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:
- 01e94a3fa01e6d6b4fa91fe51869a919c9e979d3a73e5a21138d49fe9d9da526
- Size of remote file:
- 4.86 kB
- SHA256:
- 40b8986c384ce4afbb04d0ea5ec3af2ae53b4349844d744382938899fe37e2d4
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