Instructions to use Maybe1407/diffusion_robustness_trec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maybe1407/diffusion_robustness_trec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Maybe1407/diffusion_robustness_trec")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Maybe1407/diffusion_robustness_trec") model = AutoModelForMaskedLM.from_pretrained("Maybe1407/diffusion_robustness_trec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2260a1b58ece8049fd6664f0d14f6edb0c1ee4fd7a58801346a6dc597b70f26a
- Size of remote file:
- 1.42 GB
- SHA256:
- 545fd6c0bddd4786ffccce0277c0913f60ee4a54ac1f4cad3b8e7bb35f308a04
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