Model Details
Full Model IDcross-encoder-testing/reranker-bert-tiny-gooaq-bce
Pipeline / Tasktext-ranking
Librarysentence-transformers
Downloads (all-time)156.2K
Likes0
Last Modified4/2/2025
Author / Orgcross-encoder-testing
PrivateNo � public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("text-ranking", model="cross-encoder-testing/reranker-bert-tiny-gooaq-bce")
# Run inference
result = pipe("Your input here")
print(result)����� Tags
sentence-transformerssafetensorsbertcross-encodertext-classificationgenerated_from_trainerdataset_size:578402loss:BinaryCrossEntropyLosstext-rankingenarxiv:1908.10084base_model:prajjwal1/bert-tinybase_model:finetune:prajjwal1/bert-tinylicense:apache-2.0co2_eq_emissionstext-embeddings-inferenceendpoints_compatibleregion:us
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⬇ Downloads156.2K
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🛠�� Requirements
- →Install: pip install sentence-transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.