Transformers
PyTorch
t5
generative-retrieval
information-retrieval
msmarco
robustness
reproducibility
text-generation-inference
Instructions to use kiyam/lost-in-decoding-pag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kiyam/lost-in-decoding-pag with Transformers:
# Load model directly from transformers import AutoTokenizer, T5ForLexicalSemanticGeneration tokenizer = AutoTokenizer.from_pretrained("kiyam/lost-in-decoding-pag") model = T5ForLexicalSemanticGeneration.from_pretrained("kiyam/lost-in-decoding-pag", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aba58610dc219602a361d7b9d9076a935147b145ac06124defa491535d8a6fd7
- Size of remote file:
- 942 MB
- SHA256:
- 9069588676ced21d94df2234690a93aaafa9bac4b2a4018b11c11874c480c4a1
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