Instructions to use CaffreyR/mnli-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CaffreyR/mnli-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CaffreyR/mnli-lora")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CaffreyR/mnli-lora") model = AutoModelForSequenceClassification.from_pretrained("CaffreyR/mnli-lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5960f79ae226b6798927c54d0c74a6cd38700ce3e430cdb715589227ade8e371
- Size of remote file:
- 439 MB
- SHA256:
- ab944121284220c662ebb52a2941e7c9c7ff9caaf6245a460182b224f4d0fbfa
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