Instructions to use ibraheemmoosa/mt-ranker-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibraheemmoosa/mt-ranker-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import MTRanker model = MTRanker.from_pretrained("ibraheemmoosa/mt-ranker-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ibraheemmoosa/mt-ranker-base: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/ibraheemmoosa/mt-ranker-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ibraheemmoosa/mt-ranker-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ibraheemmoosa/mt-ranker-base/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 7db5c8cd4b46ee104684849a7e92bf23d7d0991687b7bb643d2dcf91bf18db09
- Size of remote file:
- 1.11 GB
- SHA256:
- 003dbc0563107f6291d13e745629718edd856b021b38f7914b13d1a5ef7dfe75
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