования DigitalEdu с использованием трансформеров. 9 эпох. Пример вывода: [False, True]

A hybrid Graph Neural Network (GraphSAGE + GATv2) trained on labeled Solana blockchain datasets (SolRPDS, Kaggle Solana, Solarchive) to detect malicious liquidity pools, rug-pull tokens, and fraudulent wallet flows. 1. SolRPDS (Solana Rug Pull Dataset): 62,895 suspicious liquidity pools and 22,195 confirmed rug-pull tokens derived from 3.69 billion transactions. 2. Kaggle Solana Blockchain Dataset: Labeled entity categorization. 3. Solarchive: Partitioned daily Solana Parquet transaction flows.

Model · Tabular classification

collaborative-filtering-movielens

Keras

This repo contains the model and the notebook on how to build and train a Keras model for Collaborative Filtering for Movie Recommendations. Full credits to Siddhartha Banerjee. Based on a user and movies they have rated highly in the past, this model outputs the predicted rating a user would give to a movie they haven't seen yet (between 0-1). This information can be used to find out the top recommended movies for this user. The dataset consists of user's ratings on specific movies. It also consists of the movie's specific genres. The model was trained for 5 epochs with a batch size of 64. The following hyperparameters were used during training: - trainingprecision: float32

Model · Tabular classification

human-disease-prediction

AWeirdDev

Try it on Spaces The yourdata variable should be a vector of zeros and ones. A zero means "False," and a one means "True." Create a vector that pairs with the following symptoms, then the model will predict what disease it might be. It has been reported as 1.0 (100%), but I don't believe it.

Model · Tabular classification

fela-tab

Lowdown Labs

This model is a research preview. It is offered for advancing public science and for evaluation. It is not a substitute for a domain expert, and it is not a certified decision system. FelaTab reads a small table you already have and predicts the missing cells. You give it some example rows with their answers (the support rows) and one or more rows you want filled in (the query rows); it learns the pattern from your examples in a single pass and returns the answer with a calibrated confidence range. There is no per table training, no fitting, and no setup: you point it at a table and it predicts. It runs on a plain CPU with no GPU. It is a prior fitted network (a "foundation model for…