You can deploy this model and start earning money today!

BGE-M3 fine-tuned with Matryoshka + MNRLoss

Name: InfectaVec-v2
Availability: InStock

This is a sentence-transformers model finetuned from BAAI/bge-m3 on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. The main difference with InfectaVec-v1 model is that InfectaVec-v2 is trained with paraphrased and bitext mined queries (En to Kr, Kr to En).

Model Details

Model Description

Model Type: Sentence Transformer
Base model: BAAI/bge-m3
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- json
Language: en, kr
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Yesimm/InfectaVec-v1")
# Run inference
sentences = [
    '최근 몇 년간 SFTS의 발생 추세는 어떤가요?',
    '16년  23주차  (6.5-6.11)\n2016.  6.  13\n질병관리본부\n주간  국내외  감염병  동향\n◈ (국내) 수족구병 증가 지속, 중증열성혈소판감소증후군 유행시기 진입\n◈ (국외) 사우디 메르스 발생 소강 상태, 지카 중남미에서 유행 지속\n□  국내  주요  감염병  동향\n○ (수족구병) 계절적 유행시기로 증가 지속, 6월말 정점에 이를 것으로 예상\n* 최근 5주 수족구병 의사환자(외래환자 1,000명당) : 7.7→10.4→13.9→21.5→31.2\n○ (중증열성혈소판감소증후군(SFTS)) 계절적 유행시기 진입\n- 5월 이후 환자 발생 증가, 10월까지 지속 예상\n* SFTS 환자발생 현황 : 3월 0명→ 4월 3명→ 5월 9명\n○ (A형간염) 부산(사상구), 충남(서산, 홍성), 전남(목포), 경남(창원) 중심으로발생증가지속\n* 최근 5주 A형간염 신고건수 : 149→150→148→153→150(작년 동기간 주별 평균 38.6건)\n□  국외  주요  감염병  동향\n○ (메르스) 사우디아라비아에서 소강 상태(’16년 104명)\n* 최근 5주 사우디아라비아 메르스 발생 추세 : 3명→0명→1명→0명→1명\n○ (지카바이러스) 중남미 등지에서 유행 지속(53개국)\n- 일부 중남미 국가는 감소추세, 카리브해 국가들에서 증가 추세(WHO, 5.26)\n* 국내 지카 감염자 발생 현황 : 5명 (방문국 : 필리핀(3), 베트남(1), 브라질(1))\n○ (조류독감) 중국에서 조류인플루엔자 H7N9, H5N6 인체감염 발생 지속\n* 중국내 H7N9 확진자 : ’16.1월20명→2월18명→3월43명→4월4명→5월20명→6월1명\n* 중국내 H5N6 확진자 : ’16년9명보고(5월2명)\n○ (황열) 앙골라 및 주변국에서 유행 지속, 남미에서 발생 보고\n- 앙골라 의심환자 2,893명(확진 788, 사망 325), DR콩고 의심환자 670명\n(확진 52, 사망 63), 우간다 의심환자 60명(확진 7)\n- 페루 의심환자 32명(확진 14, 사망 8), 브라질 확진환자 1명 보고 16년  25주차(6.19-6.25)\n2016.  6.  27\n질병관리본부\n주간  국내외  감염병  동향\n◈  (국내)  수족구  급증  지속,  SFTS  발생증가  및 올해 첫  사망자 발생\n◈  (국외)  메르스 사우디 병원 내 유행 및 1차 감염 증가로 6월 발생 크게 증가\n□  국내  주요  감염병  동향\n○  (수족구병)  계절적  유행시기로  급증  지속\n-  6월말  정점에  이르고  8월까지  유행  예상\n* 최근 5주 수족구병 의사환자(외래환자 1,000명당) : 13.9→21.5→30.6→36.3→43.5명\n○  (중증열성혈소판감소증(SFTS))  계절적 유행시기 진입, 올해 첫 사망자 발생\n-  5월  이후  환자  발생  증가,  10월까지  지속  예상\n* SFTS 환자발생 현황 : 3월 0명→4월 3명→5월 10명→ 6월(6.22 기준) 4명\n□  국외  주요  감염병  동향\n○  (메르스)  사우디 병원 내 유행 및 1차 감염 증가 등으로 6월 발생 크게 증가\n* 사우디 리야드시 병원(King Khalid) 내 2차 감염(6.16~25) : 22명 (환자 7, 의료진 15)\n* 사우디 메르스 발생 현황(최근 5주간) : 0명→1명→1명→25명→11명\n○  (지카바이러스)  중남미  등지에서  발생  증가  및  유행  지속(54개국)\n-  WHO  긴급위원회  개최  결과  국제공중보건  위기상황  유지  결정(6.14)\n○  (조류독감)  중국에서  조류인플루엔자  H7N9  인체감염  발생  지속\n* 중국내 H7N9 확진자 : ’16.1월 20명→ 2월 18명→ 3월 43명→ 4월 4명→ 5월 20명\n→ 6월 14명\n○    (황열)  앙골라  및  주변국에서  유행  지속,  남미에서  발생지역  확대\n-  앙골라  의심환자  3,294명(확진  861,  사망  347)  보고\n* 확진자 변동 : 483명(3.30) → 653명(4.29) → 788명(6.1) → 847명(6.15) → 861명(6.17)\n* DR콩고 의심환자 1,106명(확진 68, 사망 75), 우간다 의심환자 68명(확진 7)\n* 남미지역 페루(확진 43), 브라질(확진 1) 외 콜롬비아(확진 1)에서 첫 환자 발생 표\nⅢ\n-1-2-46.\n중증열성혈소판감소증후군 연도별 월별 발생수\nTable -1-2-46. Reported cases of Severe fever with thrombocytopenia syndrome by year and month\nⅢ\n,\n:\n단위 환자수 명\n계\nTotal\n구분\n월1\nJan\n월2\nFeb\n월3\nMar\n월4\nApr\n월5\nMay\n월6\nJun\n월7\nJul\n월8\nAug\n월9\nSep\n월10\nOct\n월11\nNov\nUnit : case\n월12\nDec\n1980\n1981\n1982\n1983\n1984\n1985\n1986\n1987\n1988\n1989\n1990\n1991\n1992\n1993\n1994\n1995\n1996\n1997\n1998\n1999\n2000\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n36\n55\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n0\n0\n0\n1\n주\n년\n) 2013 , 2014\n년 통계는 확진환자만 집계함\nⅢ\n법\n정\n감\n염\n병\n전\n수\n감\n시\n통\n계\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n7\n2\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n4\n7\n10\n11\n6\n9\n2\n11\n5\n12\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n2\n2\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n0\n0\n.Ⅲ 법정감염병 전수감시 통계 • 111\n\n<table>\n<tbody>\n<tr>\n<td></td>\n</tr>\n<tr>\n<td>Ⅲ<br/>-1.<br/>연도별 통계</td>\n</tr>\n<tr>\n<td>Ⅲ<br/>-1-3.<br/>연도별 연령별 성별 발생현황</td>\n</tr>\n</tbody>\n</table>\n\n표\n단위 환자수 명\n-1-3-1.\n,\nⅢ\n:\n감염병 연도별 및 연령별 성별 발생수\n, 2001-2014\n연령 및 성\n총 계\nTotal\n남 자\nMale\n계\nTotal\n세\n0 ~ 9\nage\n10 ~ 19\n20 ~ 29\n30 ~ 39\n여 자\nFemale\n계\nTotal\n남 자\nMale\n여 자\nFemale\n계\nTotal\n남 자\nMale\n여 자\nFemale\n계\nTotal\n남 자\nMale\n여 자\nFemale\n계\nTotal\n남 자\nMale\n질병명 및 연도\n제 군1 콜레라\n장티푸스\nⅢ\n법\n정\n감\n염\n병\n전\n수\n감\n시\n통\n계\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n파라티푸스 2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n세균성이질\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n114 •\n2014\n감염병 감시연보\n162\n4\n1\n10\n16\n5\n7\n5\n0\n8\n3\n0\n3\n0\n401\n221\n199\n174\n190\n200\n223\n188\n168\n133\n148\n129\n156\n251\n36\n413\n88\n45\n31\n50\n45\n44\n36\n55\n56\n58\n54\n37\n927\n767\n1,117\n487\n317\n389\n131\n209\n180\n228\n171\n90\n294\n110\n99\n1\n1\n6\n7\n2\n3\n3\n0\n5\n1\n0\n1\n0\n185\n116\n99\n85\n97\n100\n109\n77\n87\n71\n79\n66\n78\n117\n23\n223\n49\n26\n17\n37\n29\n24\n25\n35\n37\n32\n28\n28\n408\n330\n545\n241\n139\n179\n56\n96\n59\n82\n74\n35\n129\n47\n63\n3\n0\n4\n9\n3\n4\n2\n0\n3\n2\n0\n2\n0\n216\n105\n100\n89\n93\n100\n114\n111\n81\n62\n69\n63\n78\n134\n13\n190\n39\n19\n14\n13\n16\n20\n11\n20\n19\n26\n26\n9\n519\n437\n572\n246\n178\n210\n75\n113\n121\n146\n97\n55\n165\n63\n3\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n39\n21\n24\n17\n23\n16\n27\n12\n10\n11\n9\n10\n7\n8\n8\n38\n13\n3\n3\n3\n7\n12\n12\n14\n9\n6\n11\n2\n274\n272\n471\n94\n64\n110\n11\n20\n16\n17\n15\n4\n16\n31\n2\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n17\n10\n14\n13\n7\n11\n15\n3\n9\n5\n5\n6\n5\n4\n4\n20\n10\n2\n2\n2\n4\n5\n7\n9\n7\n3\n5\n2\n140\n150\n253\n54\n36\n52\n4\n11\n6\n11\n7\n2\n8\n15\n1\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n22\n11\n10\n4\n16\n5\n12\n9\n1\n6\n4\n4\n2\n4\n4\n18\n3\n1\n1\n1\n3\n7\n5\n5\n2\n3\n6\n0\n134\n122\n218\n40\n28\n58\n7\n9\n10\n6\n8\n2\n8\n16\n5\n0\n0\n0\n0\n0\n0\n0\n0\n3\n0\n0\n0\n0\n41\n24\n14\n18\n22\n17\n36\n15\n22\n22\n24\n13\n14\n24\n2\n36\n2\n4\n2\n3\n7\n1\n2\n3\n2\n7\n4\n3\n125\n133\n218\n192\n24\n58\n16\n28\n12\n22\n25\n12\n104\n20\n4\n0\n0\n0\n0\n0\n0\n0\n0\n2\n0\n0\n0\n0\n22\n11\n7\n7\n11\n8\n16\n6\n13\n12\n16\n8\n7\n9\n0\n22\n1\n2\n1\n1\n4\n0\n2\n2\n0\n2\n2\n3\n59\n69\n122\n110\n12\n45\n10\n19\n4\n8\n10\n7\n63\n11\n1\n0\n0\n0\n0\n0\n0\n0\n0\n1\n0\n0\n0\n0\n19\n13\n7\n11\n11\n9\n20\n9\n9\n10\n8\n5\n7\n15\n2\n14\n1\n2\n1\n2\n3\n1\n0\n1\n2\n5\n2\n0\n66\n64\n96\n82\n12\n13\n6\n9\n8\n14\n15\n5\n41\n9\n23\n1\n0\n4\n7\n3\n3\n1\n0\n0\n0\n0\n1\n0\n75\n31\n37\n33\n31\n41\n30\n17\n27\n12\n30\n26\n14\n31\n2\n73\n21\n12\n9\n18\n5\n10\n4\n9\n13\n21\n9\n7\n230\n63\n62\n45\n25\n36\n17\n24\n22\n54\n54\n25\n78\n18\n18\n0\n0\n2\n3\n1\n2\n1\n0\n0\n0\n0\n0\n0\n35\n18\n20\n14\n17\n20\n15\n7\n8\n7\n19\n10\n9\n14\n2\n40\n7\n5\n5\n12\n4\n7\n3\n4\n7\n12\n3\n6\n103\n19\n20\n24\n8\n11\n7\n7\n11\n16\n22\n12\n29\n8\n5\n1\n0\n2\n4\n2\n1\n0\n0\n0\n0\n0\n1\n0\n40\n13\n17\n19\n14\n21\n15\n10\n19\n5\n11\n16\n5\n17\n0\n33\n14\n7\n4\n6\n1\n3\n1\n5\n6\n9\n6\n1\n127\n44\n42\n21\n17\n25\n10\n17\n11\n38\n32\n13\n49\n10\n32\n0\n1\n2\n2\n1\n1\n1\n0\n1\n0\n0\n1\n0\n50\n27\n28\n14\n19\n24\n33\n24\n29\n22\n22\n21\n28\n37\n10\n94\n23\n6\n6\n9\n14\n3\n5\n7\n11\n6\n9\n5\n112\n72\n100\n33\n25\n47\n19\n21\n10\n7\n21\n9\n28\n7\n25\n0\n1\n1\n1\n0\n0\n1\n0\n1\n0\n0\n1\n0\n21\n18\n13\n9\n12\n13\n17\n9\n15\n15\n13\n17\n9\n21\n9\n56\n15\n3\n4\n9\n8\n2\n4\n5\n8\n3\n5\n3\n39\n21\n43\n14\n16\n22\n9\n12\n5\n3\n8\n3\n10\n3\n\nⅢ\n법\n정\n감\n염\n병\n전\n수\n감\n시\n통\n계\nTable -1-3-1. Reported cases of infectious diseases by age and sex, 2001-2014\nⅢ\n40 ~ 49\n50 ~ 59\n60 ~ 69\n70 ≤\nAge and Sex\n여 자\nFemale\n계\nTotal\n남 자\nMale\n여 자\nFemale\n계\nTotal\n남 자\nMale\n여 자\nFemale\n계\nTotal\n남 자\nMale\n여 자\nFemale\n계\nTotal\n남 자\nMale\n여 자\nFemale\nUnit : case\nYear\nand Diseases\nGroup Ⅰ\n7\n0\n0\n1\n1\n1\n1\n0\n0\n0\n0\n0\n0\n0\n29\n9\n15\n5\n7\n11\n16\n15\n14\n7\n9\n4\n19\n16\n1\n38\n8\n3\n2\n0\n6\n1\n1\n2\n3\n3\n4\n2\n73\n51\n57\n19\n9\n25\n10\n9\n5\n4\n13\n6\n18\n4\n36\n1\n0\n1\n1\n0\n2\n1\n0\n1\n3\n0\n1\n0\n104\n61\n48\n42\n38\n34\n36\n44\n20\n19\n16\n17\n20\n27\n7\n96\n19\n11\n4\n10\n4\n7\n4\n7\n10\n9\n8\n6\n72\n46\n84\n30\n35\n43\n10\n22\n19\n33\n13\n5\n17\n6\n23\n0\n0\n1\n0\n0\n1\n1\n0\n0\n1\n0\n0\n0\n51\n36\n28\n21\n26\n19\n19\n19\n11\n11\n7\n10\n9\n12\n5\n51\n14\n9\n3\n10\n3\n4\n4\n5\n7\n8\n7\n4\n32\n19\n41\n13\n14\n18\n5\n16\n6\n11\n11\n1\n7\n5\n13\n1\n0\n0\n1\n0\n1\n0\n0\n1\n2\n0\n1\n0\n53\n25\n20\n21\n12\n15\n17\n25\n9\n8\n9\n7\n11\n15\n2\n45\n5\n2\n1\n0\n1\n3\n0\n2\n3\n1\n1\n2\n40\n27\n43\n17\n21\n25\n5\n6\n13\n22\n2\n4\n10\n1\n21\n1\n0\n1\n6\n0\n0\n1\n0\n0\n0\n0\n0\n0\n64\n28\n23\n22\n33\n32\n26\n51\n31\n26\n22\n19\n41\n57\n3\n49\n5\n2\n1\n5\n5\n6\n3\n7\n7\n5\n11\n5\n55\n50\n46\n32\n56\n36\n19\n24\n30\n38\n13\n11\n17\n12\n12\n1\n0\n1\n3\n0\n0\n0\n0\n0\n0\n0\n0\n0\n27\n12\n9\n11\n15\n15\n11\n25\n17\n10\n10\n8\n22\n29\n2\n20\n1\n2\n0\n2\n4\n3\n1\n6\n6\n3\n6\n4\n15\n11\n18\n8\n22\n11\n4\n9\n6\n14\n7\n6\n6\n2\n9\n0\n0\n0\n3\n0\n0\n1\n0\n0\n0\n0\n0\n0\n37\n16\n14\n11\n18\n17\n15\n26\n14\n16\n12\n11\n19\n28\n1\n29\n4\n0\n1\n3\n1\n3\n2\n1\n1\n2\n5\n1\n40\n39\n28\n24\n34\n25\n15\n15\n24\n24\n6\n5\n11\n10\n31\n0\n0\n2\n0\n1\n0\n1\n0\n3\n0\n0\n0\n0\n25\n23\n20\n18\n12\n25\n26\n16\n19\n13\n13\n12\n20\n36\n3\n22\n4\n5\n4\n1\n3\n4\n4\n3\n3\n3\n2\n2\n39\n72\n68\n35\n49\n41\n24\n26\n38\n26\n14\n14\n11\n5\n12\n0\n0\n1\n0\n1\n0\n0\n0\n2\n0\n0\n0\n0\n12\n10\n8\n8\n4\n11\n12\n6\n11\n4\n5\n4\n12\n20\n1\n10\n1\n2\n2\n0\n2\n2\n2\n2\n2\n0\n0\n2\n15\n21\n24\n9\n16\n17\n12\n9\n10\n10\n4\n3\n1\n1\n19\n0\n0\n1\n0\n0\n0\n1\n0\n1\n0\n0\n0\n0\n13\n13\n12\n10\n8\n14\n14\n10\n8\n9\n8\n8\n8\n16\n2\n12\n3\n3\n2\n1\n1\n2\n2\n1\n1\n3\n2\n0\n24\n51\n44\n26\n33\n24\n12\n17\n28\n16\n10\n11\n10\n4\n11\n1\n0\n0\n0\n0\n1\n0\n0\n0\n0\n0\n0\n0\n3\n6\n5\n10\n12\n11\n9\n9\n10\n8\n12\n11\n12\n31\n1\n5\n1\n2\n2\n1\n0\n1\n2\n5\n1\n1\n0\n7\n20\n59\n68\n26\n39\n18\n15\n44\n33\n31\n16\n10\n23\n11\n3\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n1\n0\n2\n5\n3\n4\n2\n3\n7\n4\n3\n5\n8\n0\n4\n0\n1\n0\n1\n0\n1\n2\n2\n0\n1\n0\n4\n5\n20\n24\n9\n15\n3\n5\n13\n11\n9\n5\n1\n5\n2\n8\n1\n0\n0\n0\n0\n1\n0\n0\n0\n0\n0\n0\n0\n3\n5\n5\n8\n7\n8\n5\n7\n7\n1\n8\n8\n7\n23\n1\n1\n1\n1\n2\n0\n0\n0\n0\n3\n1\n0\n0\n3\n15\n39\n44\n17\n24\n15\n10\n31\n22\n22\n11\n9\n18\n9\nCholera\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\nTyphoid fever\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\nParatyphoid fever\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\nShigellosis\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n.Ⅲ 법정감염병 전수감시 통계 • 115\n\n표\nⅢ\n-1-3-1.\n단위 환자수 명\n:\n,\n감염병 연도별 및 연령별 성별 발생수\n, 2001-2014(\n)\n계속\nⅢ\n법\n정\n감\n염\n병\n전\n수\n감\n시\n통\n계\n질병명 및 연도\n연령 및 성\n총 계\nTotal\n남 자\nMale\n계\nTotal\n여 자\nFemale\n계\nTotal\n세\n0 ~ 9\nage\n남 자\nMale\n10 ~ 19\n20 ~ 29\n30 ~ 39\n여 자\nFemale\n계\nTotal\n남 자\nMale\n여 자\nFemale\n계\nTotal\n남 자\nMale\n여 자\nFemale\n계\nTotal\n남 자\nMale\n제 군1 장출혈성\n2001\n대장균감염증 2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n형간염A\n제 군2 디프테리아\n백일해\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n2001\n2002\n2003\n2004\n2005\n2006\n2007\n2008\n2009\n2010\n2011\n2012\n2013\n2014\n11\n8\n52\n118\n43\n37\n41\n58\n62\n56\n71\n58\n61\n111\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n5,521\n1,197\n867\n1,307\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n9\n21\n5\n6\n11\n17\n14\n9\n66\n27\n97\n230\n36\n88\n3\n4\n27\n57\n22\n14\n20\n32\n27\n23\n34\n35\n27\n64\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n3,433\n728\n494\n771\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n6\n10\n2\n2\n6\n8\n3\n6\n28\n17\n41\n103\n18\n43\n8\n4\n25\n61\n21\n23\n21\n26\n35\n33\n37\n23\n34\n47\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n2,088\n469\n373\n536\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n3\n11\n3\n4\n5\n9\n11\n3\n38\n10\n56\n127\n18\n45\n6\n8\n35\n63\n32\n25\n28\n20\n35\n39\n40\n35\n39\n67\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n29\n14\n13\n8\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n9\n18\n5\n6\n11\n16\n14\n9\n60\n24\n61\n47\n21\n56\n2\n4\n20\n30\n16\n11\n16\n12\n19\n17\n19\n19\n18\n40\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n15\n8\n7\n4\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n6\n8\n2\n2\n6\n8\n3\n6\n24\n16\n28\n19\n12\n25\n4\n4\n15\n33\n16\n14\n12\n8\n16\n22\n21\n16\n21\n27\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n14\n6\n6\n4\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n3\n10\n3\n4\n5\n8\n11\n3\n36\n8\n33\n28\n9\n31\n0\n0\n10\n40\n2\n2\n2\n8\n3\n2\n4\n6\n6\n13\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n388\n86\n56\n52\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n3\n1\n6\n159\n4\n10\n0\n0\n5\n23\n1\n1\n1\n6\n1\n1\n3\n5\n3\n9\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n232\n48\n29\n26\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n2\n1\n3\n73\n2\n7\n0\n0\n5\n17\n1\n1\n1\n2\n2\n1\n1\n1\n3\n4\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n156\n38\n27\n26\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n1\n0\n3\n86\n2\n3\n2\n0\n2\n3\n2\n0\n0\n0\n5\n2\n4\n5\n0\n5\n0\n0\n1\n0\n1\n0\n0\n0\n1\n0\n2\n4\n0\n2\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n1,753\n327\n253\n328\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n1,011\n203\n148\n183\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n1\n0\n0\n0\n0\n0\n0\n1\n0\n7\n3\n3\n3\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n2\n3\n3\n1\n2\n0\n1\n3\n1\n0\n0\n0\n4\n2\n2\n1\n0\n3\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n742\n124\n105\n145\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n1\n0\n0\n0\n0\n0\n0\n1\n0\n5\n0\n0\n2\n1\n0\n1\n4\n2\n5\n1\n3\n3\n5\n4\n3\n4\n6\n1\n0\n0\n3\n2\n1\n0\n2\n2\n2\n1\n1\n1\n2\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n2,443\n519\n358\n545\n-\n-\n-\n-\n-\n-\n-\n-\n-\n-\n1,590\n314\n212\n336\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n1\n0\n0\n1\n1\n10\n7\n4\n2\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n1\n0\n2\n3\n0\n1\n116 •\n2014\n감염병 감시연보',
    'AVIAN INFLUENZA (25): EUROPE (UK) SEAL, HPAI H5N1 ************************************************* A ProMED-mail post http://www.promedmail.org ProMED-mail is a program of the International Society for Infectious Diseases http://www.isid.org Date: Wed 8 Feb 2023 Source: Agriland [edited] https://www.agriland.co.uk/farming-news/bird-flu-found-in-seals-along-scotlands-east-coast/ The H5N1 strain of avian influenza (bird flu), the strain most common to birds during this current outbreak, was found in 4 seals along the east coast of Scotland last week.\nThe affected seals, one grey seal and 3 harbour seals, were found in 4 coastal locations at Orkney; Highland; Aberdeenshire; and Fife.\nThere have been other cases of bird flu in Scotland since the turn of the year [2023]: Near the seaside towns of Fraserburgh, Aberdeenshire on 12 Jan 2023; near Tain, Highland on 21 Jan 2023; Near Grantown on Spey, Highland on 24 Jan 2023; Clackmannan, Clackmannanshire, also on 24 Jan 2023; Stranraer, Wigtownshire, Dumfries and Galloway on 28 Jan 2023; and near Crossgates, Fife on 2 Feb 2023.\nIt is thought that the current outbreak of bird flu, which began in October 2021, is a result of wild birds that are carrying the disease migrating from the east.\nIt is not possible to determine that bird flu was the sole cause of death in these animals. "\nRoutine wildlife surveillance in the UK has detected the H5N1 strain in a small number of mammals over the past 2 years, although it is uncertain whether these died from avian influenza of other causes," a spokesperson for NatureScot, which is concerned about the overall current bird flu outbreak, said. "\nThis is an unusual event in the UK and infection of mammals with influenza of avian origin remains an uncommon infection.\nThe risk of the H5N1 strain to non-avian UK wildlife remains low."\nAside from the seals, this year [2023], only one other mammal, a fox in Wales, has tested positive [in the UK] for influenza of avian origin.\nLast week, UK officials confirmed that "enhanced" mammalian surveillance for avian influenza was initiated at the beginning of the year [2023], mostly targeting mammals found dead near known areas of bird flu transmission.\nHowever, the Animal and Plant Health Agency (APHA) has said that evidence of mammal-to-mammal transmission in the wild remains very limited and data does not suggest widespread mammalian adaption of the virus. "\nInfection of mammals with influenza of avian origin remains uncommon and the risk of the H5N1 strain to non-avian UK wildlife remains low," an APHA spokesperson said. "\nSamples taken as part of routine wildlife surveillance over the last year have detected the presence of influenza of avian origin in 5 foxes, 4 otters and 4 seals that were found dead -- which is an uncommon event.\nAdvice remains unchanged to not touch any sick or dead wild animals and make sure to wash your hands thoroughly with soap after contact with any animal."\nChief superintendent of Scottish Society for the Prevention of Cruelty to Animals (SSPCA), Mike Flynn, is also urging people to keep their distance from sick, injured and dead animals. "\nWe strongly advise that people keep their distance from any sick, injured or dead marine animals.\nDogs should also be kept on a lead around wildlife at all times," he said on behalf of SSPCA. "\nAny birds, alive or dead, suspected of having avian flu would be a matter for Defra (the Department for Environment, Food and Rural Affairs) to investigate."\nConcerns can also be directed to the SSPCA or NatureScot.\nDead seals, whales and dolphins should be reported to the Scottish Marine Animal Stranding Scheme. [\nByline: Michelle Martin] -- Communicated by: ProMED Rapporteur Mahmoud Orabi [HPAI H5N1 viruses have the ability to infect a number of mammalian species, many being classified in the order Carnivora.\nSeals are also part of the Order Carnivora, although they are of the Suborder Pinnipedia (terrestrial carnivores are Fissipedia).\nSpread of the virus amongst carnivores is likely attributed to predatory habits, as H5N1 has been isolated from the meat of infected animals and ingestion of infected meat can cause infection.\nHPAI H5N8 was detected in 2 grey seals stranded on the Baltic coast of Poland in 2016 and 2017.\nIn March 2021, the UK notified WOAH about the diagnosis of HPAI H5N8 in the remains of one grey seal and 4 harbour seals from Surrey (20210317.8252821).\nSince April 2022 there have been several events of Eurasian HPAI H5N1 in wild carnivores, including seals.\nMost cases in seals were recorded in North America.\nAs noted by Mod.\nPMB (20220705.8704251), several subtypes of AI viruses (H7N7, H4N5, H4N6, H3N3 and H10N7) have caused epidemics in harbour seals, but they do not appear to have become established in this carnivore species. -\nMod.\nAS ProMED map: Scotland, United Kingdom: https://promedmail.org/promed-post?place=8708269,280 ] See Also \n2021 ---- Avian influenza (45): Europe (UK, Croatia) seal, fox, wild bird, HPAI H5N8, OIE 20210317.8252821 2022 ---- Avian influenza (158): Americas (Canada) seal, HPAI H5N1 20220805.8704881 Avian influenza (146): Americas (USA) seal, HPAI H5N1 20220705.8704251 .................................................sb/arn/may/ml AVIAN INFLUENZA (158): AMERICAS (CANADA) SEAL, HPAI H5N1 ******************************************************** A ProMED-mail post http://www.promedmail.org ProMED-mail is a program of the International Society for Infectious Diseases http://www.isid.org Date: Tue 3 Aug 2022 Source: Tricity News, The Canadian Press report [edited] https://www.tricitynews.com/national-news/highly-pathogenic-avian-influenza-believed-to-be-killing-seals-in-quebec-5654452 Quebec researchers have detected avian flu in at least 2 species of seal, and they fear the virus is to blame for the unusually high number of dead seals reported on the province\'s shorelines.\nA marine mammal research group, the Réseau québécois d\'urgences pour les mammifères marins, says about 100 harbour seal carcasses have been found since January [2022] along the south shore of the St. Lawrence River in eastern Quebec -- almost 6 times more than in an average year. "\nIn June [2022] alone, the number reached 65 carcasses," the research group said in a statement on Tuesday [2 Aug 2022]. "\nAvian influenza was quickly suspected of playing a role in the increasing mortality."\nAbout 15 of the dead harbour seals have tested positive for the highly pathogenic H5N1 avian flu, with the 1st case detected in a grey seal last week, said Stéphane Lair, a professor of veterinary medicine at Université de Montréal.\nHe said the seals most likely were in contact with carcasses of infected eider ducks when they came ashore to give birth at the beginning of the summer. "\nSome seals, including the grey seal, are known for eating wild birds ... but not harbour seals," Lair said in an interview. "\nThey are curious, they will smell carcasses."\nJean-François Gosselin, a biologist with Fisheries and Oceans Canada, said these are the first reported cases of the virus being passed from wild birds to marine mammals in Quebec.\nThe 1st H5N1 flu cases are believed to have arrived in North America at the end of winter, involving birds that migrated from Europe.\nGosselin said the number of dead seals reported likely understates the reality. "\nIt\'s already difficult to count the number of seals that are alive ... carcasses that are beached, or floating between rocks, is even harder," Gosselin said.\nHe added that it\'s difficult to monitor all transmission, which most likely is affecting other species -- on land as well as marine mammals.\nGosselin and Lair both said that while they are monitoring the situation, there is no threat of transmission to humans or of endangering the seal population. "\nIt\'s normal with an exotic virus ... it\'s a new virus that enters a new population that was never infected," Lair said. "\nThe mortality rate will be way higher than if the virus was already circulating naturally." "\nWe need to be careful about all predators or animals that could be in contact with wild birds," Gosselin said.\nBoth recommended that people avoid approaching or touching animal carcasses and keep their pets, particularly dogs, away. [\nbyline: Virginie Ann] -- communicated by: ProMED rapporteur Mahmoud Orabi [Infections by HPAI viruses in mammals are typically not common, but since April 2022 there have been several events of Eurasian HPAI H5N1 in wild carnivores (order Carnivora), most of them in the Americas.\nMost reports have been from red foxes (_Vulpes vulpes_, family Canidae), but skunks (family Mephitidae), and raccoons (family Procyonidae), have also been found infected.\nSeals are also part of the Order Carnivora, although they are of the Suborder Pinnipedia (terrestrial carnivores are Fissipedia).\nIn March 2021, there was a report of HPAI H5N8 in harbor seals.\nSeveral subtypes of AI viruses (H7N7, H4N5, H4N6, H3N3, and H10N7) have caused epidemics in harbour seals, but they do not appear to become established in this carnivore species.\nHPAI H5N8 was detected in 2 gray seals stranded on the Baltic coast of Poland in 2016 and 2017 ( https://doi.org/10.3201/eid2512.181472 ).\nLast month [July 2022] 4 stranded seals tested positive for HPAI H5N1 in Maine, USA. -\nMod.\nPMB ProMED map: St. Lawrence River, Quebec, Canada: https://promedmail.org/promed-post?place=8704881,68774 ] See Also \nAvian influenza (146): Americas (USA) seal, HPAI H5N1 20220705.8704251 Avian influenza (139): Americas (USA, Canada) fox, HPAI 20220616.8703889 Avian influenza (131): Americas (USA, Canada) wild carnivores, HPAI H5N1 20220601.8703600 Avian influenza (124): Americas (USA) fox, HPAI H5N1 20220523.8703428 Avian influenza (118): Americas (USA) fox, HPAI H5N1, OIE 20220512.8703221 Avian influenza (115): Americas (Canada) fox, HPAI H5N1, OIE 20220509.8703152 Avian influenza (94): Japan, raccoon dog, HPAI H5N1, update 20220415.8702609 Avian influenza (93): Japan, raccoon dog, HPAI H5N1, RFI 20220413.8702568 Avian influenza (85): Asia (Japan) crow, fox, HPAI H5 20220406.8702440 Avian influenza (47): Europe, poultry, fox, HPAI H5N1, OIE 20220216.8701500 2021 --- Avian influenza (180): Europe (Estonia) fox, HPAI H5N1, OIE 20211222.8700432 Avian influenza (133): Europe (Netherlands) fox HPAI H5N1 20211101.8699389 Avian influenza (117): Europe (Germany) seal, HPAI H5N8 20210924.8698675 Avian influenza, human (02): Russia, H5N8, 1st rep 20210221.8204014 Avian influenza (45): Europe (UK, Croatia) seal, fox, wild bird, HPAI H5N8, OIE 20210317.8252821 .................................................pmb/mj/sh A NUMBER of wild birds across the north-east of Scotland are confirmed to have bird flu.\nA record number of cases of avian influenza (bird flu) have been confirmed across Scotland, England and Wales in wild and captive birds this winter.\nWhile avian influenza is not uncommon in winter and the risk to human health from infected birds is very low, a human case of avian ‘flu was confirmed in England earlier this month.\nPreviously observed seasonal migration patterns suggest an expected increase in flocks of wild birds across the north-east of Scotland over the next few weeks.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[     1.0000,      0.6473,     -0.0003],
#         [     0.6473,      1.0000,      0.0720],
#         [    -0.0003,      0.0720,      1.0000]])

Training Details

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 1024, 5 512 6 ], 7 "matryoshka_weights": [ 8 1, 9 1 10 ], 11 "n_dims_per_step": -1 12}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 4
per_device_eval_batch_size: 4
gradient_accumulation_steps: 8
learning_rate: 2e-05
weight_decay: 0.01
num_train_epochs: 4
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
tf32: True
optim: adamw_torch_fused
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 4
per_device_eval_batch_size: 4
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 8
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: True
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Framework Versions

Python: 3.10.0
Sentence Transformers: 5.0.0
Transformers: 4.52.4
PyTorch: 2.6.0+cu118
Accelerate: 1.6.0
Datasets: 4.0.0
Tokenizers: 0.21.1

Evaluation Results

Evaluation Results on Infectious Diseases Test Dataset

Model	Epoch	Accuracy(@1)	Recall(@1)	Precision(@10)	NDCG(@10)	MRR(@10)	MAP(@100)
BGE-M3	-	44.49	44.49	7.43	58.97	54.12	54.83
InfectaVec v1	2	62.67	62.67	9.37	78.21	73.23	73.58
	3	62.30	62.30	9.42	78.58	73.52	73.85
	4	62.83	62.83	9.46	78.92	73.87	74.18
InfectaVec v2	2	59.49	59.49	9.08	75.35	70.37	70.81
	3	61.08	61.08	9.16	76.43	71.55	71.98
	4	61.29	61.29	9.16	76.49	71.63	72.07

Evaluation Results on MTEB Medical Benchmarks for Retrieval, Clustering and Semantic Text Similarity Tasks

Models	PublicHealthQA (Kr)	PublicHealthQA (En)	MedrxivClusteringS2S.v2 (En)	BIOSSES (En)
BGE-M3	80.41	83.81	30.63	83.38
Multilingual e5-large	85.14	84.57	39.14	87.45
InfectaVec-v1	79.70	82.57	34.62	79.37
Qwen-3 Embedding-0.6B	81.10	83.84	40.38	84.73
InfectaVec-v2	82.36	84.85	34.23	76.51

Citation

BibTeX

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

bibtex

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Yesimm/InfectaVec-v2

BGE-M3 fine-tuned with Matryoshka + MNRLoss

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Framework Versions

Evaluation Results

Evaluation Results on Infectious Diseases Test Dataset

Evaluation Results on MTEB Medical Benchmarks for Retrieval, Clustering and Semantic Text Similarity Tasks

Citation

BibTeX

Citation

BibTeX

Sentence Transformers

MatryoshkaLoss

MultipleNegativesRankingLoss

Yesimm/
InfectaVec-v2