Instructions to use Elegbede/Distilbert_FInetuned_For_Text_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Elegbede/Distilbert_FInetuned_For_Text_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Elegbede/Distilbert_FInetuned_For_Text_Classification")# 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") - Notebooks
- Google Colab
- Kaggle
| --- | |
| 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 π', label_1: 'Joy π', label_2: 'Love π', label_3: 'Anger π ', label_4: 'Fear π¨', label_5: 'Surprise π²' |