Update README.md

README.md

@@ -21,6 +21,61 @@ should probably proofread and complete it, then remove this comment. -->
 this predicts the `ret` column of the training dataset, given the `text` column. Fine-tuned @ ctx 1024.
 
 
+<details>
+  <summary>Click to expand code example</summary>
+
+```py
+import json
+
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Define the model repository on Hugging Face Hub
+model_repo_name = "pszemraj/deberta-v3-small-sp500-edgar-10k"
+
+# Download the regression_config.json file
+regression_config_path = hf_hub_download(
+    repo_id=model_repo_name, filename="regression_config.json"
+)
+
+# Load regression configuration
+with open(regression_config_path, "r") as f:
+    regression_config = json.load(f)
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_repo_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_repo_name)
+
+
+# Function to apply inverse scaling to a prediction
+def inverse_scale(prediction, config):
+    min_value, max_value = config["min_value"], config["max_value"]
+    return prediction * (max_value - min_value) + min_value
+
+
+# Example of using the model for inference
+def predict(text, tokenizer, model, config, ndigits=4):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predictions = logits.numpy()
+        # Assuming regression task, apply inverse scaling
+        scaled_predictions = [inverse_scale(pred[0], config) for pred in predictions]
+    return round(scaled_predictions[0], ndigits)
+
+
+# Example text
+text = "This is an example text for regression prediction."
+
+# Get predictions
+predictions = predict(text, tokenizer, model, regression_config)
+print("Predicted Value:", predictions)
+```
+<details>
+
 ## Model description
 
 This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on BEE-spoke-data/sp500-edgar-10k-markdown
@@ -30,8 +85,6 @@ It achieves the following results on the evaluation set:
 - Mse: 0.0005
 
 
-
-
 ## Training procedure
 
 ### Training hyperparameters