Instructions to use MoritzLaurer/deberta-v3-large-mnli-fever-anli-ling-wanli-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoritzLaurer/deberta-v3-large-mnli-fever-anli-ling-wanli-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-large-mnli-fever-anli-ling-wanli-binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MoritzLaurer/deberta-v3-large-mnli-fever-anli-ling-wanli-binary") model = AutoModelForSequenceClassification.from_pretrained("MoritzLaurer/deberta-v3-large-mnli-fever-anli-ling-wanli-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model description: deberta-v3-large-mnli-fever-anli-ling-wanli-binary
This model was mostly created as a comparative benchmark for another model, see here: https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33
This model was only trained on five NLI datasets, while the other model was trained on many more datasets. I mostly recommend using the other model. This NLI-only model might only be better for tasks that are not zeroshot classification but that adhere more strictly to the original NLI task.
See the other model's model card for usage instructions, training data and the paper.
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