embedinggemma_arkts / README.md

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:39122
  - loss:MultipleNegativesRankingLoss
base_model: google/embeddinggemma-300m
widget:
  - source_sentence: 组件即将出现时加载收藏商家数据
    sentences:
      - >-
        static async delete(key: string, preferenceName: string =
        defaultPreferenceName) {
            let preferences = await this.getPreferences(preferenceName)
            return await preferences.delete(key)
          }
      - |-
        async aboutToAppear(): Promise<void> {
            await this.loadFavoriteMerchants();
          }
      - |-
        Copyright (c) 2022 Huawei Device Co., Ltd.
        Licensed under the Apache License,Version 2.0 (the "License");
        you may not use this file except in compliance with the License.
        You may obtain a copy of the License at

        http://www.apache.org/licenses/LICENSE-2.0

        Unless required by applicable law or agreed to in writing, software
        distributed under the License is distributed on an "AS IS" BASIS,
        WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
        See the License for the specific language governing permissions and
        limitations under the License.
  - source_sentence: |-
      @Builder
        buildBottomNavigation() {
          Tabs({ index: this.currentTabIndex }) {
            TabContent() {
              // 首页内容在主区域显示
            }
            .tabBar(this.buildTabBarItem('首页', $r('app.media.ic_home'), 0))
            
            TabContent() {
              // 联系人内容在主区域显示
            }
            .tabBar(this.buildTabBarItem('联系人', $r('app.media.ic_contacts'), 1))
            
            TabContent() {
              // 日历内容在主区域显示
            }
            .tabBar(this.buildTabBarItem('日历', $r('app.media.ic_calendar'), 2))
            
            TabContent() {
              // 祝福语内容在主区域显示
            }
            .tabBar(this.buildTabBarItem('祝福语', $r('app.media.ic_greetings'), 3))
            
            TabContent() {
              // 设置内容在主区域显示
            }
            .tabBar(this.buildTabBarItem('设置', $r('app.media.ic_settings'), 4))
          }
          .onChange((index: number) => {
            this.onTabChange(index);
          })
          .barPosition(BarPosition.End)
          .barBackgroundColor('#ffffff')
          .barHeight(60)
        }
    sentences:
      - 定义List的builder方法
      - 错误相关常量
      - 构建底部导航栏
  - source_sentence: 插入数据库
    sentences:
      - |-
        static dateToTimestamp(date: Date): number {
            return date.getTime();
          }
      - >-
        public async insertData(context: common.Context, Contact: Contact):
        Promise<void> {
            logger.info(TAG, 'insert begin');
            if (!context) {
              logger.info(TAG, 'context is null or undefined');
            }

            const predicates = new rdb.RdbPredicates(TABLE_NAME);
            if (predicates === null || predicates === undefined) {
              logger.info(TAG, 'predicates is null or undefined');
            }

            this.rdbStore = await rdb.getRdbStore(context, STORE_CONFIG);

            let value1 = Contact.name;
            let value2 = Contact.phone;
            let value3 = Contact.email;
            let value4 = Contact.address;
            let value5 = Contact.avatar;
            let value6 = Contact.category;

            const valueBucket: ValuesBucket = {
              'name': value1,
              'phone': value2,
              'email': value3,
              'address': value4,
              'avatar': value5,
              'category': value6
            }

            if (this.rdbStore != undefined) {
              this.rdbStore.insert(TABLE_NAME, valueBucket, rdb.ConflictResolution.ON_CONFLICT_REPLACE,
                (err: BusinessError, rows: number) => {
                  if (err) {
                    logger.info(TAG, "Insert failed, err: " + err)
                    return
                  }
                  logger.info(TAG, `insert done:${rows}`);
                  promptAction.showToast({
                    message: $r('app.string.operate_rdb_in_taskpool_add_prompt_text', Contact.name),
                    duration: CommonConstants.PROMPT_DURATION_TIME
                  });
                })
            }
          }
      - 日历日期的代办事项
  - source_sentence: |-
      private recordOperation(
          type: 'create' | 'update' | 'delete' | 'complete' | 'cancel',
          todoId: string,
          changes?: ChangeRecord,
          description?: string
        ): void {
          try {
            const record: TodoOperationRecord = {
              id: this.generateId(),
              type,
              todoId,
              changes,
              timestamp: new Date().toISOString(),
              description
            };

            this.operationRecords.unshift(record);
            
            // 只保留最近100条记录
            if (this.operationRecords.length > 100) {
              this.operationRecords = this.operationRecords.slice(0, 100);
            }

            hilog.info(LogConstants.DOMAIN_APP, LogConstants.TAG_APP, `Recorded operation: ${type} for todo ${todoId}`);
          } catch (error) {
            hilog.error(LogConstants.DOMAIN_APP, LogConstants.TAG_APP, `Failed to record operation: ${error}`);
          }
        }
    sentences:
      - 记录操作
      - |-
        export interface DataConfig {
          autoBackup: AutoBackupConfig;
          dataRetention: DataRetentionConfig;
          syncConfig: SyncConfig;
        }
      - |-
        @Builder
          ExamSwitchModule() {
            Row() {
              Text('切换题库：')
                .fontSize(14)
              Text( this.guideService.guideData.licenseType !== undefined?licenseTypeName[this.guideService.guideData.licenseType]:'')
                .fontSize(14)
                .fontColor('#64BB5C')
              Image($r('app.media.right_triangle'))
                .width(16)
                .height(16)
                .fillColor('rgba(0,0,0,0.9)')
            }
            .width('100%')
            .justifyContent(FlexAlign.Start)
            .onClick(() => {
              this.vm.navStack.pushPathByName('guidePage', true)
            })
          }
  - source_sentence: 'resize(size: number): void;'
    sentences:
      - >-
        Resize the bitVector's length.


        @param { number } size - The new size for bitVector. If count is greater
        than the current size of bitVector,

        the additional bit elements are set to 0.

        @throws { BusinessError } 401 - Parameter error. Possible causes:

        1.Mandatory parameters are left unspecified.

        2.Incorrect parameter types.

        @throws { BusinessError } 10200011 - The resize method cannot be bound.

        @throws { BusinessError } 10200201 - Concurrent modification error.

        @syscap SystemCapability.Utils.Lang

        @atomicservice

        @since 12
         
        Resize the bitVector's length.


        @param { number } size - The new size for bitVector. If count is greater
        than the current size of bitVector,

        the additional bit elements are set to 0.

        @throws { BusinessError } 401 - Parameter error. Possible causes:

        1.Mandatory parameters are left unspecified.

        2.Incorrect parameter types.

        @throws { BusinessError } 10200011 - The resize method cannot be bound.

        @throws { BusinessError } 10200201 - Concurrent modification error.

        @syscap SystemCapability.Utils.Lang

        @crossplatform

        @atomicservice

        @since 18
      - |-
        makeNode(uiContext: UIContext): FrameNode {
            this.rootNode = new FrameNode(uiContext);
            if (this.rootNode !== null) {
              this.rootRenderNode = this.rootNode.getRenderNode();
            }
            return this.rootNode;
          }
      - |-
        export interface OnlineLunarYear {
          year: number;
          zodiac: string;
          ganzhi: string;
          leapMonth: number;
          isLeapYear: boolean;
          leapMonthDays?: number;
          solarTerms: SolarTermInfo[];
          festivals: LunarFestival[];
        }
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on google/embeddinggemma-300m

This is a sentence-transformers model finetuned from 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.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: google/embeddinggemma-300m
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
  (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})
  (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
  (4): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("hreyulog/embedinggemma_arkts")
# Run inference
queries = [
    "Transform an array of points with all matrices. VERY IMPORTANT: Keep\nmatrix order \"value-touch-offset\" when transforming.\n\n@param pts",
]
documents = [
    "public pointValuesToPixel(pts: number[]) {\n    this.mMatrixValueToPx.mapPoints(pts);\n    this.mViewPortHandler.getMatrixTouch().mapPoints(pts);\n    this.mMatrixOffset.mapPoints(pts);\n  }",
    '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  }',
    '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}',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.8923,  0.0264, -0.0212]])

Evaluation Results

On arkts-code-docstring dataset split test

Model	Params	MRR	NDCG@5	Recall@1	Recall@5
embedinggemma_arkts	308M	0.7788	0.8034	0.7142	0.8769
QWEN3-Embedding-0.6B	596M	0.6776	0.7015	0.6141	0.7723
embeddinggemma-300m	308M	0.6399	0.6654	0.5740	0.7416
BGE-M3	567M	0.5283	0.5603	0.4464	0.6558
BGE-base-zh-v1.5	110M	0.3598	0.3903	0.2841	0.4816
BGE-base-en-v1.5	110M	0.3439	0.3637	0.2935	0.4227
E5-base-v2	110M	0.3073	0.3261	0.2596	0.3823
BM25 (jieba)	–	0.2043	0.2204	0.1643	0.2690

Training Details

Training Dataset

Dataset: hreyulog/arkts-code-docstring

Size: 39,122 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 3 tokens
mean: 97.17 tokens
max: 512 tokens

min: 3 tokens
mean: 94.4 tokens
max: 512 tokens

	sentence_0	sentence_1
type	string	string
details	min: 3 tokens mean: 97.17 tokens max: 512 tokens	min: 3 tokens mean: 94.4 tokens max: 512 tokens

Samples:

sentence_0	sentence_1
`移除登录状态监听`	`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 }`
`PUT请求`	`static put(url: string, data?: Object, config: RequestConfig = {}): Promise> { const putConfig: RequestConfig = { method: http.RequestMethod.PUT, headers: config.headers, timeout: config.timeout, data: data }; return HttpUtil.request(url, putConfig); }`
`Transform an array of points with all matrices. VERY IMPORTANT: Keep\nmatrix order "value-touch-offset" when transforming.\n\n@param pts`	`public pointValuesToPixel(pts: number[]) {\n this.mMatrixValueToPx.mapPoints(pts);\n this.mViewPortHandler.getMatrixTouch().mapPoints(pts);\n this.mMatrixOffset.mapPoints(pts);\n }`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 2
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 2
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_ratio: None
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
enable_jit_checkpoint: False
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
use_cpu: False
seed: 42
data_seed: None
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: -1
ddp_backend: None
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
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: {}

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

@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}, 
}