Update README.md

README.md

@@ -32,25 +32,20 @@ It also uses L1 regularization to reduce overfitting.
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-This could be used for general image classification tasks, especially those for culinary uses. 
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-The direct use would be to classify food as either Western or Asian based on an image. 
+This is used for classification of books as softcover or hardcover based on their measurements.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-If the dataset was expanded, this could be used to classify other types of food among numerous other classes. 
+If the dataset was expanded, this could be used to classify other types of books or a larger dataset. 
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-This is trained off a small dataset of 30 original photos and 300 augmented photos. This could suggest overfitting of the model and additional information is required to make it more robust.
+This is trained off a small dataset of 30 original books and 300 augmented rows. 
+This limited training dataset is liable to overfitting of the model and additional information is required to make it more robust.
 
 ### Recommendations
 
@@ -58,9 +53,43 @@ This is trained off a small dataset of 30 original photos and 300 augmented phot
 
 The small dataset size means this model is not highly generalizable. 
 
-## How to Get Started with the Model
+### How to Get Started with the Model
 
 Use the code below to get started with the model.
+This code is from the 24-679 Lecture on tabular datasets. 
+
+Download the zipped native predictor directory
+zip_local_path = huggingface_hub.hf_hub_download(
+    repo_id=MODEL_REPO_ID,
+    repo_type="model",
+    filename="autogluon_predictor_dir.zip",
+    local_dir=str(download_dir),
+    local_dir_use_symlinks=False
+)
+
+Unzip to a folder
+native_dir = download_dir / "predictor_dir"
+if native_dir.exists():
+    shutil.rmtree(native_dir)
+native_dir.mkdir(parents=True, exist_ok=True)
+
+with zipfile.ZipFile(zip_local_path, "r") as zf:
+    zf.extractall(str(native_dir))
+
+Load native predictor
+predictor_native = autogluon.tabular.TabularPredictor.load(str(native_dir))
+
+Inference on synthetic test
+X_test = df_synth_test.drop(columns=[TARGET_COL])
+y_true = df_synth_test[TARGET_COL].reset_index(drop=True)
+y_pred = predictor_native.predict(X_test).reset_index(drop=True)
+
+Combine results
+results = pandas.DataFrame({
+    "y_true": y_true,
+    "y_pred": y_pred
+})
+display(results)
 
 ## Training Details