{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/focal-loss-with-logits","entry":"focal_loss_with_logits","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":5,"n_papers_ran":0,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":5,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":5},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2609.02292","paper":"/paper/arxiv-2609-02292","title":"SCX Router: Streaming Zero-Shot Model Selection with a Decoder-KV Classifier and a Real-World Task Ontology","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"Knowledgator/GLiClass","path":"gliclass/loss_functions.py","file_url":"https://github.com/Knowledgator/GLiClass/blob/HEAD/gliclass/loss_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ffb993318b61fff8","mcp_get_code":{"code_sha256":"ffb993318b61fff8"}},{"arxiv_id":"2602.18487","paper":"/paper/arxiv-2602-18487","title":"The Million-Label NER: Breaking Scale Barriers with GLiNER bi-encoder","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"urchade/GLiNER","path":"gliner/modeling/loss_functions.py","file_url":"https://github.com/urchade/GLiNER/blob/HEAD/gliner/modeling/loss_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"89ad194773a52ef8","mcp_get_code":{"code_sha256":"89ad194773a52ef8"}},{"arxiv_id":"2207.05289","paper":"/paper/plm-icd-automatic-icd-coding-with-pretrained-1","title":"PLM-ICD: Automatic ICD Coding with Pretrained Language Models","date":"2022-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"innerNULL/PLM-ICD-multi-label-classifier","path":"src/plm_icd_multi_label_classifier/loss.py","file_url":"https://github.com/innerNULL/PLM-ICD-multi-label-classifier/blob/HEAD/src/plm_icd_multi_label_classifier/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8498513641388fa5","mcp_get_code":{"code_sha256":"8498513641388fa5"}},{"arxiv_id":"2110.08988","paper":"/paper/feanet-feature-enhanced-attention-network-for","title":"FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation","date":"2021-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"matrixgame2018/FEANet","path":"loss_hub/losses/_functional.py","file_url":"https://github.com/matrixgame2018/FEANet/blob/HEAD/loss_hub/losses/_functional.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1b4fef545c17040d","mcp_get_code":{"code_sha256":"1b4fef545c17040d"}},{"arxiv_id":"2108.11250","paper":"/paper/yolop-you-only-look-once-for-panoptic-driving","title":"YOLOP: You Only Look Once for Panoptic Driving Perception","date":"2021-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"datvuthanh/HybridNets","path":"hybridnets/loss.py","file_url":"https://github.com/datvuthanh/HybridNets/blob/HEAD/hybridnets/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"edac15dd25a611c4","mcp_get_code":{"code_sha256":"edac15dd25a611c4"}}]}