Sentence Similarity
Transformers
PyTorch
mt5
feature-extraction
cross-lingual
multilingual
question-answering
retrieval
variational
custom_code
Instructions to use jwieting/vmsst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jwieting/vmsst with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jwieting/vmsst", trust_remote_code=True) model = AutoModel.from_pretrained("jwieting/vmsst", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5fc75281d4853fe8b2c468ae28bf16a5f87b3936d22bf2ead28e029968f56d9c
- Size of remote file:
- 2.26 GB
- SHA256:
- ed70bb240affcc90a945b5905dc643778806ecf9e3c1ff6542de24fa70056228
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