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
File size: 297 Bytes
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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 ๐ญ', label_1: 'Joy ๐', label_2: 'Love ๐', label_3: 'Anger ๐ ', label_4: 'Fear ๐จ', label_5: 'Surprise ๐ฒ' |