Update README.md

README.md

@@ -14,18 +14,6 @@ https://huggingface.co/rezahf2024/fine_tuned_financial_setiment_analysis_gpt2_mo
 
 This a fine-tuned GPT2 model on the https://huggingface.co/datasets/FinGPT/fingpt-sentiment-train dataset for the down-stream financial sentiment analysis. 
 
-label_mapping = {
-'LABEL_0': 'mildly positive',
-'LABEL_1': 'mildly negative',
-'LABEL_2': 'moderately negative',
-'LABEL_3': 'moderately positive',
-'LABEL_4': 'positive',
-'LABEL_5': 'negative',
-'LABEL_6': 'neutral',
-'LABEL_7': 'strong negative',
-'LABEL_8': 'strong positive'
-}
-
 - **Developed by:** Rezaul Karim, Ph.D.
 - **Funded by [optional]:** Self
 - **Shared by [optional]:** Rezaul Karim, Ph.D.
@@ -44,6 +32,54 @@ label_mapping = {
 
 The model is already fine-tuned for downstream financial sentiment analysis tasks. 
 
+```
+import torch
+
+# Load your fine-tuned model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("fine_tuned_finsetiment_model")
+tokenizer = AutoTokenizer.from_pretrained("fine_tuned_finsetiment_model")
+
+# Define the label mapping as provided
+label_mapping_reverse = {
+'LABEL_0': 'mildly positive',
+'LABEL_1': 'mildly negative',
+'LABEL_2': 'moderately negative',
+'LABEL_3': 'moderately positive',
+'LABEL_4': 'positive',
+'LABEL_5': 'negative',
+'LABEL_6': 'neutral',
+'LABEL_7': 'strong negative',
+'LABEL_8': 'strong positive'
+}
+
+def model_predict(text):
+  # Tokenize the input text
+  inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+
+  # Get predictions from the model
+  with torch.no_grad():
+    logits = model(**inputs).logits
+
+    # Convert to probabilities
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+
+    # Create a list of tuples with label and probability
+    label_prob_pairs = [(label_mapping_reverse[label_idx], prob.item()) 
+    for label_idx, prob in enumerate(probabilities.squeeze())]
+
+    # Sort the list by probability in descending order
+    sorted_label_prob_pairs = sorted(label_prob_pairs, key=lambda pair: pair[1], reverse=True)
+
+  # Return the sorted list of label-probability pairs
+  return sorted_label_prob_pairs
+
+# Example usage
+text = "Intel Corporation (NASDAQ: INTC) has unveiled a remote verification platform called Project Amber"
+predictions = model_predict(text)
+for label, prob in predictions:
+  print(f"{label}: {prob:.3f}")
+```
+
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
@@ -163,7 +199,7 @@ def compute_metrics(eval_pred):
 
 ## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+<!-- If a paper or blog post is introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**