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")# 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
- Xet hash:
- 2dad5a3591288658c127e89634e72bfaa848a1a8aa4e8883751232c8158fec97
- Size of remote file:
- 3.55 GB
- SHA256:
- 41fdfc7b911784d7dbc7dda1799ccf596dd5f714c996e63be6963589eac6dda7
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