{"m1":[],"m2":[],"m3":["resume_head","resume_name","resume_base_info","resume_job","resume_edu","resume_work","resume_hobby","resume_skill","resume_summary","resume_honor","resume_internship","resume_project","resume_portfolio","7247a352-d7e5-46d4-9761-c70503d05d8e","3f3535c6-bfe5-468e-b7d1-688225190892"],"m4":[]}
.resume_main[data_color] .skill_item .skill_slider span::before{background-color:${color};}
.resume_main[data_color] .skill_slider s i{background-color:${relative_skill_color};}
.resume_main[data_color] .skill_style_01.skill_item .skill_slider s {border-color:${relative_skill_color};}
.resume_main[data_color] .skill_style_01.skill_item .skill_slider s i{background-color:${relative_skill_color};}
.resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="average"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="average"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 #ccc, 72px 0 0 #ccc, 96px 0 0 #ccc, 120px 0 0 #ccc;}
.resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="good"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="good"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 #ccc, 96px 0 0 #ccc, 120px 0 0 #ccc;}
.resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="advanced"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="advanced"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 ${relative_skill_color}, 96px 0 0 #ccc, 120px 0 0 #ccc;}
.resume_main[data_color] .skill_style_04.skill_item .skill_slider[data_level="expert"] i,.resume_main[data_color] .skill_style_07.skill_item .skill_slider[data_level="expert"] i{box-shadow:24px 0 0 ${relative_skill_color}, 48px 0 0 ${relative_skill_color}, 72px 0 0 ${relative_skill_color}, 96px 0 0 ${relative_skill_color}, 120px 0 0 #ccc;}
.resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="average"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 #ccc,63px 0 0 #ccc,72px 0 0 #ccc,81px 0 0 #ccc;}
.resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="good"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 #ccc,72px 0 0 #ccc,81px 0 0 #ccc;}
.resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="advanced"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 ${relative_skill_color},72px 0 0 #ccc,81px 0 0 #ccc;}
.resume_main[data_color] .skill_style_08.skill_item .skill_slider[data_level="expert"] i{box-shadow:9px 0 0 ${relative_skill_color}, 18px 0 0 ${relative_skill_color}, 27px 0 0 ${relative_skill_color}, 36px 0 0 ${relative_skill_color}, 45px 0 0 ${relative_skill_color},54px 0 0 ${relative_skill_color},63px 0 0 ${relative_skill_color},72px 0 0 ${relative_skill_color},81px 0 0 #ccc;}
.resume_main[data_color] .hobby_item .hobby_item_con .hobby_item_list a.alifont{border-color:${relative_hobby_color};color:${relative_hobby_color}; }
/* ?????? */
.resume_main[data_color] .resume_cover .cover_html svg [data-svg="fill"] {fill:${color};}
.resume_main[data_color] .resume_cover .cover_html svg [data-svg="stroke"] {stroke:${color};}
.resume_main[data_color] .resume_letter .letter_html svg [data-svg="fill"] {fill:${color};}
.resume_main[data_color] .resume_letter .letter_html svg [data-svg="stroke"] {stroke:${color};}
.resume_main[data_color] .resume_letter .letter_html svg [data-fill="fill"] {fill:${color};}
.resume_main[data_color] .resume_cover[data-type="07"] .resume_cover_avatar{border-color: ${color};}
.resume_main[data_color] .resume_cover[data-type="07"] .resume_cover_content{background:${color}}
.resume_main[data_color] .resume_cover[data-type="07"] .cover_item_list a.alifont{color: ${color};}
.resume_main[data_color] .resume_cover[data-type="08"] .resume_cover_content::after{background:${color}}
.resume_main[data_color] .resume_cover[data-type="09"] .resume_cover_content{background:${color}}
.resume_main[data_color] .resume_cover[data-type="09"] .cover_item_list a.alifont{color: ${color};}
.resume_main[data_color] .resume_cover[data-type="10"]{background-color:${color}}
.resume_main[data_color] .resume_cover[data-type="11"] .resume_cover_content{background-color:${color}}
.resume_main[data_color] .resume_cover[data-type="14"]{background-color:${color}}
.resume_main[data_color] .resume_cover[data-type="15"]{background-color:${color}}
.resume_main[data_color] .resume_cover[data-type="19"] .resume_cover_word::before{background-color:${color}}
.resume_main[data_color] .resume_cover[data-type="20"]{background-color:${color}}
.resume_main[data_color] .resume_letter[data-type="06"]{background-color:${color}}
.resume_main[data_color] .resume_letter[data-type="12"]{background-color:${color}}
.resume_main[data_color] .default_item_html svg path:last-child{fill:${color};}
.resume_main[data_color] .default_html svg path{fill:${color};}
.resume_main[data_color] .default_item_html svg path:first-child{stroke:${color};}
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姓名
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錘子簡歷
萬眾狂歡時懂得沉默,一片寂靜時挺身而出。
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教育背景
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2009.09-2013.06
錘子簡歷大學(xué)
軟件工程
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工作經(jīng)驗
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2017.04-2019.11
錘子簡歷公司
自然語言處理工程師
- 負(fù)責(zé)公司消費者數(shù)據(jù)分析平臺自然語言處理算法模塊的研發(fā),如未登錄詞識別、文本分類、情感、語義分析等算法模型的訓(xùn)練、測試;
- 追蹤最新的處理技術(shù),關(guān)注業(yè)界前沿,并嘗試將技術(shù)熱點應(yīng)用落地,優(yōu)化平臺算法模型;
- 開發(fā)處理算法基礎(chǔ)圖形界面工具,提供數(shù)據(jù)分析師使用,提高分析能力和效率;
- 落地產(chǎn)品解決方案,并進(jìn)行效果調(diào)優(yōu), 發(fā)布相關(guān)產(chǎn)品,不斷迭代產(chǎn)品效果;
- 針對自然語言處理中遇到的問題進(jìn)行分析,并總結(jié)規(guī)則提出解決方案,同時進(jìn)行應(yīng)用方面的調(diào)研;
2015.01-2017.03
錘子簡歷公司
自然語言處理工程師
- 負(fù)責(zé)自然語言理解(NLU)研發(fā)工作,包含意圖識別、實體抽取等;
- 負(fù)責(zé)語音語義場景應(yīng)用數(shù)據(jù)及效果閉環(huán),優(yōu)化提升線上效果;
- 負(fù)責(zé)語料收集、挖掘訓(xùn)練工作,提升質(zhì)量及效率;
- 參加定期培訓(xùn),嘗試新的算法,持續(xù)不斷學(xué)習(xí)新技術(shù),擁有寬廣的技術(shù)視野;
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作品展示
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+(支持jpg/png格式,單張圖片不超過2M,最多支持添加8張圖片)
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其他
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- 技能: 熟悉自然語言處理、機(jī)器學(xué)習(xí)、深度學(xué)習(xí)等常用算法, 熟練使用自然語言處理、機(jī)器學(xué)習(xí)開源工具和深度學(xué)習(xí)框架,主要使用C++;精通Python/JAVA,有扎實的代碼功底和實戰(zhàn)能力;精通MATLAB、R語言等科學(xué)計算語言;精通多元統(tǒng)計分析的各種算法、模型;
- 語言能力: CET-6,有較強(qiáng)的聽說讀寫能力;
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項目經(jīng)歷
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2018.01-2018.03
文本分類等模型圖形用戶界面工具
- 開發(fā)詞向量、文本分類等基礎(chǔ)模型,幫助數(shù)據(jù)分析師在各領(lǐng)域項目中自主處理語料,提高工作效率;
- 研發(fā)基于結(jié)巴分詞工具和 fasttext 算法 C++版本的文本分類接口,使用 QT Creator開發(fā)圖形用戶界面工具,訓(xùn)練食品、母嬰等行業(yè)領(lǐng)域的詞向量;
- 使用 wxpython 開發(fā)訓(xùn)練詞向量、二元短語結(jié)構(gòu)識別、未登錄詞識別等模型的 GUI,測試、發(fā)布工具以及后續(xù)的用戶反饋與優(yōu)化;