Update README.md

README.md

@@ -11,7 +11,7 @@ tags:
 - bert
 - Inference Endpoints
 ---
-# Model Card for Model ID
+# Model Card for FineTuned finbert model
 
 <!-- Provide a quick summary of what the model is/does. -->
 
@@ -28,7 +28,7 @@ Our fine-tuned FinBERT model is a powerful tool designed for sentiment analysis
 - **Language:** English
 - **Finetuned from model:** yiyanghkust/finbert-tone
 
-### Model Sources [optional]
+### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
@@ -47,7 +47,7 @@ from transformers import BertTokenizer, BertForSequenceClassification
 from transformers import pipeline
 
 # Load the fine-tuned FinBERT model and tokenizer
-finbert = BertForSequenceClassification.from_pretrained('kdave/FineTuned_Finbert, num_labels=3)
+finbert = BertForSequenceClassification.from_pretrained('kdave/FineTuned_Finbert', num_labels=3)
 tokenizer = BertTokenizer.from_pretrained('kdave/FineTuned_Finbert')
 
 # Create a sentiment-analysis pipeline
@@ -148,11 +148,12 @@ tokenizer = BertTokenizer.from_pretrained('kdave/FineTuned_Finbert')
 
 # Create a sentiment-analysis pipeline
 nlp_pipeline = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
+```
+
 Step 3: Perform Sentiment Analysis
 Now, you're ready to analyze sentiment! Provide the model with sentences related to Indian stock market news:
 
-python
-Copy code
+```python
 # Example sentences related to Indian stock market news
 sentences = [
     "The Indian stock market experienced a surge in trading activity.",