Instructions to use ncsu-dk-lab/AutoDisProxyT-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncsu-dk-lab/AutoDisProxyT-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ncsu-dk-lab/AutoDisProxyT-RTE")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ncsu-dk-lab/AutoDisProxyT-RTE") model = AutoModelForSequenceClassification.from_pretrained("ncsu-dk-lab/AutoDisProxyT-RTE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec2b6ff06f4b59521e89b013ef092dbdada797e790d21d2f5a5158027304e173
- Size of remote file:
- 34.3 MB
- SHA256:
- 2e23f115d9c836af156b71b1ba1d5a7cb0679f6944c4597942a2b31db336e9f2
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