Delete RobertaGoEmotionClassifier

RobertaGoEmotionClassifier

@@ -1,35 +0,0 @@
-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)