updated usage code in README.md

README.md

@@ -150,6 +150,28 @@ During fine-tuning, the following hyperparameters were used to optimize model pe
 ---
 ## Usage
 
+### Pipeline Approach
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+
+model_name = "project-aps/finbert-finetune"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Override the config's id2label and label2id
+label_map = {0: "neutral", 1: "negative", 2: "positive"}
+model.config.id2label = label_map
+model.config.label2id = {v: k for k, v in label_map.items()}
+
+pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+text = "Earnings smashed expectations AAPL posts $0.89 EPS vs $0.78 est. Bullish momentum incoming! #EarningsSeason"
+print(pipe(text)) #Output: [{'label': 'positive', 'score': 0.9997484087944031}]
+
+```
+
+### Simple Approach
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
@@ -168,6 +190,7 @@ print(f"Text : {text}")
 print(f"Sentiment: {label_map[predicted_class]}")
 
 ```
+
 ---
 ## Acknowledgements