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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- dense |
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- generated_from_trainer |
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- dataset_size:39122 |
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- loss:MultipleNegativesRankingLoss |
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base_model: google/embeddinggemma-300m |
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widget: |
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- source_sentence: 组件即将出现时加载收藏商家数据 |
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sentences: |
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- "static async delete(key: string, preferenceName: string = defaultPreferenceName)\ |
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\ {\n let preferences = await this.getPreferences(preferenceName)\n return\ |
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\ await preferences.delete(key)\n }" |
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- "async aboutToAppear(): Promise<void> {\n await this.loadFavoriteMerchants();\n\ |
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\ }" |
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- 'Copyright (c) 2022 Huawei Device Co., Ltd. |
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Licensed under the Apache License,Version 2.0 (the "License"); |
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you may not use this file except in compliance with the License. |
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You may obtain a copy of the License at |
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http://www.apache.org/licenses/LICENSE-2.0 |
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Unless required by applicable law or agreed to in writing, software |
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distributed under the License is distributed on an "AS IS" BASIS, |
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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See the License for the specific language governing permissions and |
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limitations under the License.' |
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- source_sentence: "@Builder\n buildBottomNavigation() {\n Tabs({ index: this.currentTabIndex\ |
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\ }) {\n TabContent() {\n // 首页内容在主区域显示\n }\n .tabBar(this.buildTabBarItem('首页',\ |
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\ $r('app.media.ic_home'), 0))\n \n TabContent() {\n // 联系人内容在主区域显示\n\ |
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\ }\n .tabBar(this.buildTabBarItem('联系人', $r('app.media.ic_contacts'),\ |
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\ 1))\n \n TabContent() {\n // 日历内容在主区域显示\n }\n .tabBar(this.buildTabBarItem('日历',\ |
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\ $r('app.media.ic_calendar'), 2))\n \n TabContent() {\n // 祝福语内容在主区域显示\n\ |
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\ }\n .tabBar(this.buildTabBarItem('祝福语', $r('app.media.ic_greetings'),\ |
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\ 3))\n \n TabContent() {\n // 设置内容在主区域显示\n }\n .tabBar(this.buildTabBarItem('设置',\ |
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\ $r('app.media.ic_settings'), 4))\n }\n .onChange((index: number) => {\n\ |
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\ this.onTabChange(index);\n })\n .barPosition(BarPosition.End)\n \ |
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\ .barBackgroundColor('#ffffff')\n .barHeight(60)\n }" |
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sentences: |
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- 定义List的builder方法 |
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- 错误相关常量 |
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- 构建底部导航栏 |
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- source_sentence: 插入数据库 |
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sentences: |
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- "static dateToTimestamp(date: Date): number {\n return date.getTime();\n }" |
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- "public async insertData(context: common.Context, Contact: Contact): Promise<void>\ |
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\ {\n logger.info(TAG, 'insert begin');\n if (!context) {\n logger.info(TAG,\ |
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\ 'context is null or undefined');\n }\n\n const predicates = new rdb.RdbPredicates(TABLE_NAME);\n\ |
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\ if (predicates === null || predicates === undefined) {\n logger.info(TAG,\ |
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\ 'predicates is null or undefined');\n }\n\n this.rdbStore = await rdb.getRdbStore(context,\ |
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\ STORE_CONFIG);\n\n let value1 = Contact.name;\n let value2 = Contact.phone;\n\ |
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\ let value3 = Contact.email;\n let value4 = Contact.address;\n let value5\ |
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\ = Contact.avatar;\n let value6 = Contact.category;\n\n const valueBucket:\ |
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\ ValuesBucket = {\n 'name': value1,\n 'phone': value2,\n 'email':\ |
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\ value3,\n 'address': value4,\n 'avatar': value5,\n 'category':\ |
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\ value6\n }\n\n if (this.rdbStore != undefined) {\n this.rdbStore.insert(TABLE_NAME,\ |
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\ valueBucket, rdb.ConflictResolution.ON_CONFLICT_REPLACE,\n (err: BusinessError,\ |
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\ rows: number) => {\n if (err) {\n logger.info(TAG, \"Insert\ |
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\ failed, err: \" + err)\n return\n }\n logger.info(TAG,\ |
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\ `insert done:${rows}`);\n promptAction.showToast({\n message:\ |
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\ $r('app.string.operate_rdb_in_taskpool_add_prompt_text', Contact.name),\n \ |
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\ duration: CommonConstants.PROMPT_DURATION_TIME\n });\n \ |
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\ })\n }\n }" |
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- 日历日期的代办事项 |
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- source_sentence: "private recordOperation(\n type: 'create' | 'update' | 'delete'\ |
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\ | 'complete' | 'cancel',\n todoId: string,\n changes?: ChangeRecord,\n\ |
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\ description?: string\n ): void {\n try {\n const record: TodoOperationRecord\ |
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\ = {\n id: this.generateId(),\n type,\n todoId,\n \ |
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\ changes,\n timestamp: new Date().toISOString(),\n description\n\ |
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\ };\n\n this.operationRecords.unshift(record);\n \n // 只保留最近100条记录\n\ |
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\ if (this.operationRecords.length > 100) {\n this.operationRecords\ |
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\ = this.operationRecords.slice(0, 100);\n }\n\n hilog.info(LogConstants.DOMAIN_APP,\ |
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\ LogConstants.TAG_APP, `Recorded operation: ${type} for todo ${todoId}`);\n \ |
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\ } catch (error) {\n hilog.error(LogConstants.DOMAIN_APP, LogConstants.TAG_APP,\ |
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\ `Failed to record operation: ${error}`);\n }\n }" |
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sentences: |
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- 记录操作 |
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- "export interface DataConfig {\n autoBackup: AutoBackupConfig;\n dataRetention:\ |
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\ DataRetentionConfig;\n syncConfig: SyncConfig;\n}" |
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- "@Builder\n ExamSwitchModule() {\n Row() {\n Text('切换题库：')\n .fontSize(14)\n\ |
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\ Text( this.guideService.guideData.licenseType !== undefined?licenseTypeName[this.guideService.guideData.licenseType]:'')\n\ |
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\ .fontSize(14)\n .fontColor('#64BB5C')\n Image($r('app.media.right_triangle'))\n\ |
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\ .width(16)\n .height(16)\n .fillColor('rgba(0,0,0,0.9)')\n\ |
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\ }\n .width('100%')\n .justifyContent(FlexAlign.Start)\n .onClick(()\ |
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\ => {\n this.vm.navStack.pushPathByName('guidePage', true)\n })\n }" |
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- source_sentence: 'resize(size: number): void;' |
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sentences: |
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- "Resize the bitVector's length.\n\n@param { number } size - The new size for bitVector.\ |
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\ If count is greater than the current size of bitVector,\nthe additional bit\ |
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\ elements are set to 0.\n@throws { BusinessError } 401 - Parameter error. Possible\ |
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\ causes:\n1.Mandatory parameters are left unspecified.\n2.Incorrect parameter\ |
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\ types.\n@throws { BusinessError } 10200011 - The resize method cannot be bound.\n\ |
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@throws { BusinessError } 10200201 - Concurrent modification error.\n@syscap SystemCapability.Utils.Lang\n\ |
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@atomicservice\n@since 12\n \nResize the bitVector's length.\n\n@param { number\ |
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\ } size - The new size for bitVector. If count is greater than the current size\ |
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\ of bitVector,\nthe additional bit elements are set to 0.\n@throws { BusinessError\ |
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\ } 401 - Parameter error. Possible causes:\n1.Mandatory parameters are left unspecified.\n\ |
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2.Incorrect parameter types.\n@throws { BusinessError } 10200011 - The resize\ |
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\ method cannot be bound.\n@throws { BusinessError } 10200201 - Concurrent modification\ |
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\ error.\n@syscap SystemCapability.Utils.Lang\n@crossplatform\n@atomicservice\n\ |
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@since 18" |
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- "makeNode(uiContext: UIContext): FrameNode {\n this.rootNode = new FrameNode(uiContext);\n\ |
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\ if (this.rootNode !== null) {\n this.rootRenderNode = this.rootNode.getRenderNode();\n\ |
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\ }\n return this.rootNode;\n }" |
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- "export interface OnlineLunarYear {\n year: number;\n zodiac: string;\n ganzhi:\ |
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\ string;\n leapMonth: number;\n isLeapYear: boolean;\n leapMonthDays?: number;\n\ |
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\ solarTerms: SolarTermInfo[];\n festivals: LunarFestival[];\n}" |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on google/embeddinggemma-300m |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'}) |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) |
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(3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) |
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(4): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("hreyulog/embedinggemma_arkts") |
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# Run inference |
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queries = [ |
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"Transform an array of points with all matrices. VERY IMPORTANT: Keep\nmatrix order \"value-touch-offset\" when transforming.\n\n@param pts", |
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] |
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documents = [ |
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"public pointValuesToPixel(pts: number[]) {\n this.mMatrixValueToPx.mapPoints(pts);\n this.mViewPortHandler.getMatrixTouch().mapPoints(pts);\n this.mMatrixOffset.mapPoints(pts);\n }", |
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'makeNode(uiContext: UIContext): FrameNode {\n this.rootNode = new FrameNode(uiContext);\n if (this.rootNode !== null) {\n this.rootRenderNode = this.rootNode.getRenderNode();\n }\n return this.rootNode;\n }', |
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'export interface OnlineLunarYear {\n year: number;\n zodiac: string;\n ganzhi: string;\n leapMonth: number;\n isLeapYear: boolean;\n leapMonthDays?: number;\n solarTerms: SolarTermInfo[];\n festivals: LunarFestival[];\n}', |
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] |
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query_embeddings = model.encode_query(queries) |
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document_embeddings = model.encode_document(documents) |
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print(query_embeddings.shape, document_embeddings.shape) |
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# [1, 768] [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(query_embeddings, document_embeddings) |
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print(similarities) |
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# tensor([[ 0.8923, 0.0264, -0.0212]]) |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Evaluation Results |
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On arkts-code-docstring dataset split test |
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| Model | Params | MRR | NDCG@5 | Recall@1 | Recall@5 | |
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|------|--------|-----|--------|----------|----------| |
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| **embedinggemma_arkts** | 308M | **0.7788** | **0.8034** | **0.7142** | **0.8769** | |
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| QWEN3-Embedding-0.6B | 596M | 0.6776 | 0.7015 | 0.6141 | 0.7723 | |
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| embeddinggemma-300m | 308M | 0.6399 | 0.6654 | 0.5740 | 0.7416 | |
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| BGE-M3 | 567M | 0.5283 | 0.5603 | 0.4464 | 0.6558 | |
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| BGE-base-zh-v1.5 | 110M | 0.3598 | 0.3903 | 0.2841 | 0.4816 | |
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| BGE-base-en-v1.5 | 110M | 0.3439 | 0.3637 | 0.2935 | 0.4227 | |
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| E5-base-v2 | 110M | 0.3073 | 0.3261 | 0.2596 | 0.3823 | |
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| BM25 (jieba) | – | 0.2043 | 0.2204 | 0.1643 | 0.2690 | |
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## Training Details |
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### Training Dataset |
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Dataset: [hreyulog/arkts-code-docstring](https://huggingface.co/datasets/hreyulog/arkts-code-docstring) |
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* Size: 39,122 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 97.17 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 94.4 tokens</li><li>max: 512 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>移除登录状态监听</code> | <code>public removeLoginStateListener(listener: (isLoggedIn: boolean) => void) {\n const index = this.loginStateListeners.indexOf(listener);\n if (index !== -1) {\n this.loginStateListeners.splice(index, 1);\n }\n }</code> | |
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| <code>PUT请求</code> | <code>static put<T = Object>(url: string, data?: Object, config: RequestConfig = {}): Promise<HttpResponse<T>> {<br> const putConfig: RequestConfig = {<br> method: http.RequestMethod.PUT,<br> headers: config.headers,<br> timeout: config.timeout,<br> data: data<br> };<br> return HttpUtil.request<T>(url, putConfig);<br> }</code> | |
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| <code>Transform an array of points with all matrices. VERY IMPORTANT: Keep\nmatrix order \"value-touch-offset\" when transforming.\n\n@param pts</code> | <code>public pointValuesToPixel(pts: number[]) {\n this.mMatrixValueToPx.mapPoints(pts);\n this.mViewPortHandler.getMatrixTouch().mapPoints(pts);\n this.mMatrixOffset.mapPoints(pts);\n }</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim", |
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"gather_across_devices": false |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `num_train_epochs`: 2 |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 2 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: None |
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- `warmup_ratio`: None |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `enable_jit_checkpoint`: False |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `use_cpu`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `bf16`: False |
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- `fp16`: False |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: -1 |
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- `ddp_backend`: None |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `parallelism_config`: None |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `project`: huggingface |
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- `trackio_space_id`: trackio |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
|
|
- `dataloader_persistent_workers`: False |
|
|
- `skip_memory_metrics`: True |
|
|
- `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_for_metrics`: [] |
|
|
- `eval_do_concat_batches`: True |
|
|
- `auto_find_batch_size`: False |
|
|
- `full_determinism`: False |
|
|
- `ddp_timeout`: 1800 |
|
|
- `torch_compile`: False |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
|
|
- `include_num_input_tokens_seen`: no |
|
|
- `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`: True |
|
|
- `use_cache`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: batch_sampler |
|
|
- `multi_dataset_batch_sampler`: round_robin |
|
|
- `router_mapping`: {} |
|
|
- `learning_rate_mapping`: {} |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | |
|
|
|:------:|:----:|:-------------:| |
|
|
| 0.4088 | 500 | 0.3798 | |
|
|
| 0.8177 | 1000 | 0.2489 | |
|
|
| 1.2265 | 1500 | 0.1308 | |
|
|
| 1.6353 | 2000 | 0.0877 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.10.19 |
|
|
- Sentence Transformers: 5.2.2 |
|
|
- Transformers: 5.0.0 |
|
|
- PyTorch: 2.9.1 |
|
|
- Accelerate: 1.12.0 |
|
|
- Datasets: 4.5.0 |
|
|
- Tokenizers: 0.22.2 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
|
|
|
#### ArkTS-CodeSearch |
|
|
```bibtex |
|
|
@misc{he2026arktscodesearchopensourcearktsdataset, |
|
|
title={ArkTS-CodeSearch: A Open-Source ArkTS Dataset for Code Retrieval}, |
|
|
author={Yulong He and Artem Ermakov and Sergey Kovalchuk and Artem Aliev and Dmitry Shalymov}, |
|
|
year={2026}, |
|
|
eprint={2602.05550}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.SE}, |
|
|
url={https://arxiv.org/abs/2602.05550}, |
|
|
} |
|
|
``` |
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