Instructions to use StevenLimcorn/unsup-simcse-roberta-large-semeval2016-laptops with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StevenLimcorn/unsup-simcse-roberta-large-semeval2016-laptops with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="StevenLimcorn/unsup-simcse-roberta-large-semeval2016-laptops")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("StevenLimcorn/unsup-simcse-roberta-large-semeval2016-laptops") model = AutoModel.from_pretrained("StevenLimcorn/unsup-simcse-roberta-large-semeval2016-laptops", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from StevenLimcorn/unsup-simcse-roberta-large-semeval2016-laptops: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/StevenLimcorn/unsup-simcse-roberta-large-semeval2016-laptops/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://StevenLimcorn/unsup-simcse-roberta-large-semeval2016-laptops/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/StevenLimcorn/unsup-simcse-roberta-large-semeval2016-laptops/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- ccc30a5e03dd27a1bd6a2d5621ff3089753af2506f94e99648e86e4fe6a70a1c
- Size of remote file:
- 1.42 GB
- SHA256:
- 9f375dc059c60539ac4c60eadbeaad7cfafa4b3de2c8a695953d65664cd09ca3
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