Update README.md

README.md

@@ -33,6 +33,7 @@ from huggingface_hub import hf_hub_download
 
 model_repo_name = "pszemraj/deberta-v3-small-sp500-edgar-10k"
 pipe = pipeline("text-classification", model=model_repo_name)
+pipe.tokenizer.model_max_length = 1024
 
 # Download the regression_config.json file
 regression_config_path = hf_hub_download(
@@ -41,23 +42,26 @@ regression_config_path = hf_hub_download(
 with open(regression_config_path, "r") as f:
     regression_config = json.load(f)
 
+
 def inverse_scale(prediction, config):
     """apply inverse scaling to a prediction"""
     min_value, max_value = config["min_value"], config["max_value"]
     return prediction * (max_value - min_value) + min_value
 
+
 def predict_with_pipeline(text, pipe, config, ndigits=4):
-    result = pipe(text)[0]  # Get the first (and likely only) result
-    score = result['score'] if result['label'] == 'LABEL_1' else 1 - result['score']
+    result = pipe(text, truncation=True)[0]  # Get the first (and likely only) result
+    score = result["score"] if result["label"] == "LABEL_1" else 1 - result["score"]
     # Apply inverse scaling
     scaled_score = inverse_scale(score, config)
     return round(scaled_score, ndigits)
 
+
 text = "This is an example text for regression prediction."
 
 # Get predictions
 predictions = predict_with_pipeline(text, pipe, regression_config)
-print("Predicted Value:", predictions)
+print("Predicted returns (frec):", predictions)
 ```
 
 </details>