Text Classification
Transformers
PyTorch
Chinese
English
xlm-roberta
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use neofung/bge-reranker-large-1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neofung/bge-reranker-large-1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="neofung/bge-reranker-large-1k")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("neofung/bge-reranker-large-1k") model = AutoModelForSequenceClassification.from_pretrained("neofung/bge-reranker-large-1k") - Notebooks
- Google Colab
- Kaggle
本模型是在 BAAI/bge-reranker-large 上对模型的 model.base_model.embeddings.position_embeddings.weight 依赖经验值进行修改,来扩大模型输入长度,并没有进行任何继续supervised fined tuning,作为新手示例。
同时亦附上在C-MTEB的分数作为对比。
感谢原作者的工作。
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Evaluation results
- map on MTEB CMedQAv1test set self-reported82.150
- mrr on MTEB CMedQAv1test set self-reported84.923
- map on MTEB CMedQAv2test set self-reported84.187
- mrr on MTEB CMedQAv2test set self-reported86.969
- map on MTEB MMarcoRerankingself-reported37.642
- mrr on MTEB MMarcoRerankingself-reported36.577
- map on MTEB T2Rerankingself-reported67.479
- mrr on MTEB T2Rerankingself-reported77.610