Text Ranking
Transformers
English
rk-transformers
bert
text-classification
rknn
rockchip
npu
rk3588
text-embeddings-inference
Instructions to use rk-transformers/ms-marco-MiniLM-L12-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rk-transformers/ms-marco-MiniLM-L12-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rk-transformers/ms-marco-MiniLM-L12-v2") model = AutoModelForSequenceClassification.from_pretrained("rk-transformers/ms-marco-MiniLM-L12-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.rknn from rk-transformers/ms-marco-MiniLM-L12-v2: direct link, hf CLI and curl.
- Browser
- Download file 72.1 MB
-
https://huggingface.co/rk-transformers/ms-marco-MiniLM-L12-v2/resolve/main/model.rknn
- Command line
-
hf download hf://rk-transformers/ms-marco-MiniLM-L12-v2/model.rknn
-
curl -L -o model.rknn https://huggingface.co/rk-transformers/ms-marco-MiniLM-L12-v2/resolve/main/model.rknn
72.1 MB
- Xet hash:
- daa602440864ce336e1d8d22b4d641296b69b68bc75c10752fc7d1a77c4d5147
- Size of remote file:
- 72.1 MB
- SHA256:
- 646a50d03c63c0aa2745c6716cf5a25f79fa3b1ee39bd3d266d7fcf074e5e4d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.