Zero-Shot Classification
Transformers
PyTorch
Safetensors
deberta-v2
text-classification
mdeberta-v3-base
nli
natural-language-inference
multitask
multi-task
pipeline
extreme-multi-task
extreme-mtl
tasksource
zero-shot
rlhf
Instructions to use sileod/mdeberta-v3-base-tasksource-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sileod/mdeberta-v3-base-tasksource-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="sileod/mdeberta-v3-base-tasksource-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sileod/mdeberta-v3-base-tasksource-nli") model = AutoModelForSequenceClassification.from_pretrained("sileod/mdeberta-v3-base-tasksource-nli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97d9721cfefd5154633764dd70c7aaf175a4bbae43350ad1564044c128a6343a
- Size of remote file:
- 1.12 GB
- SHA256:
- 445c3d805ba6bfef5f87d419fe76a5185c99a5e4c0ace4c47b7a9c0f74612a89
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