Instructions to use ielabgroup/Reproduced_Pairwise_Random_Msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ielabgroup/Reproduced_Pairwise_Random_Msmarco with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ielabgroup/Reproduced_Pairwise_Random_Msmarco")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ielabgroup/Reproduced_Pairwise_Random_Msmarco") model = AutoModel.from_pretrained("ielabgroup/Reproduced_Pairwise_Random_Msmarco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d9a253c1a0d77a6bae4a537449732e6f0dac9a8f1412c062261083780ceed86
- Size of remote file:
- 265 MB
- SHA256:
- a7baec5b07b377fbaf8641152782ff0910c9561d13b47f6efddbd85fa330a9d6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.