Zero-Shot Classification
Transformers
PyTorch
English
deberta-v2
text-classification
deberta-v3
deberta-v2`
deberta-mnli
Instructions to use NDugar/1epochv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NDugar/1epochv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="NDugar/1epochv3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NDugar/1epochv3") model = AutoModelForSequenceClassification.from_pretrained("NDugar/1epochv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from NDugar/1epochv3: direct link, hf CLI and curl.
- Browser
- Download file 2.8 kB
-
https://huggingface.co/NDugar/1epochv3/resolve/main/training_args.bin
- Command line
-
hf download hf://NDugar/1epochv3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/NDugar/1epochv3/resolve/main/training_args.bin
2.8 kB
- Xet hash:
- 3298ad9469c95dcf4aa3a54afa704bcdf186820fb6e68ba9a9b488a92de79031
- Size of remote file:
- 2.8 kB
- SHA256:
- b7049bdba5214197f455f596374cc7e1f5e563cbef16d548b943e8353a525ef9
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