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Rename ConTextTab -> sap-rpt-1-oss #1832

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Rename ConTextTab -> sap-rpt-1-oss #1832

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86aa456
Rename
MaxSchambach Nov 4, 2025
257bccc
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MaxSchambach Nov 5, 2025
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19 changes: 8 additions & 11 deletions packages/tasks/src/model-libraries-snippets.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -156,23 +156,22 @@ pred_df = pipeline.predict_df(
return [installSnippet, exampleSnippet];
};

export const contexttab = (): string[] => {
const installSnippet = `pip install git+https://github.com/SAP-samples/contexttab`;
export const sap_rpt_one_oss = (): string[] => {
const installSnippet = `pip install git+https://github.com/SAP-samples/sap-rpt-1-oss`;

const classificationSnippet = `# Run a classification task
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

from contexttab import ConTextTabClassifier
from sap_rpt_oss import SAP_RPT_OSS_Classifier

# Load sample data
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)

# Initialize a classifier
# You can omit checkpoint and checkpoint_revision to use the default model
clf = ConTextTabClassifier(checkpoint="l2/base.pt", checkpoint_revision="v1.0.0", bagging=1, max_context_size=2048)
# Initialize a classifier, 8k context and 8-fold bagging gives best performance, reduce if running out of memory
clf = SAP_RPT_OSS_Classifier(max_context_size=8192, bagging=8)

clf.fit(X_train, y_train)

Expand All @@ -187,8 +186,7 @@ from sklearn.datasets import fetch_openml
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split

from contexttab import ConTextTabRegressor

from sap_rpt_oss import SAP_RPT_OSS_Regressor

# Load sample data
df = fetch_openml(data_id=531, as_frame=True)
Expand All @@ -198,9 +196,8 @@ y = df.target.astype(float)
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)

# Initialize the regressor
# You can omit checkpoint and checkpoint_revision to use the default model
regressor = ConTextTabRegressor(checkpoint="l2/base.pt", checkpoint_revision="v1.0.0", bagging=1, max_context_size=2048)
# Initialize the regressor, 8k context and 8-fold bagging gives best performance, reduce if running out of memory
regressor = SAP_RPT_OSS_Regressor(max_context_size=8192, bagging=8)

regressor.fit(X_train, y_train)

Expand Down
14 changes: 7 additions & 7 deletions packages/tasks/src/model-libraries.ts
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Expand Up @@ -235,13 +235,6 @@ export const MODEL_LIBRARIES_UI_ELEMENTS = {
repoUrl: "https://github.com/Unbabel/COMET/",
countDownloads: `path:"hparams.yaml"`,
},
contexttab: {
prettyLabel: "ConTextTab",
repoName: "ConTextTab",
repoUrl: "https://github.com/SAP-samples/contexttab",
countDownloads: `path_extension:"pt"`,
snippets: snippets.contexttab,
},
cosmos: {
prettyLabel: "Cosmos",
repoName: "Cosmos",
Expand Down Expand Up @@ -966,6 +959,13 @@ export const MODEL_LIBRARIES_UI_ELEMENTS = {
filter: true,
countDownloads: `path:"cfg.json"`,
},
"sap-rpt-1-oss": {
prettyLabel: "sap-rpt-1-oss",
repoName: "sap-rpt-1-oss",
repoUrl: "https://github.com/SAP-samples/sap-rpt-1-oss",
countDownloads: `path_extension:"pt"`,
snippets: snippets.sap_rpt_one_oss,
},
sapiens: {
prettyLabel: "sapiens",
repoName: "sapiens",
Expand Down
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