Instructions to use sileod/deberta-v3-base-tasksource-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sileod/deberta-v3-base-tasksource-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sileod/deberta-v3-base-tasksource-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sileod/deberta-v3-base-tasksource-sentiment") model = AutoModelForSequenceClassification.from_pretrained("sileod/deberta-v3-base-tasksource-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 104d113a077581f1da61d9496b358b64be71b2069b4d231faeb92e2644093b44
- Size of remote file:
- 738 MB
- SHA256:
- a65ce53906e9d0e8e90aafd2fbb60eb39eb783aa1cba69d8ba4b0e8808721d67
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