Instructions to use djovak/reranker-MiniLM-L6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djovak/reranker-MiniLM-L6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="djovak/reranker-MiniLM-L6")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("djovak/reranker-MiniLM-L6") model = AutoModelForSequenceClassification.from_pretrained("djovak/reranker-MiniLM-L6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from djovak/reranker-MiniLM-L6: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/djovak/reranker-MiniLM-L6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://djovak/reranker-MiniLM-L6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/djovak/reranker-MiniLM-L6/resolve/main/pytorch_model.bin
90.9 MB
- Xet hash:
- d3e8be3d95639f708d1a14289e14f29751ea98d61b382cf0cc45198bd3dab95e
- Size of remote file:
- 90.9 MB
- SHA256:
- 144bcb949ccfec5057b52c22efc90e510460de49adb7e6715fa59e3b66def724
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