diff --git a/examples/inference.py b/examples/inference.py
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+++ b/examples/inference.py
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+"""
+This is an example of how to use the toolkit to run inference.
+We use saved feature processors (generated by code in the tests folder).
+This code assumes you have a dataframe of datapoints and batches them up.
+"""
+
+import os
+import sys
+
+sys.path.append("./")
+
+import joblib
+import numpy as np
+import pandas as pd
+import torch
+from transformers import AutoConfig, AutoTokenizer, HfArgumentParser, set_seed
+
+from multimodal_exp_args import (
+    ModelArguments,
+    MultimodalDataTrainingArguments,
+    OurTrainingArguments,
+)
+from multimodal_transformers.data import load_data
+from multimodal_transformers.model import AutoModelWithTabular, TabularConfig
+
+if __name__ == "__main__":
+    DEBUG = True
+    DEBUG_DATASET_SIZE = 50
+    JSON_FILE = "./tests/test_airbnb.json"
+    MODEL_SAVE_DIR = "./logs_airbnb/bertmultilingual_gating_on_cat_and_num_feats_then_sum_full_model_lr_3e-3/"
+    NUMERICAL_TRANSFORMER_PATH = os.path.join(
+        MODEL_SAVE_DIR, "numerical_transformer.pkl"
+    )
+    CATEGORICAL_TRANSFORMER_PATH = os.path.join(
+        MODEL_SAVE_DIR, "categorical_transformer.pkl"
+    )
+    MODEL_CONFIG_PATH = os.path.join(MODEL_SAVE_DIR, "config.json")
+    MODEL_PATH = os.path.join(MODEL_SAVE_DIR, "model.safetensors")
+
+    # Parse our input json files
+    parser = HfArgumentParser(
+        (ModelArguments, MultimodalDataTrainingArguments, OurTrainingArguments)
+    )
+    model_args, data_args, training_args = parser.parse_json_file(
+        json_file=os.path.abspath(JSON_FILE)
+    )
+
+    # Set random seed for reproducibility
+    set_seed(training_args.seed)
+
+    # Create a tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(
+        (
+            model_args.tokenizer_name
+            if model_args.tokenizer_name
+            else model_args.model_name_or_path
+        ),
+        cache_dir=model_args.cache_dir,
+    )
+
+    # Load our feature processors
+    categorical_transformer = joblib.load(CATEGORICAL_TRANSFORMER_PATH)
+    numerical_transformer = joblib.load(NUMERICAL_TRANSFORMER_PATH)
+
+    # Load our test set
+    data_df = pd.read_csv(os.path.join(data_args.data_path, "test.csv"))
+
+    # Load and preprocess our test dataset
+    test_dataset = load_data(
+        data_df=data_df,
+        text_cols=data_args.column_info["text_cols"],
+        tokenizer=tokenizer,
+        label_col=data_args.column_info["label_col"],
+        label_list=data_args.column_info["label_list"],
+        categorical_cols=data_args.column_info["cat_cols"],
+        numerical_cols=data_args.column_info["num_cols"],
+        sep_text_token_str=(
+            tokenizer.sep_token
+            if not data_args.column_info["text_col_sep_token"]
+            else data_args.column_info["text_col_sep_token"]
+        ),
+        categorical_transformer=categorical_transformer,
+        numerical_transformer=numerical_transformer,
+        max_token_length=training_args.max_token_length,
+        debug=DEBUG,
+        debug_dataset_size=DEBUG_DATASET_SIZE,
+    )
+
+    task = data_args.task
+    # Regression tasks have only one "label"
+    if task == "regression":
+        num_labels = 1
+    else:
+        num_labels = (
+            len(np.unique(test_dataset.labels))
+            if data_args.num_classes == -1
+            else data_args.num_classes
+        )
+
+    # Setup configs
+    config = AutoConfig.from_pretrained(
+        MODEL_CONFIG_PATH,
+        cache_dir=model_args.cache_dir,
+    )
+    tabular_config = TabularConfig(
+        num_labels=num_labels,
+        cat_feat_dim=(
+            test_dataset.cat_feats.shape[1] if test_dataset.cat_feats is not None else 0
+        ),
+        numerical_feat_dim=(
+            test_dataset.numerical_feats.shape[1]
+            if test_dataset.numerical_feats is not None
+            else 0
+        ),
+        **vars(data_args),
+    )
+    config.tabular_config = tabular_config
+
+    # Make model
+    model = AutoModelWithTabular.from_pretrained(
+        MODEL_PATH,
+        config=config,
+        cache_dir=model_args.cache_dir,
+    )
+
+    # Run inference
+    dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=16)
+    model.eval()
+    all_labels = []
+    all_preds = []
+    with torch.no_grad():
+        for batch in dataloader:
+            _, logits, classifier_outputs = model(
+                batch["input_ids"],
+                attention_mask=batch["attention_mask"],
+                token_type_ids=batch["token_type_ids"],
+                cat_feats=batch["cat_feats"],
+                numerical_feats=batch["numerical_feats"],
+            )
+            all_labels.append(batch["labels"])
+            all_preds.append(logits.argmax(axis=1))
+
+    all_preds = torch.cat(all_preds)
+    all_labels = torch.cat(all_labels)
+    acc = torch.sum(all_preds == all_labels) / all_labels.shape[0]
+    print(f"Accuracy: {acc}")