Update README.md

README.md

@@ -79,3 +79,29 @@ The following hyperparameters were used during training:
 - Pytorch 2.5.1
 - Datasets 3.1.0
 - Tokenizers 0.20.3
+
+### code to use in pipeline
+import matplotlib.pyplot as plt
+import plotly.graph_objects as go
+%matplotlib inline
+from transformers import pipeline
+classifier = pipeline("text-classification", model="Sharpaxis/FIN_BERT_sentiment",top_k=None)
+def finance_sentiment_predictor(text):
+    text = str(text)
+    out = classifier(text)[0]
+    scores = [sample['score'] for sample in out]
+    labels = [sample['label'] for sample in out ]
+    label_map = {'LABEL_0':"Negative",'LABEL_1':"Neutral",'LABEL_2':"Positive"}
+    sentiments = [label_map[label] for label  in labels]
+    for i in range(len(scores)):
+        print(f"{sentiments[i]} : {scores[i]}")
+    print(f"Sentiment of text is {sentiments[np.argmax(scores)]}")
+    fig = go.Figure(
+        data=[go.Bar(x=sentiments,y=scores,marker=dict(color=["red", "blue", "green"]),width=0.3)])
+    fig.update_layout(
+        title="Sentiment Analysis Scores",
+        xaxis_title="Sentiments",
+        yaxis_title="Scores",
+        template="plotly_dark"
+    )
+    fig.show()