Instructions to use ncsu-dk-lab/AutoDisProxyT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncsu-dk-lab/AutoDisProxyT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ncsu-dk-lab/AutoDisProxyT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ncsu-dk-lab/AutoDisProxyT") model = AutoModelForMaskedLM.from_pretrained("ncsu-dk-lab/AutoDisProxyT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83caf1b6d829d983a21cf2ac2b4bbb334030476cbe72a6b26ff8fc61e1f5ec6e
- Size of remote file:
- 34.3 MB
- SHA256:
- 03a0e825d55b18014148187006a3b74c85de9caa7915c02d00965fcb6d025b2a
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