Instructions to use LDCC/bge-reranker-v2-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LDCC/bge-reranker-v2-m3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LDCC/bge-reranker-v2-m3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LDCC/bge-reranker-v2-m3") model = AutoModelForSequenceClassification.from_pretrained("LDCC/bge-reranker-v2-m3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1968e6555b0f35d7ca0ebd7a5bc79c0dcb558c3962b918625780f60d61b6be1b
- Size of remote file:
- 2.27 GB
- SHA256:
- 9a748c82efb2079d24650c489e053dbb3c71d8acbbcf04d7b2340db66f2748f7
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