Instructions to use UdS-LSV/muv2x-simcse-smole-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UdS-LSV/muv2x-simcse-smole-bert with Transformers:
# Load model directly from transformers import AutoTokenizer, BertForCL tokenizer = AutoTokenizer.from_pretrained("UdS-LSV/muv2x-simcse-smole-bert") model = BertForCL.from_pretrained("UdS-LSV/muv2x-simcse-smole-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26330cd2e16cdd240a114513f2d027b6a633fa4f1873944e030eec987de989e3
- Size of remote file:
- 2.1 kB
- SHA256:
- 0e20b9783889afd98075320ea4a1c3f1dd6be6c80a6354b0de1d48a1457826ae
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