Instructions to use dmis-lab/phrase-reranker-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmis-lab/phrase-reranker-nq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dmis-lab/phrase-reranker-nq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dmis-lab/phrase-reranker-nq") model = AutoModelForSequenceClassification.from_pretrained("dmis-lab/phrase-reranker-nq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dmis-lab/phrase-reranker-nq: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/dmis-lab/phrase-reranker-nq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dmis-lab/phrase-reranker-nq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dmis-lab/phrase-reranker-nq/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 512709b2ef57cf2236627e9e882c02f1b983d316c338b2eb2060a4fa62b5290a
- Size of remote file:
- 1.42 GB
- SHA256:
- 57fc94a142fbe37a386693778c41eebcf07326cd4099a8deeb0ed498f48fa55e
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