Instructions to use hle2000/mpnet_subgraphs_reraking_t5xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hle2000/mpnet_subgraphs_reraking_t5xl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hle2000/mpnet_subgraphs_reraking_t5xl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hle2000/mpnet_subgraphs_reraking_t5xl") model = AutoModelForSequenceClassification.from_pretrained("hle2000/mpnet_subgraphs_reraking_t5xl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hle2000/mpnet_subgraphs_reraking_t5xl: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/hle2000/mpnet_subgraphs_reraking_t5xl/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hle2000/mpnet_subgraphs_reraking_t5xl/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hle2000/mpnet_subgraphs_reraking_t5xl/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 28fc79f0661892d56d1e3da562eb09147fe70fa8a81bbd061641fc5741ab4599
- Size of remote file:
- 438 MB
- SHA256:
- 1534ce2bf5fdc82dbbedb2b8f390d64041726046981f30ab26247cf7cda236ae
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