Instructions to use TigerByteCyber/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 TigerByteCyber/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="TigerByteCyber/deberta-v3-large-zeroshot-v2.0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TigerByteCyber/deberta-v3-large-zeroshot-v2.0") model = AutoModelForSequenceClassification.from_pretrained("TigerByteCyber/deberta-v3-large-zeroshot-v2.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06526292e3f8636cb9a56163a47f58c59e7bcd2b52adaddc7609e1dee29d9201
- Size of remote file:
- 1.74 GB
- SHA256:
- beded3d71421ceb718861af381d1a0d29037b3679c7350ebd41edbf79cdced1e
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