Instructions to use fanjiang98/ABEL-Passage-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fanjiang98/ABEL-Passage-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fanjiang98/ABEL-Passage-Encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("fanjiang98/ABEL-Passage-Encoder") model = AutoModel.from_pretrained("fanjiang98/ABEL-Passage-Encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea0364b7bf89b632a61500174c6b97a2de810328567a6db2e8b2741d17fb909c
- Size of remote file:
- 438 MB
- SHA256:
- 48b3314ff4408fee61335804dc6705ef5ffe684ad5ca608d951395c5f2c16512
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