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…
Open-weight model · Tabular classification
tourism-wellness-model
by ShrimathiNarayanachar shrimathin/tourism-wellness-model
tourism-wellness-model is an open-weight model for tabular classification from ShrimathiNarayanachar, released under MIT License. Its published files total 42.9 MB.
Predicts whether a "Visit with Us" customer will buy the Wellness Tourism Package (ProdTaken).
Parameters—
Context—
Weights42.9 MB
Licensemit
AccessOpen weights
Monthly Downloads—
Model Card
By ShrimathiNarayanachar, published under mit, revision 190f29535351.
Predicts whether a "Visit with Us" customer will buy the Wellness Tourism Package (ProdTaken).
Read ShrimathiNarayanachar's full model card
Wellness Tourism Package: purchase propensity model
Predicts whether a "Visit with Us" customer will buy the Wellness Tourism Package (ProdTaken).
- Algorithm: random_forest inside a scikit-learn Pipeline (impute + scale + one-hot)
- Decision threshold: 0.316 (maximises out-of-fold F1)
- Trained: 2026-10-12T03:09:33+00:00 | commit
219a9c2
| Test metric | Value |
|---|---|
| Accuracy | 0.936 |
| Precision | 0.851 |
| Recall | 0.813 |
| F1 | 0.832 |
| ROC-AUC | 0.970 |
| PR-AUC | 0.904 |
import joblib, json
from huggingface_hub import hf_hub_download
model = joblib.load(hf_hub_download("shrimathin/tourism-wellness-model", "wellness_model.joblib"))
meta = json.load(open(hf_hub_download("shrimathin/tourism-wellness-model", "model_metadata.json")))
proba = model.predict_proba(df)[:, 1]
buy = proba >= meta["threshold"]
Identity and Version
- Repository
- shrimathin/tourism-wellness-model
- Publisher
- ShrimathiNarayanachar
- Task
- Tabular classification
- Modality
- Tabular
- Library
- Not stated by the source
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- 190f2953535190e5abc00674c946e0a99f427b60
- First published
- 2026-09-27
- Last updated
- 2026-10-12
Files and Weights
4 files, 42.9 MB in total.
Configuration1 file · 2.0 KB
Documentation1 file · 993 B
Other1 file · 42.9 MB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model_metadata.json | Configuration | 2.0 KB | — |
| README.md | Documentation | 993 B | — |
| wellness_model.joblib | Other | 42.9 MB | fa599a380e0d |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
Download from ShrimathiNarayanachar
Released by ShrimathiNarayanachar through its official repository on Hugging Face. Read the license.
Built From
- Trained on (disclosed) shrimathin/tourism-wellness-dataset
Questions About tourism-wellness-model
Can I use tourism-wellness-model commercially?
Yes. tourism-wellness-model is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
Similar Models
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…
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…
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.…
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…
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…