Instructions to use VivekMalipatel23/mDeBERTa-v3-base-text-emotion-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VivekMalipatel23/mDeBERTa-v3-base-text-emotion-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="VivekMalipatel23/mDeBERTa-v3-base-text-emotion-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VivekMalipatel23/mDeBERTa-v3-base-text-emotion-classification") model = AutoModelForSequenceClassification.from_pretrained("VivekMalipatel23/mDeBERTa-v3-base-text-emotion-classification") - Notebooks
- Google Colab
- Kaggle
Multilingual mDeBERTa base model fineted on Text_emotions dataset.
Dataset link : https://www.kaggle.com/datasets/nelgiriyewithana/emotions/data
Finetuned for classifying text into sadness (0) joy (1) love (2) anger (3) fear (4) and surprise (5) emotions.
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