Instructions to use AIArchiveInfo/deberta-v3-large-zeroshot-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIArchiveInfo/deberta-v3-large-zeroshot-v2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="AIArchiveInfo/deberta-v3-large-zeroshot-v2.0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AIArchiveInfo/deberta-v3-large-zeroshot-v2.0") model = AutoModelForSequenceClassification.from_pretrained("AIArchiveInfo/deberta-v3-large-zeroshot-v2.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AIArchiveInfo/deberta-v3-large-zeroshot-v2.0: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/AIArchiveInfo/deberta-v3-large-zeroshot-v2.0/resolve/main/training_args.bin
- Command line
-
hf download hf://AIArchiveInfo/deberta-v3-large-zeroshot-v2.0/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AIArchiveInfo/deberta-v3-large-zeroshot-v2.0/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 1318147c163e863fbbdefd86ef197af555a6d7a9e512041e1a0983252a2046b6
- Size of remote file:
- 4.92 kB
- SHA256:
- 7174ebede2e9f6479616b183d5cfb9e7fe2b0e0ae1b8ae1566788b50cd1c9a6e
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