Upload model

1_Pooling/config.json

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+{
+    "embedding_dimension": 1024,
+    "pooling_mode": "mean",
+    "include_prompt": true
+}

2_Dense/config.json

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+{
+    "in_features": 1024,
+    "out_features": 1792,
+    "bias": true,
+    "activation_function": "torch.nn.modules.linear.Identity",
+    "module_input_name": "sentence_embedding",
+    "module_output_name": "sentence_embedding"
+}

2_Dense/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a93f2b0d7a9370ac679f1d0aead979e26ba6e76dd006c6c440d7fc3b238974c0
+size 3673768

README.md

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+---
+tags:
+- unsloth
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:164633
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: IEITYuan/Yuan-embedding-2.0-zh
+widget:
+- source_sentence: 小狗憋不住尿
+  sentences:
+  - "狗憋不住尿是什么问题\n1、发育不完善\n\n如果是年龄较小的狗狗，由于排尿中枢发育不完善，就有可能会出现憋不住尿的情况。不过这种情况一般随着狗狗年龄的增长，憋尿时间也会得到进一步延长，所以主人不用太过担心。\n\
+    \n\t\n\n\n \n\n2、喝太多水\n\n如果狗狗近期喝了太多的水，或者吃了含水量较多的食物 ，狗狗的膀胱就会时刻饱满充盈着尿液，从而法控制尿液。这种情况也是正常的，只要狗狗正常吃喝，精神状态好，就不用太过紧张。\n\
+    \n\t\n\n\n \n\n3、受到惊吓\n\n有一些胆子比较小的狗狗，在受到惊吓之后也会出现憋不住尿或者漏尿的情况。这种情况属于机体正常的生理反馈，一般在狗狗惊吓过后就可以自行恢复，不需要特殊的处理，但主人平时也要尽量避免这种情况发生。\n\
+    \n\t\n\n\n \n\n4、年龄大\n\n随着狗狗的年龄不断增长，狗狗的神经系统的反应能力也会降低，这时狗狗对于自己尿液的控制能力也会下降，所以就会出现憋不住尿的情况。这种情况几乎没有很好的解决办法，可以尝试性进行针灸或激光理疗，但效果难以保证。\n\
+    \n \n\n\t\n\n\n5、患有泌尿系统疾病\n\n如果狗狗除了出现憋不住尿的问题之外，还伴有尿频、尿痛、尿淋漓等症状，那就很可能是患有泌尿系统疾病，比如尿道炎、膀胱炎、尿结石等等。由于结石和炎症长时间刺激泌尿系统相关区域的粘膜，进而导致狗狗无法长时间憋尿，出现尿频的情况。这种问题十分严重，建议主人一旦发现就及时带狗狗到宠物医院治疗。 #新手养狗[话题]#  #养狗经验分享[话题]#  #养狗攻略[话题]# "
+  - '离得肝病的人远点，别被传染？
+
+    离得肝病的人远点，别被传染？
+
+
+    我们要知道并不是所有的疾病都具有传染性，肝病也是这样。肝病有很多种，常见的有甲肝、乙肝、丙肝这种病毒感染导致的病毒性肝炎，也有脂肪肝酒精肝这种不良生活习惯导致的肝病，还有一些自身免疫性的肝病等等。但是具有传染性的就只有病毒性肝炎，其他的是不具有传染性的。现在传染性最强也最广泛的就是乙肝，目前国内80%肝病患者都是因为感染了乙肝病毒，现阶段医疗水平还没有达到能完全消灭乙肝的水平，所以乙肝病毒的攻克依然是一个严峻的问题。对于肝病患者来说我们要注意防护，注意日常生活饮食，尽可能地让我们的身体处于一个较好的水平，等待新技术，新药的应用。对于健康人群，我们要做好日常的防护，碗筷消毒，不共用毛巾，水杯等生活用品，我们也不要去歧视和贬低肝病患者，己所不欲勿施于人！ #山东[话题]#  #肝病[话题]#  #健康科普[话题]# 
+
+
+    '
+  - "快抄作业✍️结婚必拍爆\U0001F525的喜嫁风婚纱照\n喜嫁婚纱照|新中式婚纱照|婚纱照风格\n\U0001F4F8拍摄：@玛萨婚纱照 \n-\n小红书热拍\U0001F525\
+    喜嫁婚纱照合集\n各种类型的喜嫁婚纱照\n不挑人 i人e人轻松驾驭\U0001F90F\n东方新娘结婚必拍\U0001F4E3\n-\n主纱 新中式 复古\
+    \ 俏皮都有的喜嫁婚纱照\n\U0001F514满足各种风格的新人们\n✅建议备婚的收藏哦~\n-\n25年的新娘们备婚不走弯路\U0001F92B\U0001F92B\
+    \n必选好看不踩雷的喜嫁婚纱照\n百搭且特别实用\U0001F44B\n-\n更多类型婚纱照戳主页哦‼️\n咨询拍摄\U0001F251戳@玛萨婚纱照 \n\
+    抢先获取 >>惊喜优惠[红包R]\n[打卡R]了解套餐/预约进店 评论\U0001F522\n-\n#芜湖婚纱照[话题]#｜#芜湖婚纱照推荐[话题]#｜#婚纱摄影[话题]#\n\
+    #婚纱照风格[话题]#｜#芜湖婚纱摄影[话题]#｜#喜嫁婚纱照[话题]#\n#新中式婚纱照[话题]#｜#汉服婚纱照[话题]#｜#复古婚纱照[话题]#"
+- source_sentence: 生日快乐文案
+  sentences:
+  - '高级小众的生日祝福
+
+    🎈🎈
+
+    01
+
+    生日快乐
+
+    长岁常安
+
+    ·
+
+    02
+
+    一岁一礼
+
+    一寸欢喜
+
+    ·
+
+    03
+
+    这一岁
+
+    要找到生活中最温柔的光
+
+    ·
+
+    04
+
+    生活明朗一路追光
+
+    可爱一如往常
+
+    ·
+
+    05
+
+    朝暮与年岁并往
+
+    希望热爱与平淡共存
+
+    ·
+
+    06
+
+    生而自由，日日欢愉
+
+    快意人生，乐得自在
+
+    ·
+
+    07
+
+    生日快乐
+
+    前路浩浩荡荡
+
+    万事尽可期待
+
+    ·
+
+    08
+
+    祝你一生明朗，无惧岁月冗长
+
+    前���的路上，温柔闪光
+
+    ·
+
+    09
+
+    何其有幸，年岁并进
+
+    前路浩浩荡荡，万事尽可期待
+
+    ·
+
+    10
+
+    渐入佳境
+
+    大概是我对人生最好的祝愿了
+
+    生日快乐
+
+    ·
+
+    11
+
+    祝你常拾理想，终得浪漫
+
+    在朝朝暮暮里岁岁长安
+
+    ·
+
+    12
+
+    年年岁岁，承蒙时光不弃
+
+    岁岁年年，万喜万般皆宜
+
+    ·
+
+    13
+
+    祝你温柔浪漫，不失爱与果敢
+
+    朝朝如愿，岁岁如意平安
+
+    ·
+
+    14
+
+    希望你继续兴致盎然地与世界交手
+
+    一直走在充满鲜花的道路上
+
+    ·
+
+    15
+
+    新的一岁，愿生活：
+
+    风平浪静，柳暗花明
+
+    三分惊喜，七分尽兴
+
+    ·
+
+    16
+
+    祝你早日找到小宇宙
+
+    在自己的小天地做喜欢的事
+
+    生日快乐
+
+    ·
+
+    ✨✨
+
+    —The end—
+
+    🌈感谢阅读
+
+    #生日快乐[话题]# #生日文案[话题]# #文字素材分享[话题]# #生日[话题]# #文字语录[话题]# #文案[话题]# #速写人间万象[话题]# #浪漫生活的记录者[话题]#
+    #生日祝福[话题]# '
+  - 'nan
+
+    王安宇：撞我枪口上了，我粤语非常好[笑哭R][笑哭R][笑哭R]咋会这么搞笑#王安宇[话题]# #正在追的综艺[话题]# #现在就出发2[话题]# #非原创[话题]#
+    #沈腾[话题]# #胡先煦[话题]# #金晨[话题]# #宋亚轩[话题]# '
+  - '库迪星尘厚乳拿铁里的晶球是酸的吗？
+
+    太久没喝了，忘记原来啥味道了[捂脸R]
+
+    这杯感觉酸酸的，里面的晶球好酸，不会坏了吧，
+
+    有没有经常喝的人知道哇[皱眉R]
+
+    还是说，酸是正常的？#库迪[话题]# #库迪咖啡[话题]# #星尘厚乳拿铁[话题]# #咖啡日常[话题]# '
+- source_sentence: 莞城美食平价
+  sentences:
+  - "家人们！来评论区许愿！\n活动规则：点赞+评论本条视频的高考毕业生，从高中→大学的第一幅美瞳我来送[飞吻R]\n抽奖产品：店内美瞳任选4个花色[萌萌哒R]\n\
+    活动时间：2024年6月8日-2024年6月10日\n\t\n\n家人们许愿的时候\n不要忘记关注oon\n助力小o完成涨粉目标[哇R]\n#高考[话题]#\
+    \ #oon美瞳[话题]# #人生第一幅美瞳[话题]# #抽奖活动[话题]# #半年抛美瞳[话题]# "
+  - '🔥东莞莞城美食地图大公开🗺️
+
+    [派对R]友友们，我带着莞城逛吃版攻略来啦！准备好跟我一起开启美食之旅了吗？[哇R]
+
+    ✅第一站: 猪宝饼家🍞
+
+    这里的鲜奶蛋挞和菠萝包🍞是绝对不能错过的。蛋挞的奶味浓郁，让人回味无穷😍。这次尝试的金枪鱼面包则是咸口面包，但是没有特别突出。要记得早点去，不然像菠萝包这样的热门产品可能会售罄哦。
+
+    ✅第二站: 街边茶档🧋
+
+    位于猪宝饼家隔壁的这家街边茶档，虽然店面不起眼，但却是隐藏版的奶茶天堂。这里的奶茶种类虽然不多，但每一款都能与香港正宗茶餐厅的饮料相媲美。特别是港奶和冻柠茶，味道浓郁，让人一喝难忘。别忘了欣赏一下帅气的老板🤩哦！
+
+    ✅第三站: 芹菜糖糕点🍮
+
+    虽然这家店位于市场的小巷子里，但人气却一点也不低。开导航就能轻松找到。这里的糕点都是糯叽叽的，特别推荐油煎蛋黄卷和糯米糍。蛋黄卷煎得热热的，配上香甜的内馅，简直美味极了😋！而糯米糍则有丰富的口味选择，豆沙和奶黄馅都很赞，甜度适中☺️。
+
+    ✅第四站: 炸蛋小超人🍢
+
+    这家炸嘢店在莞城可是家喻户晓的。人气旺盛，总是需要排队等候。裹粉鸡翅汁水丰富，炸金针菇酥脆可口。如果犹豫不决的宝宝们，按照菜单上的推荐点准没错。虽然等待时间可能会有些长😭，但美食总是值得等待的。
+
+    ✅第五站: 超级棒🍧
+
+    来到莞城怎么能错过这家刨冰🍧店呢？童年回忆的刨冰店！芒果🥭红豆牛奶冰是最受推荐的选择。炼乳浓郁，芒果丰富多汁，红豆也是满满的。芒果🥭与红豆的搭配简直是绝配！强烈安利这款刨冰🍧给你！
+
+    最后附上我的宝藏打卡店铺地图🗺️！这次的莞城逛吃版City Walk之旅真的太有feel了！
+
+    💕喜欢的宝宝们快别错过了这样的莞城美食之旅！
+
+    #东莞美食[话题]# #东莞旅游美食[话题]# #莞城美食[话题]# #美食日常[话题]# #东莞探店[话题]# #莞城探店[话题]# #莞城[话题]# #东莞[话题]#
+    #东莞莞城美食[话题]# #东莞莞城[话题]# #东莞美食攻略[话题]# #东莞美食探店[话题]# '
+  - "119\U0001F19A219｜两款lena直板夹区别在哪\U0001F914\n\U0001F4B0119 经典款：小\U0001F360爆款直板夹\n\
+    性价比之选 各方面无短板\n-\n\U0001F4B0219 无感款：高级有质感 博主爱用款\n使用频率高 预算充足可选\n\t\n#lena[话题]# #直板夹[话题]#\
+    \ #lena直板夹[话题]# #直板夹测评[话题]# #直板夹推荐[话题]# #直板夹卷发[话题]# #极限二选一[话题]# #选哪个[话题]# #夹板[话题]#\
+    \ #1年1度购物狂欢[话题]#"
+- source_sentence: 宣传工作个人述职
+  sentences:
+  - "原来超赞的述职报告这么好写\U0001F525\n述职报告写不好？不会写？\n看这一篇就对了！\n戳[向右R][向右R][向右R]【立刻咨询】匹配1v1写作老师\n\
+    \t\n那么述职报告到底如何写好呢?\n[一R]简明扼要，提高报告的可读性和说服力\n[二R]突出重点，对履行职责深入分析及研究\n[三R]全面报告，为后续内容做好铺垫\n\
+    [四R]客观分析，概述任职期间主要成绩\n[五R]解决方案，展现个人解决问题的能力\n\t\n如果还担心写不好，来不及[可怜R]\n也可以戳[向右R][向右R][向右R]【立刻咨询】解锁立刻1v1写作老师！\n\
+    \t\n[星R]专业团队，经验丰富、效率高\n[星R]一对一贴心指导，手把手教会你如何文章优化\n[星R]24小时全天在线，随时解答你的问题难点\n[星R]收费公道透明，性价比高\n\
+    \t\n还在为文章没方向，内容写不出来，时间紧任务重而担心的宝子们~\n点击   [向右R][向右R][向右R]【立刻咨询】即刻匹配专业老师为你解决文章难题！！！\n\
+    \t\n#述职报告[话题]# #述职报告工作总结[话题]# #述职报告ppt[话题]# #年终述职[话题]# #转正述职报告[话题]# #晋升述职[话题]#\
+    \ #银行副职述职竞聘报告[话题]# "
+  - '人生第一条鱼王居然是臭水沟钓的？
+
+    我说这把钥匙是干嘛的原来是开下水道的，而且下水道通往城镇哎，实现地图闪现哈哈哈哈，我万万没想到第一条鱼王是在下水道的臭水沟里钓到的哈哈哈哈收藏起来，没想到还挺好钓的，为什么其他的鱼王这么难钓阿，下水道那还可以拿💰换职业，果然💰是万能的哇！
+
+    还有这个小黑好可爱，好想带回家养着！[萌萌哒R]
+
+    又嫖到了一个星之果实🌟#星露谷[话题]# #星露谷新手[话题]# #星露谷下水道[话题]# '
+  - '咔乐加动感猫眼胶好灰啊
+
+    个人玩家测评。今天快递到了赶忙去拿随手涂了一下，动感系列的都会发灰，没涂打底色直接涂的胶周圈一边黑色可不好看，单看猫眼颜色挺好看的#猫眼[话题]# #咔乐加猫眼胶[话题]#
+    #平价猫眼胶[话题]##美甲[话题]##原相机[话题]#'
+- source_sentence: 上学文具分享
+  sentences:
+  - '云南丽江～ 束河古镇风景（上）
+
+    拍于2023年11.19哦～
+
+    从白沙坐公交去的束河古镇
+
+    进门的时候忘记拍牌楼了[笑哭R]
+
+    刚进门不久走到了茶马古道博物馆但是是关闭状态的
+
+    不走人多的地方风景很不错
+
+    图上那只松鼠的表情真的笑死
+
+    看起来比香格里拉和雨崩的可爱
+
+    建议沿着水流走 风景不会太差
+
+    #云南[话题]# #云南游[话题]# #云南丽江[话题]# #束河古镇[话题]# '
+  - "准初三生的书包里有啥\U0001F609\U0001F449\U0001F3FB\U0001F497\n都是一些很真实的东西哈哈哈 \n我就问有谁懂…？\n\
+    #笔袋[话题]# #笔袋介绍[话题]# #我的文具分享[话题]# \n#晒晒我的书桌[话题]# #我的日常[话题]# \n#whatsinmybag[话题]#\
+    \ #书包里面装什么[话题]# \n#书包[话题]# "
+  - '下辈子我也要当仓鼠
+
+    傻傻的胖胖的不知道悲伤……#珍藏的宠物照[话题]# #侏儒仓鼠[话题]# #鼠鼠教[话题]# #宠物[话题]# #宠物日常[话题]# '
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_ndcg@100
+- cosine_mrr@10
+- cosine_mrr@100
+- cosine_map@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer based on IEITYuan/Yuan-embedding-2.0-zh
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: query note test
+      type: query_note_test
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.23479758828596037
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.46718346253229975
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5714039621016366
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.6919896640826874
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.23479758828596037
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.20160780935974734
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1750904392764858
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.1343152454780362
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.089571995809469
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.21276711060827266
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.290847859967523
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.4043580098850914
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.31369612431129706
+      name: Cosine Ndcg@10
+    - type: cosine_ndcg@100
+      value: 0.4213142361215435
+      name: Cosine Ndcg@100
+    - type: cosine_mrr@10
+      value: 0.3758652229194859
+      name: Cosine Mrr@10
+    - type: cosine_mrr@100
+      value: 0.38581415116789297
+      name: Cosine Mrr@100
+    - type: cosine_map@10
+      value: 0.2185657312084243
+      name: Cosine Map@10
+    - type: cosine_map@100
+      value: 0.254050567242055
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: query note test 256
+      type: query_note_test_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.22997416020671835
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.446167097329888
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5488372093023256
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.6690783807062877
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.22997416020671835
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.19092736146999711
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16675279931093884
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.12809646856158485
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.08578555881472219
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.20298188200672027
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.27491914047529314
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.38317051407751485
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.2987742813298307
+      name: Cosine Ndcg@10
+    - type: cosine_ndcg@100
+      value: 0.4019666587503052
+      name: Cosine Ndcg@100
+    - type: cosine_mrr@10
+      value: 0.36383679914687544
+      name: Cosine Mrr@10
+    - type: cosine_mrr@100
+      value: 0.37401737089804155
+      name: Cosine Mrr@100
+    - type: cosine_map@10
+      value: 0.20713694797471097
+      name: Cosine Map@10
+    - type: cosine_map@100
+      value: 0.23981106920718018
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: query note val
+      type: query_note_val
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.2973365617433414
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5941888619854722
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7297820823244552
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.85181598062954
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.2973365617433414
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2687651331719128
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.235544794188862
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.178498789346247
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.12551538574264365
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.32960528205430656
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.45124208285458633
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.6155340966297885
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.45092052083837153
+      name: Cosine Ndcg@10
+    - type: cosine_ndcg@100
+      value: 0.5626402106691932
+      name: Cosine Ndcg@100
+    - type: cosine_mrr@10
+      value: 0.47398516468734525
+      name: Cosine Mrr@10
+    - type: cosine_mrr@100
+      value: 0.4808673110081914
+      name: Cosine Mrr@100
+    - type: cosine_map@10
+      value: 0.33275259014017233
+      name: Cosine Map@10
+    - type: cosine_map@100
+      value: 0.3795452213509927
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: query note val 256
+      type: query_note_val_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.2963680387409201
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.5932203389830508
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7220338983050848
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.847457627118644
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.2963680387409201
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.26682808716707024
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.233317191283293
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.1764164648910412
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.12586104987005262
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.32797772143937326
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.4473090263384894
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.6070124305129267
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.44746749042985406
+      name: Cosine Ndcg@10
+    - type: cosine_ndcg@100
+      value: 0.5585225512719426
+      name: Cosine Ndcg@100
+    - type: cosine_mrr@10
+      value: 0.47298935393366487
+      name: Cosine Mrr@10
+    - type: cosine_mrr@100
+      value: 0.47991217157767396
+      name: Cosine Mrr@100
+    - type: cosine_map@10
+      value: 0.33037623162438684
+      name: Cosine Map@10
+    - type: cosine_map@100
+      value: 0.3769025263387124
+      name: Cosine Map@100
+---
+
+# SentenceTransformer
+
+This model was finetuned with [Unsloth](https://github.com/unslothai/unsloth).
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+ based on IEITYuan/Yuan-embedding-2.0-zh
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [IEITYuan/Yuan-embedding-2.0-zh](https://huggingface.co/IEITYuan/Yuan-embedding-2.0-zh). It maps sentences & paragraphs to a 1792-dimensional dense vector space and can be used for retrieval.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [IEITYuan/Yuan-embedding-2.0-zh](https://huggingface.co/IEITYuan/Yuan-embedding-2.0-zh) <!-- at revision fb4ab1ed9d3447b64c79e305c8913340327668b5 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1792 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Supported Modality:** Text
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
+  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', 'include_prompt': True})
+  (2): Dense({'in_features': 1024, 'out_features': 1792, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+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("sentence_transformers_model_id")
+# Run inference
+queries = [
+    '上学文具分享',
+]
+documents = [
+    '准初三生的书包里有啥😉👉🏻💗\n都是一些很真实的东西哈哈哈 \n我就问有谁懂…？\n#笔袋[话题]# #笔袋介绍[话题]# #我的文具分享[话题]# \n#晒晒我的书桌[话题]# #我的日常[话题]# \n#whatsinmybag[话题]# #书包里面装什么[话题]# \n#书包[话题]# ',
+    '下辈子我也要当仓鼠\n傻傻的胖胖的不知道悲伤……#珍藏的宠物照[话题]# #侏儒仓鼠[话题]# #鼠鼠教[话题]# #宠物[话题]# #宠物日常[话题]# ',
+    '云南丽江～ 束河古镇风景（上）\n拍于2023年11.19哦～\n从白沙坐公交去的束河古镇\n进门的时候忘记拍牌楼了[笑哭R]\n刚进门不久走到了茶马古道博物馆但是是关闭状态的\n不走人多的地方风景很不错\n图上那只松鼠的表情真的笑死\n看起来比香格里拉和雨崩的可爱\n建议沿着水流走 风景不会太差\n#云南[话题]# #云南游[话题]# #云南丽江[话题]# #束河古镇[话题]# ',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 1792] [3, 1792]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[ 0.6349,  0.0464, -0.0339]])
+```
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `query_note_test` and `query_note_val`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | query_note_test | query_note_val |
+|:--------------------|:----------------|:---------------|
+| cosine_accuracy@1   | 0.2348          | 0.2973         |
+| cosine_accuracy@3   | 0.4672          | 0.5942         |
+| cosine_accuracy@5   | 0.5714          | 0.7298         |
+| cosine_accuracy@10  | 0.692           | 0.8518         |
+| cosine_precision@1  | 0.2348          | 0.2973         |
+| cosine_precision@3  | 0.2016          | 0.2688         |
+| cosine_precision@5  | 0.1751          | 0.2355         |
+| cosine_precision@10 | 0.1343          | 0.1785         |
+| cosine_recall@1     | 0.0896          | 0.1255         |
+| cosine_recall@3     | 0.2128          | 0.3296         |
+| cosine_recall@5     | 0.2908          | 0.4512         |
+| cosine_recall@10    | 0.4044          | 0.6155         |
+| cosine_ndcg@10      | 0.3137          | 0.4509         |
+| **cosine_ndcg@100** | **0.4213**      | **0.5626**     |
+| cosine_mrr@10       | 0.3759          | 0.474          |
+| cosine_mrr@100      | 0.3858          | 0.4809         |
+| cosine_map@10       | 0.2186          | 0.3328         |
+| cosine_map@100      | 0.2541          | 0.3795         |
+
+#### Information Retrieval
+
+* Datasets: `query_note_test_256` and `query_note_val_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "truncate_dim": 256
+  }
+  ```
+
+| Metric              | query_note_test_256 | query_note_val_256 |
+|:--------------------|:--------------------|:-------------------|
+| cosine_accuracy@1   | 0.23                | 0.2964             |
+| cosine_accuracy@3   | 0.4462              | 0.5932             |
+| cosine_accuracy@5   | 0.5488              | 0.722              |
+| cosine_accuracy@10  | 0.6691              | 0.8475             |
+| cosine_precision@1  | 0.23                | 0.2964             |
+| cosine_precision@3  | 0.1909              | 0.2668             |
+| cosine_precision@5  | 0.1668              | 0.2333             |
+| cosine_precision@10 | 0.1281              | 0.1764             |
+| cosine_recall@1     | 0.0858              | 0.1259             |
+| cosine_recall@3     | 0.203               | 0.328              |
+| cosine_recall@5     | 0.2749              | 0.4473             |
+| cosine_recall@10    | 0.3832              | 0.607              |
+| cosine_ndcg@10      | 0.2988              | 0.4475             |
+| **cosine_ndcg@100** | **0.402**           | **0.5585**         |
+| cosine_mrr@10       | 0.3638              | 0.473              |
+| cosine_mrr@100      | 0.374               | 0.4799             |
+| cosine_map@10       | 0.2071              | 0.3304             |
+| cosine_map@100      | 0.2398              | 0.3769             |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 164,633 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 100 samples:
+  |          | anchor                                                                           | positive                                                                             |
+  |:---------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type     | string                                                                           | string                                                                               |
+  | modality | text                                                                             | text                                                                                 |
+  | details  | <ul><li>min: 3 tokens</li><li>mean: 8.78 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: 38 tokens</li><li>mean: 308.37 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | anchor                   | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+  |:-------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>jeep冲锋衣属于档次</code> | <code>🧥吉普冲锋衣，秋季必备🍂<br>Jeep吉普官方正品，品质有保障👍。	<br>	<br>卡其色/M码，适合90-110斤，款式时尚百搭。	<br>	<br>防风防水设计，适合户外活动，保暖又舒适😊。	<br>	<br>秋季穿搭必备，你值得拥有！ #小红书秋日焕新[话题]# #jeep[话题]# #冲锋衣[话题]# #秋季外套[话题]# #正品保证[话题]# #防风防水[话题]# #户外穿搭[话题]# #时尚百搭[话题]# #品质选择[话题]#</code>                                                                                                                                                                                                                                                                                                                                                                                                                         |
+  | <code>喝酒文案微醺</code>      | <code>秋冬时刻需要威士忌 \| 见面吗？！<br>🍽️<br>属于我和先生的浪漫约会日常<br>便是放下孩子后的小酌怡情~<br> 刚好最近气温骤降，多了些凉意<br>想来一杯Whisky的心情达到上限<br> 🍽️<br>听闻格兰菲迪最近和MU·慕有出合作套餐<br>与先生的couple vacation时刻瞬间启动~<br>在轻奢环境下更适合开一瓶格兰菲迪22年单一麦芽苏格兰威士忌<br>@格兰菲迪 Glenfiddich <br>作为在全球屡获殊荣的单一麦芽苏格兰威士忌品牌<br>自诞生起就从未停止探索的脚步<br>不断探究威士忌与餐食搭配的新风味<br> 🍽️<br>当杯中深红琥珀色的酒体<br>在灯光下衬得更加迷人时<br>黑巧克力以及葡萄果干的浓郁香气也同时散发<br>象拔蚌和波士顿龙虾的上场<br>将视觉的色与口感的香瞬间突出<br>薄如刀片的爽脆蚌肉和Q弹龙虾肉<br>在酒精的碰撞下太立体太美好<br> <br>余韵的温润柑橘果香和馥郁水果蛋糕香<br>像是激情生活之后的源远流长<br>搭配新西兰羊排嫩多汁的奶香气息<br>像是被酒精注入新的灵感和层次<br> 🤎<br>婚后的微醺节奏<br>同样能拉近彼此的距离<br>大家也可以打卡格兰菲迪合作餐厅哦<br>用一杯格兰菲迪杯找寻浪漫~找寻生活吧<br>#广州探店[话题]# #福鹿结伴山海寻鲜[话题]# #威士忌推荐[话题]# #格兰菲迪[话题]# #格兰菲迪单一麦芽威士忌[话题]#</code> |
+  | <code>长沙酒吧</code>        | <code>长沙一定要去的6️⃣大夜店！！！<br>长沙夜生活 蹦迪💃酒吧 再多的攻略都不及去亲身体验一次！！<br>	<br>✅长沙一定要去的夜店❗️❗️<br>1️⃣X-sta：长沙最火爆的高空edm场 声光电体验感强 非常具有科技感 总投资1.8亿[赞R] 四五六楼是包厢 娱乐综合体<br>2️⃣one9：小厅bounce场 韩国江南风夜店 前身是火爆长沙的NBEK 喜欢小厅的朋友不要错过啦[派对R]<br>3️⃣FTF：新开业中型edm/bounce酒吧 位置在海信广场 以前的超级猴子[派对R]<br>4️⃣kok：网红爆品小厅 每晚都有男团演出与玩家们互动 人气也一直居高不下[自拍R]<br>5️⃣猩猩地堡：连锁品牌 打卡的人很多 长沙年轻人的聚集地<br>6️⃣试音：长沙hiphop吧天花板 嘉宾众多气氛火爆 值得打卡<br>	<br>🌿住宿推荐<br>建议住五一广场/开���区万达广场（去哪都方便）<br>	<br>⚠️温馨提示：想去长沙夜店玩的朋友记得提前做好攻略，提前找销售预约好门店避免排队！<br>	<br>希望这些对您有所帮助！<br>图片来源于网络 侵删！<br>#长沙酒吧[话题]# #长沙酒吧推荐[话题]# #长沙酒吧订台[话题]# #长沙旅游[话题]# #长沙蹦迪[话题]#</code>                                                                                 |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          1024,
+          256
+      ],
+      "matryoshka_weights": [
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 256
+- `learning_rate`: 0.0001
+- `weight_decay`: 0.01
+- `num_train_epochs`: 1
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.05
+- `seed`: 3407
+- `bf16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 0.0001
+- `weight_decay`: 0.01
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.05
+- `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`: 3407
+- `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`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `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}
+- `parallelism_config`: 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
+- `hub_revision`: None
+- `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
+- `liger_kernel_config`: None
+- `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`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | query_note_test_cosine_ndcg@100 | query_note_test_256_cosine_ndcg@100 | query_note_val_cosine_ndcg@100 | query_note_val_256_cosine_ndcg@100 |
+|:------:|:----:|:-------------:|:-------------------------------:|:-----------------------------------:|:------------------------------:|:----------------------------------:|
+| -1     | -1   | -             | 0.4213                          | 0.4020                              | -                              | -                                  |
+| 0.0155 | 10   | 3.6018        | -                               | -                                   | -                              | -                                  |
+| 0.0311 | 20   | 2.5033        | -                               | -                                   | -                              | -                                  |
+| 0.0466 | 30   | 1.1976        | -                               | -                                   | -                              | -                                  |
+| 0.0621 | 40   | 1.0096        | -                               | -                                   | -                              | -                                  |
+| 0.0776 | 50   | 0.8353        | -                               | -                                   | -                              | -                                  |
+| 0.0932 | 60   | 0.8335        | -                               | -                                   | -                              | -                                  |
+| 0.1087 | 70   | 0.8197        | -                               | -                                   | -                              | -                                  |
+| 0.1242 | 80   | 0.7655        | -                               | -                                   | -                              | -                                  |
+| 0.1398 | 90   | 0.7013        | -                               | -                                   | -                              | -                                  |
+| 0.1553 | 100  | 0.7359        | -                               | -                                   | -                              | -                                  |
+| 0.1708 | 110  | 0.7523        | -                               | -                                   | -                              | -                                  |
+| 0.1863 | 120  | 0.7146        | -                               | -                                   | -                              | -                                  |
+| 0.2019 | 130  | 0.7609        | -                               | -                                   | -                              | -                                  |
+| 0.2174 | 140  | 0.751         | -                               | -                                   | -                              | -                                  |
+| 0.2329 | 150  | 0.6394        | -                               | -                                   | -                              | -                                  |
+| 0.2484 | 160  | 0.7417        | -                               | -                                   | -                              | -                                  |
+| 0.2640 | 170  | 0.7253        | -                               | -                                   | -                              | -                                  |
+| 0.2795 | 180  | 0.7553        | -                               | -                                   | -                              | -                                  |
+| 0.2950 | 190  | 0.681         | -                               | -                                   | -                              | -                                  |
+| 0.3106 | 200  | 0.6562        | -                               | -                                   | -                              | -                                  |
+| 0.3261 | 210  | 0.5938        | -                               | -                                   | -                              | -                                  |
+| 0.3416 | 220  | 0.6074        | -                               | -                                   | -                              | -                                  |
+| 0.3571 | 230  | 0.6752        | -                               | -                                   | -                              | -                                  |
+| 0.3727 | 240  | 0.6051        | -                               | -                                   | -                              | -                                  |
+| 0.3882 | 250  | 0.7016        | -                               | -                                   | -                              | -                                  |
+| 0.4037 | 260  | 0.6432        | -                               | -                                   | -                              | -                                  |
+| 0.4193 | 270  | 0.656         | -                               | -                                   | -                              | -                                  |
+| 0.4348 | 280  | 0.5781        | -                               | -                                   | -                              | -                                  |
+| 0.4503 | 290  | 0.5972        | -                               | -                                   | -                              | -                                  |
+| 0.4658 | 300  | 0.6699        | -                               | -                                   | -                              | -                                  |
+| 0.4814 | 310  | 0.6081        | -                               | -                                   | -                              | -                                  |
+| 0.4969 | 320  | 0.6143        | -                               | -                                   | -                              | -                                  |
+| 0.5124 | 330  | 0.657         | -                               | -                                   | -                              | -                                  |
+| 0.5280 | 340  | 0.6711        | -                               | -                                   | -                              | -                                  |
+| 0.5435 | 350  | 0.6589        | -                               | -                                   | -                              | -                                  |
+| 0.5590 | 360  | 0.6388        | -                               | -                                   | -                              | -                                  |
+| 0.5745 | 370  | 0.5856        | -                               | -                                   | -                              | -                                  |
+| 0.5901 | 380  | 0.6781        | -                               | -                                   | -                              | -                                  |
+| 0.6056 | 390  | 0.6044        | -                               | -                                   | -                              | -                                  |
+| 0.6211 | 400  | 0.5893        | -                               | -                                   | -                              | -                                  |
+| 0.6366 | 410  | 0.6496        | -                               | -                                   | -                              | -                                  |
+| 0.6522 | 420  | 0.6423        | -                               | -                                   | -                              | -                                  |
+| 0.6677 | 430  | 0.6304        | -                               | -                                   | -                              | -                                  |
+| 0.6832 | 440  | 0.6052        | -                               | -                                   | -                              | -                                  |
+| 0.6988 | 450  | 0.6767        | -                               | -                                   | -                              | -                                  |
+| 0.7143 | 460  | 0.6036        | -                               | -                                   | -                              | -                                  |
+| 0.7298 | 470  | 0.6324        | -                               | -                                   | -                              | -                                  |
+| 0.7453 | 480  | 0.6036        | -                               | -                                   | -                              | -                                  |
+| 0.7609 | 490  | 0.5681        | -                               | -                                   | -                              | -                                  |
+| 0.7764 | 500  | 0.5643        | -                               | -                                   | -                              | -                                  |
+| 0.7919 | 510  | 0.6016        | -                               | -                                   | -                              | -                                  |
+| 0.8075 | 520  | 0.6362        | -                               | -                                   | -                              | -                                  |
+| 0.8230 | 530  | 0.601         | -                               | -                                   | -                              | -                                  |
+| 0.8385 | 540  | 0.5468        | -                               | -                                   | -                              | -                                  |
+| 0.8540 | 550  | 0.5602        | -                               | -                                   | -                              | -                                  |
+| 0.8696 | 560  | 0.6183        | -                               | -                                   | -                              | -                                  |
+| 0.8851 | 570  | 0.5713        | -                               | -                                   | -                              | -                                  |
+| 0.9006 | 580  | 0.59          | -                               | -                                   | -                              | -                                  |
+| 0.9161 | 590  | 0.5639        | -                               | -                                   | -                              | -                                  |
+| 0.9317 | 600  | 0.5733        | -                               | -                                   | -                              | -                                  |
+| 0.9472 | 610  | 0.5941        | -                               | -                                   | -                              | -                                  |
+| 0.9627 | 620  | 0.6197        | -                               | -                                   | -                              | -                                  |
+| 0.9783 | 630  | 0.6259        | -                               | -                                   | -                              | -                                  |
+| 0.9938 | 640  | 0.5718        | -                               | -                                   | -                              | -                                  |
+| 1.0    | 644  | -             | -                               | -                                   | 0.5626                         | 0.5585                             |
+
+
+### Training Time
+- **Training**: 48.8 minutes
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 5.5.1
+- Transformers: 4.56.2
+- PyTorch: 2.11.0+cu128
+- Accelerate: 1.13.0
+- Datasets: 4.3.0
+- Tokenizers: 0.22.2
+
+## 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{oord2019representationlearningcontrastivepredictive,
+      title={Representation Learning with Contrastive Predictive Coding},
+      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
+      year={2019},
+      eprint={1807.03748},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1807.03748},
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "dtype": "bfloat16",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.56.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "pytorch": "2.11.0+cu128",
+    "sentence_transformers": "5.5.1",
+    "transformers": "4.56.2"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5df8283c0c2b7e8b25ae25445f315ba6086b8f35e74d67dfdd4779fbcef0d986
+size 679572832

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.base.modules.dense.Dense"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,10 @@
+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}