Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v1
deberta-mnli
Instructions to use NDugar/v2xl-again-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NDugar/v2xl-again-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="NDugar/v2xl-again-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NDugar/v2xl-again-mnli") model = AutoModelForSequenceClassification.from_pretrained("NDugar/v2xl-again-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from NDugar/v2xl-again-mnli: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/NDugar/v2xl-again-mnli/resolve/main/training_args.bin
- Command line
-
hf download hf://NDugar/v2xl-again-mnli/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/NDugar/v2xl-again-mnli/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- 15c9779082d04fc49d0b4b308926ff60f0fc07a7f64215f15347426f970e3652
- Size of remote file:
- 2.8 kB
- SHA256:
- 937df70044f985ff8dee511a2bc22fca38bfcadff7c2ef74fd252af4bd7e9dd2
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