Instructions to use timpan/qee_rerank with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use timpan/qee_rerank with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="timpan/qee_rerank")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("timpan/qee_rerank") model = AutoModel.from_pretrained("timpan/qee_rerank", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8cb2a01f3f2ce82d7528ac147a07d75b4ed22adb9f7a94870053306645a7688c
- Size of remote file:
- 471 MB
- SHA256:
- 87957c17eb95106171118a995a35204b375802df10ccd89464e42e96a249439b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.