BTZSC: A Benchmark for Zero-Shot Text Classification Across Cross-Encoders, Embedding Models, Rerankers and LLMs | ScienceToStartup | ScienceToStartup

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References (59)

[1]

Maintaining MTEB: Towards Long Term Usability and Reproducibility of Embedding Benchmarks

2025Isaac Chung, Imene Kerboua et al.

[2]

Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

2025Yanzhao Zhang, Mingxin Li et al.

[3]

Qwen3 Technical Report

Founder's Pitch

"BTZSC is a benchmark for evaluating zero-shot text classification across various model families, promoting reproducibility and fair comparisons."

Text Classification Benchmarking•Score: 7•View PDF ↗

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BTZSC: A Benchmark for Zero-Shot Text Classification Across Cross-Encoders, Embedding Models, Rerankers and LLMs

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Founder's Pitch

"BTZSC is a benchmark for evaluating zero-shot text classification across various model families, promoting reproducibility and fair comparisons."

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BTZSC: A Benchmark for Zero-Shot Text Classification Across Cross-Encoders, Embedding Models, Rerankers and LLMs

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References (59)

Founder's Pitch

"BTZSC is a benchmark for evaluating zero-shot text classification across various model families, promoting reproducibility and fair comparisons."

Commercial Viability Breakdown

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