Update README.md

README.md

@@ -5,6 +5,8 @@ language:
 - en
 metrics:
 - accuracy
+- code_eval
+library_name: adapter-transformers
 ---
 # Model Card for SentimentTensor
 
@@ -76,14 +78,17 @@ predicted_label = outputs.logits.argmax().item()
 # Example Usage
 
 #Load the model and tokenizer
+
 model = AutoModelForSequenceClassification.from_pretrained("your-model-name")
 tokenizer = AutoTokenizer.from_pretrained("your-tokenizer-name")
 
 #Tokenize text data
+
 text = "This is a great movie!"
 tokenized_input = tokenizer(text, return_tensors="pt")
 
 #Perform sentiment analysis
+
 outputs = model(**tokenized_input)
 predicted_label = outputs.logits.argmax().item()
 
@@ -97,5 +102,4 @@ print(f"Predicted Sentiment: {sentiment_labels[predicted_label]}")
 The SentimentTensor model is based on LSTM architecture, which is well-suited for sequence classification tasks like sentiment analysis. It uses long short-term memory cells to capture dependencies in sequential data.
 
 # Model Card Authors
-Saish Shinde
-
+Saish Shinde