Feature Extraction
Transformers
PyTorch
ONNX
bert
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use andersonbcdefg/bge-small-4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andersonbcdefg/bge-small-4096 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="andersonbcdefg/bge-small-4096")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("andersonbcdefg/bge-small-4096") model = AutoModel.from_pretrained("andersonbcdefg/bge-small-4096") - Notebooks
- Google Colab
- Kaggle
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Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported68.746
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported31.114
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported62.629
- accuracy on MTEB AmazonPolarityClassificationtest set self-reported81.303
- ap on MTEB AmazonPolarityClassificationtest set self-reported76.056
- f1 on MTEB AmazonPolarityClassificationtest set self-reported81.232
- accuracy on MTEB AmazonReviewsClassification (en)test set self-reported38.566
- f1 on MTEB AmazonReviewsClassification (en)test set self-reported38.015