Create RobertaGoEmotionClassifier.py

RobertaGoEmotionClassifier.py

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+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+from datasets import load_dataset
+
+# Load the Enron dataset
+dataset = load_dataset("Hellisotherpeople/enron_emails_parsed")
+enron_data = pd.DataFrame(dataset['train'])
+
+# Load the model and tokenizer
+model_name = "modelSamLowe/roberta-base-go_emotions"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Define the emotion labels (based on the GoEmotions dataset)
+emotion_labels = ["admiration", "amusement", "anger", "annoyance", "approval",
+                  "caring", "confusion", "curiosity", "desire", "disappointment",
+                  "disapproval", "disgust", "embarrassment", "excitement", "fear",
+                  "gratitude", "grief", "joy", "love", "nervousness", "optimism",
+                  "pride", "realization", "relief", "remorse", "sadness", "surprise",
+                  "neutral"]
+
+# Function to classify emotion
+def classify_emotion(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_id = torch.argmax(logits, dim=-1).item()
+    return emotion_labels[predicted_class_id]
+
+# Apply emotion classification to the email content
+enron_data['emotion'] = enron_data['body'].apply(classify_emotion)
+
+# Save the results to a CSV file
+enron_data.to_csv("enron_emails_with_emotions.csv", index=False)