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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "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/config.json

@@ -0,0 +1,6 @@
+{
+    "in_features": 768,
+    "out_features": 3072,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

2_Dense/model.safetensors

@@ -0,0 +1,3 @@
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+oid sha256:b05e8eec2e91af5ffdd9ebf007b5ee85a9db2c92ac69c5ceb72b5221a6bff1a9
+size 9437272

3_Dense/config.json

@@ -0,0 +1,6 @@
+{
+    "in_features": 3072,
+    "out_features": 768,
+    "bias": false,
+    "activation_function": "torch.nn.modules.linear.Identity"
+}

3_Dense/model.safetensors

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README.md

@@ -0,0 +1,432 @@
+---
+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)\
+    \ {\n    let preferences = await this.getPreferences(preferenceName)\n    return\
+    \ await preferences.delete(key)\n  }"
+  - "async aboutToAppear(): Promise<void> {\n    await this.loadFavoriteMerchants();\n\
+    \  }"
+  - '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\n  buildBottomNavigation() {\n    Tabs({ index: this.currentTabIndex\
+    \ }) {\n      TabContent() {\n        // 首页内容在主区域显示\n      }\n      .tabBar(this.buildTabBarItem('首页',\
+    \ $r('app.media.ic_home'), 0))\n      \n      TabContent() {\n        // 联系人内容在主区域显示\n\
+    \      }\n      .tabBar(this.buildTabBarItem('联系人', $r('app.media.ic_contacts'),\
+    \ 1))\n      \n      TabContent() {\n        // 日历内容在主区域显示\n      }\n      .tabBar(this.buildTabBarItem('日历',\
+    \ $r('app.media.ic_calendar'), 2))\n      \n      TabContent() {\n        // 祝福语内容在主区域显示\n\
+    \      }\n      .tabBar(this.buildTabBarItem('祝福语', $r('app.media.ic_greetings'),\
+    \ 3))\n      \n      TabContent() {\n        // 设置内容在主区域显示\n      }\n      .tabBar(this.buildTabBarItem('设置',\
+    \ $r('app.media.ic_settings'), 4))\n    }\n    .onChange((index: number) => {\n\
+    \      this.onTabChange(index);\n    })\n    .barPosition(BarPosition.End)\n \
+    \   .barBackgroundColor('#ffffff')\n    .barHeight(60)\n  }"
+  sentences:
+  - 定义List的builder方法
+  - 错误相关常量
+  - 构建底部导航栏
+- source_sentence: 插入数据库
+  sentences:
+  - "static dateToTimestamp(date: Date): number {\n    return date.getTime();\n  }"
+  - "public async insertData(context: common.Context, Contact: Contact): Promise<void>\
+    \ {\n    logger.info(TAG, 'insert begin');\n    if (!context) {\n      logger.info(TAG,\
+    \ 'context is null or undefined');\n    }\n\n    const predicates = new rdb.RdbPredicates(TABLE_NAME);\n\
+    \    if (predicates === null || predicates === undefined) {\n      logger.info(TAG,\
+    \ 'predicates is null or undefined');\n    }\n\n    this.rdbStore = await rdb.getRdbStore(context,\
+    \ STORE_CONFIG);\n\n    let value1 = Contact.name;\n    let value2 = Contact.phone;\n\
+    \    let value3 = Contact.email;\n    let value4 = Contact.address;\n    let value5\
+    \ = Contact.avatar;\n    let value6 = Contact.category;\n\n    const valueBucket:\
+    \ ValuesBucket = {\n      'name': value1,\n      'phone': value2,\n      'email':\
+    \ value3,\n      'address': value4,\n      'avatar': value5,\n      'category':\
+    \ value6\n    }\n\n    if (this.rdbStore != undefined) {\n      this.rdbStore.insert(TABLE_NAME,\
+    \ valueBucket, rdb.ConflictResolution.ON_CONFLICT_REPLACE,\n        (err: BusinessError,\
+    \ rows: number) => {\n          if (err) {\n            logger.info(TAG, \"Insert\
+    \ failed, err: \" + err)\n            return\n          }\n          logger.info(TAG,\
+    \ `insert done:${rows}`);\n          promptAction.showToast({\n            message:\
+    \ $r('app.string.operate_rdb_in_taskpool_add_prompt_text', Contact.name),\n  \
+    \          duration: CommonConstants.PROMPT_DURATION_TIME\n          });\n   \
+    \     })\n    }\n  }"
+  - 日历日期的代办事项
+- source_sentence: "private recordOperation(\n    type: 'create' | 'update' | 'delete'\
+    \ | 'complete' | 'cancel',\n    todoId: string,\n    changes?: ChangeRecord,\n\
+    \    description?: string\n  ): void {\n    try {\n      const record: TodoOperationRecord\
+    \ = {\n        id: this.generateId(),\n        type,\n        todoId,\n      \
+    \  changes,\n        timestamp: new Date().toISOString(),\n        description\n\
+    \      };\n\n      this.operationRecords.unshift(record);\n      \n      // 只保留最近100条记录\n\
+    \      if (this.operationRecords.length > 100) {\n        this.operationRecords\
+    \ = this.operationRecords.slice(0, 100);\n      }\n\n      hilog.info(LogConstants.DOMAIN_APP,\
+    \ LogConstants.TAG_APP, `Recorded operation: ${type} for todo ${todoId}`);\n \
+    \   } catch (error) {\n      hilog.error(LogConstants.DOMAIN_APP, LogConstants.TAG_APP,\
+    \ `Failed to record operation: ${error}`);\n    }\n  }"
+  sentences:
+  - 记录操作
+  - "export interface DataConfig {\n  autoBackup: AutoBackupConfig;\n  dataRetention:\
+    \ DataRetentionConfig;\n  syncConfig: SyncConfig;\n}"
+  - "@Builder\n  ExamSwitchModule() {\n    Row() {\n      Text('切换题库：')\n        .fontSize(14)\n\
+    \      Text( this.guideService.guideData.licenseType !== undefined?licenseTypeName[this.guideService.guideData.licenseType]:'')\n\
+    \        .fontSize(14)\n        .fontColor('#64BB5C')\n      Image($r('app.media.right_triangle'))\n\
+    \        .width(16)\n        .height(16)\n        .fillColor('rgba(0,0,0,0.9)')\n\
+    \    }\n    .width('100%')\n    .justifyContent(FlexAlign.Start)\n    .onClick(()\
+    \ => {\n      this.vm.navStack.pushPathByName('guidePage', true)\n    })\n  }"
+- source_sentence: 'resize(size: number): void;'
+  sentences:
+  - "Resize the bitVector's length.\n\n@param { number } size - The new size for bitVector.\
+    \ If count is greater than the current size of bitVector,\nthe additional bit\
+    \ elements are set to 0.\n@throws { BusinessError } 401 - Parameter error. Possible\
+    \ causes:\n1.Mandatory parameters are left unspecified.\n2.Incorrect parameter\
+    \ types.\n@throws { BusinessError } 10200011 - The resize method cannot be bound.\n\
+    @throws { BusinessError } 10200201 - Concurrent modification error.\n@syscap SystemCapability.Utils.Lang\n\
+    @atomicservice\n@since 12\n \nResize the bitVector's length.\n\n@param { number\
+    \ } size - The new size for bitVector. If count is greater than the current size\
+    \ of bitVector,\nthe additional bit elements are set to 0.\n@throws { BusinessError\
+    \ } 401 - Parameter error. Possible causes:\n1.Mandatory parameters are left unspecified.\n\
+    2.Incorrect parameter types.\n@throws { BusinessError } 10200011 - The resize\
+    \ method cannot be bound.\n@throws { BusinessError } 10200201 - Concurrent modification\
+    \ error.\n@syscap SystemCapability.Utils.Lang\n@crossplatform\n@atomicservice\n\
+    @since 18"
+  - "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}"
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on google/embeddinggemma-300m
+
+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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **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({'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:
+
+```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 = [
+    "resize(size: number): void;",
+]
+documents = [
+    "Resize the bitVector's length.\n\n@param { number } size - The new size for bitVector. If count is greater than the current size of bitVector,\nthe additional bit elements are set to 0.\n@throws { BusinessError } 401 - Parameter error. Possible causes:\n1.Mandatory parameters are left unspecified.\n2.Incorrect parameter types.\n@throws { BusinessError } 10200011 - The resize method cannot be bound.\n@throws { BusinessError } 10200201 - Concurrent modification error.\n@syscap SystemCapability.Utils.Lang\n@atomicservice\n@since 12\n \nResize the bitVector's length.\n\n@param { number } size - The new size for bitVector. If count is greater than the current size of bitVector,\nthe additional bit elements are set to 0.\n@throws { BusinessError } 401 - Parameter error. Possible causes:\n1.Mandatory parameters are left unspecified.\n2.Incorrect parameter types.\n@throws { BusinessError } 10200011 - The resize method cannot be bound.\n@throws { BusinessError } 10200201 - Concurrent modification error.\n@syscap SystemCapability.Utils.Lang\n@crossplatform\n@atomicservice\n@since 18",
+    '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]])
+```
+
+<!--
+### 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.*
+-->
+
+<!--
+## 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: 39,122 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                        |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            |
+  | 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> |
+* Samples:
+  | sentence_0                                                                                                                                                                                                                                                                                   | sentence_1                                                                                                                                                                                                                                                                                                                                                             |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>static showToast(options: ShowToastOptions): void;</code>                                                                                                                                                                                                                              | <code>Displays the notification text.<br><br>@param { ShowToastOptions } options - Options.<br>@syscap SystemCapability.ArkUI.ArkUI.Full<br>@since 3<br> <br>Displays the notification text.<br><br>@param { ShowToastOptions } options - Options.<br>@syscap SystemCapability.ArkUI.ArkUI.Full<br>@atomicservice<br>@since 11</code>                                  |
+  | <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> |
+  | <code>protected init(): void {<br>    super.init();<br><br>    if (this.mAnimator) {<br>      this.mRenderer = new PieChartRenderer(this, this.mAnimator, this.mViewPortHandler);<br>    }<br>    this.mXAxis = null;<br><br>    this.mHighlighter = new PieHighlighter(this);<br>  }</code> | <code>@Override</code>                                                                                                                                                                                                                                                                                                                                                 |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "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
+<details><summary>Click to expand</summary>
+
+- `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`: {}
+
+</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
+
+#### 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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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 +1,68 @@
+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3TextModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
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config_sentence_transformers.json

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modules.json

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sentence_bert_config.json

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tokenizer.json

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tokenizer_config.json

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