Transformers
PyTorch
t5
text2text-generation
protein-generation
sequence-generation
conditional-generation
text-generation-inference
Instructions to use MoMA-LAAS/prop2seq-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoMA-LAAS/prop2seq-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MoMA-LAAS/prop2seq-model") model = AutoModelForSeq2SeqLM.from_pretrained("MoMA-LAAS/prop2seq-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69ce76ac4c96a21d3ae18cc43fce3430dc37c930c7da543937b2513106c62c0a
- Size of remote file:
- 194 kB
- SHA256:
- 1d3a595fde72cb1540803fde26d04f7f3cfa714a7200b7fa8575c55d50adc15d
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