Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v3-base
deberta-v3
deberta
nli
natural-language-inference
multitask
multi-task
pipeline
extreme-multi-task
extreme-mtl
tasksource
zero-shot
rlhf
Eval Results (legacy)
Instructions to use sileod/deberta-v3-base-tasksource-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sileod/deberta-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/deberta-v3-base-tasksource-nli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sileod/deberta-v3-base-tasksource-nli") model = AutoModelForSequenceClassification.from_pretrained("sileod/deberta-v3-base-tasksource-nli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from sileod/deberta-v3-base-tasksource-nli: direct link, hf CLI and curl.
- Browser
- Download file 8.66 MB
-
https://huggingface.co/sileod/deberta-v3-base-tasksource-nli/resolve/main/tokenizer.json
- Command line
-
hf download hf://sileod/deberta-v3-base-tasksource-nli/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sileod/deberta-v3-base-tasksource-nli/resolve/main/tokenizer.json
8.66 MB
File too large to display, you can check the raw version instead.