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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 165ed53f23251e99832734741a86785632e766f4335d257b402f7e86d1e9d7ae
- Size of remote file:
- 1.12 GB
- SHA256:
- 2b9d764bb07ec13b33229906d220eeb86135b143a6a532955e6e3f2c194b9754
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