# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Elegbede/Distilbert_FInetuned_For_Text_Classification")
model = AutoModelForSequenceClassification.from_pretrained("Elegbede/Distilbert_FInetuned_For_Text_Classification")Quick Links
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license: apache-2.0 --- This model involves finetuning Distilbert for text classification to generate emotions from texts The label mapping corresponds to: label_0: 'Sadness 😭', label_1: 'Joy 😂', label_2: 'Love 😍', label_3: 'Anger 😠', label_4: 'Fear 😨', label_5: 'Surprise 😲'
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Elegbede/Distilbert_FInetuned_For_Text_Classification")