Instructions to use fanjiang98/ABEL-Passage-Encoder-Warmup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fanjiang98/ABEL-Passage-Encoder-Warmup with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fanjiang98/ABEL-Passage-Encoder-Warmup")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("fanjiang98/ABEL-Passage-Encoder-Warmup") model = AutoModel.from_pretrained("fanjiang98/ABEL-Passage-Encoder-Warmup", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d80dc5ce85e0f1c48574aec70fcf59057cadc3d6fa3e3c68c4c6a0846ccc562f
- Size of remote file:
- 438 MB
- SHA256:
- 2f741902105ff5e427db4d9df886a7f54f0b4505d191c1dda384c9b00f509470
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