PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text
  Embedding
Authors: name: Iliass Ayaou, name: Denis Cavallucci

Patent text embeddings enable prior art search, technology landscaping, and
patent analysis, yet existing benchmarks inadequately capture patent-specific
challenges. We introduce PatenTEB, a comprehensive benchmark comprising 15
tasks across retrieval, classification, paraphrase, and clustering, with 2.06
million examples. PatenTEB employs domain-stratified splits, domain specific
hard negative mining, and systematic coverage of asymmetric
fragment-to-document matching scenarios absent from general embedding
benchmarks. We develop the patembed model family through multi-task training,
spanning 67M to 344M parameters with context lengths up to 4096 tokens.
External validation shows strong generalization: patembed-base achieves
state-of-the-art on MTEB BigPatentClustering.v2 (0.494 V-measure vs. 0.445
previous best), while patembed-large achieves 0.377 NDCG@100 on DAPFAM.
Systematic ablations reveal that multi-task training improves external
generalization despite minor benchmark costs, and that domain-pretrained
initialization provides consistent advantages across task families. All
resources will be made available at https://github.com/iliass-y/patenteb.
Keywords: patent retrieval, sentence embeddings, multi-task learning,
asymmetric retrieval, benchmark evaluation, contrastive learning.