Update README.md

README.md

@@ -1,577 +1,124 @@
 ---
 tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:2732400
-- loss:MultipleNegativesRankingLoss
-base_model: Shuu12121/CodeModernBERT-Owl-2.0
-widget:
-- source_sentence: 'put string value.
-
-
-    @param body _.
-
-    @throws IllegalArgumentException thrown if parameters fail the validation.
-
-    @throws HttpResponseException thrown if the service returns an error.
-
-    @throws RuntimeException all other wrapped checked exceptions if the request fails
-    to be sent.'
-  sentences:
-  - "func ComposeSSHCloneURL(doer *user_model.User, ownerName, repoName string) string\
-    \ {\n\tsshUser := setting.SSH.User\n\tsshDomain := setting.SSH.Domain\n\n\tif\
-    \ sshUser == \"(DOER_USERNAME)\" {\n\t\t// Some users use SSH reverse-proxy and\
-    \ need to use the current signed-in username as the SSH user\n\t\t// to make the\
-    \ SSH reverse-proxy could prepare the user's public keys ahead.\n\t\t// For most\
-    \ cases we have the correct \"doer\", then use it as the SSH user.\n\t\t// If\
-    \ we can't get the doer, then use the built-in SSH user.\n\t\tif doer != nil {\n\
-    \t\t\tsshUser = doer.Name\n\t\t} else {\n\t\t\tsshUser = setting.SSH.BuiltinServerUser\n\
-    \t\t}\n\t}\n\n\t// non-standard port, it must use full URI\n\tif setting.SSH.Port\
-    \ != 22 {\n\t\tsshHost := net.JoinHostPort(sshDomain, strconv.Itoa(setting.SSH.Port))\n\
-    \t\treturn fmt.Sprintf(\"ssh://%s@%s/%s/%s.git\", sshUser, sshHost, url.PathEscape(ownerName),\
-    \ url.PathEscape(repoName))\n\t}\n\n\t// for standard port, it can use a shorter\
-    \ URI (without the port)\n\tsshHost := sshDomain\n\tif ip := net.ParseIP(sshHost);\
-    \ ip != nil && ip.To4() == nil {\n\t\tsshHost = \"[\" + sshHost + \"]\" // for\
-    \ IPv6 address, wrap it with brackets\n\t}\n\tif setting.Repository.UseCompatSSHURI\
-    \ {\n\t\treturn fmt.Sprintf(\"ssh://%s@%s/%s/%s.git\", sshUser, sshHost, url.PathEscape(ownerName),\
-    \ url.PathEscape(repoName))\n\t}\n\treturn fmt.Sprintf(\"%s@%s:%s/%s.git\", sshUser,\
-    \ sshHost, url.PathEscape(ownerName), url.PathEscape(repoName))\n}"
-  - "@java.lang.Override\n  public boolean hasFieldExtractionMetadata() {\n    return\
-    \ ((bitField0_ & 0x00000001) != 0);\n  }"
-  - "@Metadata(properties = { MetadataProperties.GENERATED })\n    @ServiceMethod(returns\
-    \ = ReturnType.SINGLE)\n    public void put(String body) {\n        this.serviceClient.put(body);\n\
-    \    }"
-- source_sentence: 'Optional. User specified ID for the notebook runtime.
-
-
-    Generated from protobuf field <code>string notebook_runtime_id = 4 [(.google.api.field_behavior)
-    = OPTIONAL];</code>
-
-    @return string'
-  sentences:
-  - "public function getNotebookRuntimeId()\n    {\n        return $this->notebook_runtime_id;\n\
-    \    }"
-  - "func (client *BlobContainersClient) BeginObjectLevelWorm(ctx context.Context,\
-    \ resourceGroupName string, accountName string, containerName string, options\
-    \ *BlobContainersClientBeginObjectLevelWormOptions) (*runtime.Poller[BlobContainersClientObjectLevelWormResponse],\
-    \ error) {\n\tif options == nil || options.ResumeToken == \"\" {\n\t\tresp, err\
-    \ := client.objectLevelWorm(ctx, resourceGroupName, accountName, containerName,\
-    \ options)\n\t\tif err != nil {\n\t\t\treturn nil, err\n\t\t}\n\t\tpoller, err\
-    \ := runtime.NewPoller(resp, client.internal.Pipeline(), &runtime.NewPollerOptions[BlobContainersClientObjectLevelWormResponse]{\n\
-    \t\t\tFinalStateVia: runtime.FinalStateViaLocation,\n\t\t\tTracer:        client.internal.Tracer(),\n\
-    \t\t})\n\t\treturn poller, err\n\t} else {\n\t\treturn runtime.NewPollerFromResumeToken(options.ResumeToken,\
-    \ client.internal.Pipeline(), &runtime.NewPollerFromResumeTokenOptions[BlobContainersClientObjectLevelWormResponse]{\n\
-    \t\t\tTracer: client.internal.Tracer(),\n\t\t})\n\t}\n}"
-  - "def version(self) -> Union[int, str]:\n        \n        if self._version is\
-    \ None:\n            self._version = self._get_next_version()\n        return\
-    \ self._version"
-- source_sentence: '<pre>
-
-    Output only. An email message received in reply to the case.
-
-    </pre>
-
-
-    <code>
-
-    .google.cloud.support.v2beta.EmailMessage email_message = 102 [(.google.api.field_behavior)
-    = OUTPUT_ONLY];
-
-    </code>
-
-
-    @return The emailMessage.'
-  sentences:
-  - "@java.lang.Override\n  public com.google.cloud.support.v2beta.EmailMessage getEmailMessage()\
-    \ {\n    if (eventObjectCase_ == 102) {\n      return (com.google.cloud.support.v2beta.EmailMessage)\
-    \ eventObject_;\n    }\n    return com.google.cloud.support.v2beta.EmailMessage.getDefaultInstance();\n\
-    \  }"
-  - "def df_isin(df, values):\n    \n    if is_list_like(values) and not isinstance(values,\
-    \ dict):\n        values = list(values)\n    elif not isinstance(\n        values,\
-    \ (SERIES_TYPE, DATAFRAME_TYPE, TENSOR_TYPE, INDEX_TYPE, dict)\n    ):\n     \
-    \   raise TypeError(\n            \"only list-like objects or dict are allowed\
-    \ to be passed to isin(), \"\n            f\"you passed a [{type(values)}]\"\n\
-    \        )\n    op = DataFrameIsin(values=values)\n    return op(df)"
-  - "public function getModelDeploymentMonitoringJobs()\n    {\n        return $this->model_deployment_monitoring_jobs;\n\
-    \    }"
-- source_sentence: Compute the maximum violation of KKT conditions.
-  sentences:
-  - "def with_url(self,raw_url: str) -> SearchesRequestBuilder:\n        \n      \
-    \  if raw_url is None:\n            raise TypeError(\"raw_url cannot be null.\"\
-    )\n        return SearchesRequestBuilder(self.request_adapter, raw_url)"
-  - "def get_subscription\n  # Create a client object. The client can be reused for\
-    \ multiple calls.\n  client = Google::Apps::Events::Subscriptions::V1::SubscriptionsService::Client.new\n\
-    \n  # Create a request. To set request fields, pass in keyword arguments.\n  request\
-    \ = Google::Apps::Events::Subscriptions::V1::GetSubscriptionRequest.new\n\n  #\
-    \ Call the get_subscription method.\n  result = client.get_subscription request\n\
-    \n  # The returned object is of type Google::Apps::Events::Subscriptions::V1::Subscription.\n\
-    \  p result\nend"
-  - "def compute_kkt_optimality(g, on_bound):\n    \n    g_kkt = g * on_bound\n  \
-    \  free_set = on_bound == 0\n    g_kkt[free_set] = np.abs(g[free_set])\n    return\
-    \ np.max(g_kkt)"
-- source_sentence: 'Creates a unary expression NEGATIVE
-
-
-    # Errors
-
-
-    This function errors when the argument''s type is not signed numeric'
-  sentences:
-  - "public function searchItemAction()\n    {\n        return $this->searchBase(\"\
-    ifgroupentry\", ['ifname', 'descr', 'members', 'sequence'], \"ifname\");\n   \
-    \ }"
-  - "pub fn poller(self) -> impl lro::Poller<(), crate::model::DeleteSitemapMetadata>\
-    \ {\n            type Operation =\n                lro::internal::Operation<wkt::Empty,\
-    \ crate::model::DeleteSitemapMetadata>;\n            let polling_error_policy\
-    \ = self.0.stub.get_polling_error_policy(&self.0.options);\n            let polling_backoff_policy\
-    \ = self.0.stub.get_polling_backoff_policy(&self.0.options);\n\n            let\
-    \ stub = self.0.stub.clone();\n            let mut options = self.0.options.clone();\n\
-    \            options.set_retry_policy(gax::retry_policy::NeverRetry);\n      \
-    \      let query = move |name| {\n                let stub = stub.clone();\n \
-    \               let options = options.clone();\n                async {\n    \
-    \                let op = GetOperation::new(stub)\n                        .set_name(name)\n\
-    \                        .with_options(options)\n                        .send()\n\
-    \                        .await?;\n                    Ok(Operation::new(op))\n\
-    \                }\n            };\n\n            let start = move || async {\n\
-    \                let op = self.send().await?;\n                Ok(Operation::new(op))\n\
-    \            };\n\n            lro::internal::new_unit_response_poller(\n    \
-    \            polling_error_policy,\n                polling_backoff_policy,\n\
-    \                start,\n                query,\n            )\n        }"
-  - "pub fn negative(\n    arg: Arc<dyn PhysicalExpr>,\n    input_schema: &Schema,\n\
-    ) -> Result<Arc<dyn PhysicalExpr>> {\n    let data_type = arg.data_type(input_schema)?;\n\
-    \    if !coercion::is_signed_numeric(&data_type) {\n        Err(DataFusionError::Internal(\n\
-    \            format!(\n                \"(- '{:?}') can't be evaluated because\
-    \ the expression's type is {:?}, not signed numeric\",\n                arg, data_type,\n\
-    \            ),\n        ))\n    } else {\n        Ok(Arc::new(NegativeExpr::new(arg)))\n\
-    \    }\n}"
+- code
+- python
+- java
+- javascript
+- go
+- ruby
+- rust
+- typescript
+- php
+- sentence-transformer
+base_model:
+- Shuu12121/CodeModernBERT-Owl-2.0
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
----
-
-# SentenceTransformer based on Shuu12121/CodeModernBERT-Owl-2.0
+license: apache-2.0
+language:
+- en
+datasets:
+- Shuu12121/python-codesearch-dedupe-filtered
+- Shuu12121/java-codesearch-dedupe-filtered
+- Shuu12121/javascript-codesearch-dedupe-filtered
+- Shuu12121/typescipt-codesearch-dedupe-filtered
+- Shuu12121/go-codesearch-dedupe-filtered
+- Shuu12121/ruby-codesearch-dedupe-filtered
+- Shuu12121/rust-codesearch-dedupe-filtered
+- Shuu12121/php-codesearch-dedupe-filtered
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Shuu12121/CodeModernBERT-Owl-2.0](https://huggingface.co/Shuu12121/CodeModernBERT-Owl-2.0). 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:** [Shuu12121/CodeModernBERT-Owl-2.0](https://huggingface.co/Shuu12121/CodeModernBERT-Owl-2.0) <!-- at revision a6f43b644188b4e7fe211f38003c7742218607c0 -->
-- **Maximum Sequence Length:** 1024 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/UKPLab/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': 1024, 'do_lower_case': False}) with Transformer model: ModernBertModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-)
-```
-
-## 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
-sentences = [
-    "Creates a unary expression NEGATIVE\n\n# Errors\n\nThis function errors when the argument's type is not signed numeric",
-    'pub fn negative(\n    arg: Arc<dyn PhysicalExpr>,\n    input_schema: &Schema,\n) -> Result<Arc<dyn PhysicalExpr>> {\n    let data_type = arg.data_type(input_schema)?;\n    if !coercion::is_signed_numeric(&data_type) {\n        Err(DataFusionError::Internal(\n            format!(\n                "(- \'{:?}\') can\'t be evaluated because the expression\'s type is {:?}, not signed numeric",\n                arg, data_type,\n            ),\n        ))\n    } else {\n        Ok(Arc::new(NegativeExpr::new(arg)))\n    }\n}',
-    'public function searchItemAction()\n    {\n        return $this->searchBase("ifgroupentry", [\'ifname\', \'descr\', \'members\', \'sequence\'], "ifname");\n    }',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 768]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
-```
-
-<!--
-### 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
+# 🦉 CodeSearch-ModernBERT-Owl-2.0-Plus
 
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
+##  日本語版（Japanese）
 
-<!--
-## Bias, Risks and Limitations
+**Shuu12121/CodeSearch-ModernBERT-Owl-2.0-Plus** は、マルチリンガルなコード理解・検索のために設計された **CodeModernBERT-Owl** 系列の最新事前学習モデルです。  
+本モデルは、VSCode拡張機能 [**OwlSpotlight**](https://marketplace.visualstudio.com/items?itemName=Shun0212.owlspotlight) にて使用されており、関数レベルの意味的コード検索を実現します。
 
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
+### 🔧 特徴
 
-<!--
-### Recommendations
+- **独自コーパスで事前学習**  
+  CodeBERT (Feng et al., 2020) の約4倍の規模となる、完全独自収集の高品質なコード・docstringコーパスを用いて事前学習。
 
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
+- **8言語対応**  
+  Python, Java, JavaScript, PHP, Ruby, Go, Rust に加えて、**TypeScript** を新たにサポート。
 
-## Training Details
+- **長文対応（最大8192トークン）**  
+  訓練時最大2048トークン、推論時には8192トークンまでの入力を処理可能（Position Embedding拡張済み）。
 
-### Training Dataset
+- **徹底したノイズ除去・データクリーニング**
+  - Tree-sitter による関数・docstring抽出
+  - 無意味な定型コメント・多言語ノイズの除去
+  - シークレット・APIキーの自動マスキング
+  - ライセンス記述の除外
+  - 重複関数の除去によるリーク対策
 
-#### Unnamed Dataset
+### 📦 基本情報
 
-* Size: 2,732,400 training samples
-* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                            | sentence_1                                                                           | label                                                         |
-  |:--------|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:--------------------------------------------------------------|
-  | type    | string                                                                                | string                                                                               | float                                                         |
-  | details | <ul><li>min: 13 tokens</li><li>mean: 109.22 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 250.01 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
-* Samples:
-  | sentence_0                                                                                                | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | label            |
-  |:----------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
-  | <code>Prints the specified `pkg`.<br><br>If `is_main` is not set, nested package notation is used.</code> | <code>pub fn print_package(<br>        &mut self,<br>        resolve: &Resolve,<br>        pkg: PackageId,<br>        is_main: bool,<br>    ) -> Result<()> {<br>        let pkg = &resolve.packages[pkg];<br>        self.print_package_outer(pkg)?;<br><br>        if is_main {<br>            self.output.semicolon();<br>            self.output.newline();<br>        } else {<br>            self.output.indent_start();<br>        }<br><br>        for (name, id) in pkg.interfaces.iter() {<br>            self.print_interface_outer(resolve, *id, name)?;<br>            self.output.indent_start();<br>            self.print_interface(resolve, *id)?;<br>            self.output.indent_end();<br>            if is_main {<br>                self.output.newline();<br>            }<br>        }<br><br>        for (name, id) in pkg.worlds.iter() {<br>            self.print_docs(&resolve.worlds[*id].docs);<br>            self.print_stability(&resolve.worlds[*id].stability);<br>            self.output.keyword("world");<br>            self.output.str(" ");<br>            self.print_name_type(name, TypeKind:...</code> | <code>1.0</code> |
-  | <code><p>An alternative descriptive name for the user.</p></code>                                         | <code>pub fn nick_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {<br>        self.nick_name = ::std::option::Option::Some(input.into());<br>        self<br>    }</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | <code>1.0</code> |
-  | <code><p>Indicates whether the match is case sensitive.</p></code>                                        | <code>pub fn case_sensitive(mut self, input: bool) -> Self {<br>        self.case_sensitive = ::std::option::Option::Some(input);<br>        self<br>    }</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>1.0</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"
-  }
-  ```
+| 項目 | 内容 |
+|------|------|
+| モデル名 | Shuu12121/CodeSearch-ModernBERT-Owl-2.0-Plus |
+| モデルサイズ | 約150Mパラメータ（ModernBERTベース） |
+| 対応言語 | Python, Java, JavaScript, PHP, Ruby, Go, Rust, TypeScript |
+| 最大トークン長 | 学習時: 2048 / 推論時: 8192 |
+| トークナイザ | 独自BPE（52,000語彙） |
 
-### Training Hyperparameters
-#### Non-Default Hyperparameters
+### 🚀 主な用途
 
-- `per_device_train_batch_size`: 150
-- `per_device_eval_batch_size`: 150
-- `fp16`: True
-- `multi_dataset_batch_sampler`: round_robin
+- 関数レベルの意味的コード検索（自然言語 → 関数コード）
+- コード補完・要約・分類・クローン検出などの下流タスク
+- Retrieval-Augmented Generation（RAG）における高精度なコード検索
 
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
+### 🧪 利用例：VSCode拡張「[OwlSpotlight](https://github.com/Shun0212/OwlSpotLight)」
 
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: no
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 150
-- `per_device_eval_batch_size`: 150
-- `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`: 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`: 3
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.0
-- `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`: 42
-- `data_seed`: None
-- `jit_mode_eval`: False
-- `use_ipex`: False
-- `bf16`: False
-- `fp16`: True
-- `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}
-- `deepspeed`: None
-- `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch
-- `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
-- `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
-- `eval_use_gather_object`: False
-- `average_tokens_across_devices`: False
-- `prompts`: None
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
+本モデルは、[OwlSpotlight](https://marketplace.visualstudio.com/items?itemName=Shun0212.owlspotlight) に組み込まれており、自然言語による直感的な関数検索が可能です。  
+🖥 Mac（Mシリーズ含む）上で軽量に動作し、開発中のコードベースに即座にインデックス作成・検索できます。
 
-</details>
+---
 
-### Training Logs
-<details><summary>Click to expand</summary>
+##  English Version
 
-| Epoch  | Step  | Training Loss |
-|:------:|:-----:|:-------------:|
-| 0.0274 | 500   | 0.8232        |
-| 0.0549 | 1000  | 0.1248        |
-| 0.0823 | 1500  | 0.1102        |
-| 0.1098 | 2000  | 0.1008        |
-| 0.1372 | 2500  | 0.0962        |
-| 0.1647 | 3000  | 0.0928        |
-| 0.1921 | 3500  | 0.0878        |
-| 0.2196 | 4000  | 0.0827        |
-| 0.2470 | 4500  | 0.078         |
-| 0.2745 | 5000  | 0.0763        |
-| 0.3019 | 5500  | 0.075         |
-| 0.3294 | 6000  | 0.0716        |
-| 0.3568 | 6500  | 0.0691        |
-| 0.3843 | 7000  | 0.0673        |
-| 0.4117 | 7500  | 0.065         |
-| 0.4392 | 8000  | 0.0668        |
-| 0.4666 | 8500  | 0.0609        |
-| 0.4941 | 9000  | 0.0613        |
-| 0.5215 | 9500  | 0.0596        |
-| 0.5490 | 10000 | 0.0596        |
-| 0.5764 | 10500 | 0.058         |
-| 0.6039 | 11000 | 0.0527        |
-| 0.6313 | 11500 | 0.0521        |
-| 0.6588 | 12000 | 0.0521        |
-| 0.6862 | 12500 | 0.049         |
-| 0.7137 | 13000 | 0.0481        |
-| 0.7411 | 13500 | 0.0484        |
-| 0.7686 | 14000 | 0.049         |
-| 0.7960 | 14500 | 0.0482        |
-| 0.8235 | 15000 | 0.045         |
-| 0.8509 | 15500 | 0.0423        |
-| 0.8783 | 16000 | 0.0425        |
-| 0.9058 | 16500 | 0.04          |
-| 0.9332 | 17000 | 0.0406        |
-| 0.9607 | 17500 | 0.0374        |
-| 0.9881 | 18000 | 0.038         |
-| 1.0156 | 18500 | 0.0257        |
-| 1.0430 | 19000 | 0.0154        |
-| 1.0705 | 19500 | 0.015         |
-| 1.0979 | 20000 | 0.0157        |
-| 1.1254 | 20500 | 0.0144        |
-| 1.1528 | 21000 | 0.0148        |
-| 1.1803 | 21500 | 0.0152        |
-| 1.2077 | 22000 | 0.0154        |
-| 1.2352 | 22500 | 0.0161        |
-| 1.2626 | 23000 | 0.0155        |
-| 1.2901 | 23500 | 0.0148        |
-| 1.3175 | 24000 | 0.0152        |
-| 1.3450 | 24500 | 0.015         |
-| 1.3724 | 25000 | 0.0148        |
-| 1.3999 | 25500 | 0.0151        |
-| 1.4273 | 26000 | 0.0144        |
-| 1.4548 | 26500 | 0.0147        |
-| 1.4822 | 27000 | 0.0143        |
-| 1.5097 | 27500 | 0.0148        |
-| 1.5371 | 28000 | 0.0147        |
-| 1.5646 | 28500 | 0.0145        |
-| 1.5920 | 29000 | 0.0137        |
-| 1.6195 | 29500 | 0.0134        |
-| 1.6469 | 30000 | 0.0137        |
-| 1.6744 | 30500 | 0.0133        |
-| 1.7018 | 31000 | 0.0137        |
-| 1.7292 | 31500 | 0.0132        |
-| 1.7567 | 32000 | 0.0132        |
-| 1.7841 | 32500 | 0.0124        |
-| 1.8116 | 33000 | 0.0133        |
-| 1.8390 | 33500 | 0.0118        |
-| 1.8665 | 34000 | 0.0122        |
-| 1.8939 | 34500 | 0.0114        |
-| 1.9214 | 35000 | 0.0116        |
-| 1.9488 | 35500 | 0.0113        |
-| 1.9763 | 36000 | 0.0115        |
-| 2.0037 | 36500 | 0.0105        |
-| 2.0312 | 37000 | 0.0056        |
-| 2.0586 | 37500 | 0.0056        |
-| 2.0861 | 38000 | 0.0051        |
-| 2.1135 | 38500 | 0.0053        |
-| 2.1410 | 39000 | 0.0054        |
-| 2.1684 | 39500 | 0.0052        |
-| 2.1959 | 40000 | 0.0053        |
-| 2.2233 | 40500 | 0.0054        |
-| 2.2508 | 41000 | 0.0051        |
-| 2.2782 | 41500 | 0.0052        |
-| 2.3057 | 42000 | 0.0052        |
-| 2.3331 | 42500 | 0.0046        |
-| 2.3606 | 43000 | 0.0048        |
-| 2.3880 | 43500 | 0.0051        |
-| 2.4155 | 44000 | 0.0049        |
-| 2.4429 | 44500 | 0.0047        |
-| 2.4704 | 45000 | 0.0047        |
-| 2.4978 | 45500 | 0.0048        |
-| 2.5253 | 46000 | 0.005         |
-| 2.5527 | 46500 | 0.0049        |
-| 2.5801 | 47000 | 0.0047        |
-| 2.6076 | 47500 | 0.0046        |
-| 2.6350 | 48000 | 0.0048        |
-| 2.6625 | 48500 | 0.0045        |
-| 2.6899 | 49000 | 0.0043        |
-| 2.7174 | 49500 | 0.0047        |
-| 2.7448 | 50000 | 0.0045        |
-| 2.7723 | 50500 | 0.0046        |
-| 2.7997 | 51000 | 0.0046        |
-| 2.8272 | 51500 | 0.0044        |
-| 2.8546 | 52000 | 0.0042        |
-| 2.8821 | 52500 | 0.0045        |
-| 2.9095 | 53000 | 0.0045        |
-| 2.9370 | 53500 | 0.0043        |
-| 2.9644 | 54000 | 0.0044        |
-| 2.9919 | 54500 | 0.0043        |
+**Shuu12121/CodeSearch-ModernBERT-Owl-2.0-Plus** is the latest pretrained model in the multilingual **CodeModernBERT-Owl** series, designed for high-quality code understanding and semantic retrieval.
 
-</details>
+It powers the [**OwlSpotlight**](https://marketplace.visualstudio.com/items?itemName=Shun0212.owlspotlight) extension for VSCode, enabling fast and intuitive natural language to code search at the function level.
 
-### Framework Versions
-- Python: 3.11.12
-- Sentence Transformers: 4.1.0
-- Transformers: 4.52.3
-- PyTorch: 2.6.0+cu124
-- Accelerate: 1.6.0
-- Datasets: 3.6.0
-- Tokenizers: 0.21.1
+### 🔧 Highlights
 
-## Citation
+- **Pretrained on a custom large-scale corpus**  
+  The training corpus is ~4x larger than CodeBERT’s bimodal dataset and built entirely from scratch, ensuring high-quality code and documentation pairs.
 
-### BibTeX
+- **Supports 8 programming languages**  
+  Python, Java, JavaScript, PHP, Ruby, Go, Rust, and newly **TypeScript**.
 
-#### 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",
-}
-```
+- **Long-sequence input support**  
+  Trained on sequences up to 2048 tokens, and extended to handle 8192 tokens at inference.
 
-#### 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}
-}
-```
+- **Robust data cleaning & filtering**  
+  - Tree-sitter-based function/docstring extraction  
+  - Removal of templated or non-English comments  
+  - API key and secret masking  
+  - License-related content exclusion  
+  - Deduplication for data leakage prevention
 
-<!--
-## Glossary
+### 📦 Model Specs
 
-*Clearly define terms in order to be accessible across audiences.*
--->
+| Item | Detail |
+|------|--------|
+| Name | Shuu12121/CodeSearch-ModernBERT-Owl-2.0-Plus |
+| Size | ~150M parameters (ModernBERT backbone) |
+| Supported Languages | Python, Java, JavaScript, PHP, Ruby, Go, Rust, TypeScript |
+| Max Token Length | 2048 (train), 8192 (inference) |
+| Tokenizer | Custom BPE tokenizer (52k vocab) |
 
-<!--
-## Model Card Authors
+### 🚀 Use Cases
 
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
+- Function-level semantic code search (natural language → code)
+- Code completion, summarization, classification, and clone detection
+- Retrieval for RAG systems
 
-<!--
-## Model Card Contact
+### 🧪 Real-World Use: [OwlSpotlight](https://github.com/Shun0212/OwlSpotLight)
 
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+This model is used in the [OwlSpotlight](https://marketplace.visualstudio.com/items?itemName=Shun0212.owlspotlight) VSCode extension.  
+Search through your Python codebase using plain English and jump instantly to relevant functions with semantic understanding.  
+Tested and optimized for macOS (including Apple Silicon).