PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text
  Embedding
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.
690099a6646208eac0d1ef97
Iliass Ayaou
claimed_verified
2025-10-29T12:49:32.842Z
67d2f1d89c0aff2e5e498f3c
https://cdn-avatars.huggingface.co/v1/production/uploads/67d2f1d89c0aff2e5e498f3c/5yckUjssk77GwMucCLkKx.png
iliass ayaou
datalyes
user
datalyes
690099a6646208eac0d1ef98
Denis Cavallucci
PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text
  Embedding
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.
PatenTEB is a comprehensive benchmark for patent text embeddings with 15 tasks, and the patembed model family demonstrates strong generalization across various patent-specific challenges.
domain-stratified splits domain specific hard negative mining asymmetric fragment-to-document matching multi-task training span context lengths V-measure NDCG@100 domain-pretrained initialization