End of training

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
+}

README.md

@@ -0,0 +1,631 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:283621
+- loss:CachedMultipleNegativesRankingLoss
+base_model: answerdotai/ModernBERT-base
+widget:
+- source_sentence: '// Uint is a helper routine that allocates a new uint value to
+    store v and
+
+    // returns a pointer to it.  This is useful when assigning optional parameters.'
+  sentences:
+  - "func (c *Animation) GetCurrentTimeWithParams(v *AnimationGetCurrentTimeParams)\
+    \ (float64, error) {\n\tresp, err := gcdmessage.SendCustomReturn(c.target, c.target.GetSendCh(),\
+    \ &gcdmessage.ParamRequest{Id: c.target.GetId(), Method: \"Animation.getCurrentTime\"\
+    , Params: v})\n\tif err != nil {\n\t\treturn 0, err\n\t}\n\n\tvar chromeData struct\
+    \ {\n\t\tResult struct {\n\t\t\tCurrentTime float64\n\t\t}\n\t}\n\n\tif resp ==\
+    \ nil {\n\t\treturn 0, &gcdmessage.ChromeEmptyResponseErr{}\n\t}\n\n\t// test\
+    \ if error first\n\tcerr := &gcdmessage.ChromeErrorResponse{}\n\tjson.Unmarshal(resp.Data,\
+    \ cerr)\n\tif cerr != nil && cerr.Error != nil {\n\t\treturn 0, &gcdmessage.ChromeRequestErr{Resp:\
+    \ cerr}\n\t}\n\n\tif err := json.Unmarshal(resp.Data, &chromeData); err != nil\
+    \ {\n\t\treturn 0, err\n\t}\n\n\treturn chromeData.Result.CurrentTime, nil\n}"
+  - "func Uint(v uint) *uint {\n\tp := new(uint)\n\t*p = v\n\treturn p\n}"
+  - "def after_init_app(self, app: FlaskUnchained):\n        \"\"\"\n        Configure\
+    \ the JSON encoder for Flask to be able to serialize Enums,\n        LocalProxy\
+    \ objects, and SQLAlchemy models.\n        \"\"\"\n        self.set_json_encoder(app)\n\
+    \        app.before_first_request(self.register_model_resources)"
+- source_sentence: 'Returns a template for the parent of this template.
+
+
+    @throws ValidationException if the template has no parent.'
+  sentences:
+  - "func BodyContainsOr(values ...string) ResponseCondition {\n\treturn func(res\
+    \ *http.Response) error {\n\t\tbody, err := ioutil.ReadAll(res.Body)\n\t\tif err\
+    \ != nil {\n\t\t\treturn fmt.Errorf(\"failed to read response body: %s\", err)\n\
+    \t\t}\n\n\t\tfor _, value := range values {\n\t\t\tif strings.Contains(string(body),\
+    \ value) {\n\t\t\t\treturn nil\n\t\t\t}\n\t\t}\n\t\treturn fmt.Errorf(\"could\
+    \ not find '%v' in body '%s'\", values, string(body))\n\t}\n}"
+  - "protected function after_update($result) {\n        global $DB;\n\n        if\
+    \ (!$result) {\n            $this->beforeupdate = null;\n            return;\n\
+    \        }\n\n        // The parent ID has changed, we need to fix all the paths\
+    \ of the children.\n        if ($this->beforeupdate->get('parentid') != $this->get('parentid'))\
+    \ {\n            $beforepath = $this->beforeupdate->get('path') . $this->get('id')\
+    \ . '/';\n\n            $like = $DB->sql_like('path', '?');\n            $likesearch\
+    \ = $DB->sql_like_escape($beforepath) . '%';\n\n            $table = '{' . self::TABLE\
+    \ . '}';\n            $sql = \"UPDATE $table SET path = REPLACE(path, ?, ?) WHERE\
+    \ \" . $like;\n            $DB->execute($sql, array(\n                $beforepath,\n\
+    \                $this->get('path') . $this->get('id') . '/',\n              \
+    \  $likesearch\n            ));\n\n            // Resolving sortorder holes left\
+    \ after changing parent.\n            $table = '{' . self::TABLE . '}';\n    \
+    \        $sql = \"UPDATE $table SET sortorder = sortorder -1 \"\n            \
+    \        . \" WHERE  competencyframeworkid = ? AND parentid = ? AND sortorder\
+    \ > ?\";\n            $DB->execute($sql, array($this->get('competencyframeworkid'),\n\
+    \                                        $this->beforeupdate->get('parentid'),\n\
+    \                                        $this->beforeupdate->get('sortorder')\n\
+    \                                    ));\n        }\n\n        $this->beforeupdate\
+    \ = null;\n    }"
+  - "public PathTemplate parentTemplate() {\n    int i = segments.size();\n    Segment\
+    \ seg = segments.get(--i);\n    if (seg.kind() == SegmentKind.END_BINDING) {\n\
+    \      while (i > 0 && segments.get(--i).kind() != SegmentKind.BINDING) {}\n \
+    \   }\n    if (i == 0) {\n      throw new ValidationException(\"template does\
+    \ not have a parent\");\n    }\n    return new PathTemplate(segments.subList(0,\
+    \ i), urlEncoding);\n  }"
+- source_sentence: 'Build a potentially nested fieldgroup
+
+
+    @param mixed $valueOrGroup Value of item, or title of group
+
+    @param string|array $titleOrOptions Title of item, or options in grouip
+
+    @return ArrayData Data for this item'
+  sentences:
+  - "protected function getFieldOption($valueOrGroup, $titleOrOptions)\n    {\n  \
+    \      // Return flat option\n        if (!is_array($titleOrOptions)) {\n    \
+    \        return parent::getFieldOption($valueOrGroup, $titleOrOptions);\n    \
+    \    }\n\n        // Build children from options list\n        $options = new\
+    \ ArrayList();\n        foreach ($titleOrOptions as $childValue => $childTitle)\
+    \ {\n            $options->push($this->getFieldOption($childValue, $childTitle));\n\
+    \        }\n\n        return new ArrayData(array(\n            'Title' => $valueOrGroup,\n\
+    \            'Options' => $options\n        ));\n    }"
+  - "public static function minify($content, array $options = [])\n    {\n       \
+    \ $min = preg_replace(['/[\\n\\r]/', '/\\>[^\\S ]+/s', '/[^\\S ]+\\</s', '/(\\\
+    s)+/s', ], ['', '>', '<', '\\\\1'], trim($content));\n        $min = str_replace(['>\
+    \ <'], ['><'], $min);\n        \n        if (ArrayHelper::getValue($options, 'comments',\
+    \ false)) {\n            $min = preg_replace('/<!--(.*)-->/Uis', '', $min);\n\
+    \        }\n        \n        return $min;\n    }"
+  - "private function loadXInclude(XInclude $xinclude, $filePath){\n        //load\
+    \ DOMDocument\n        $xml = new DOMDocument();\n        $loadSuccess = $xml->load($filePath);\n\
+    \        $node = $xml->documentElement;\n        if($loadSuccess && !is_null($node)){\n\
+    \            //parse the href content\n            $parser = new ParserFactory($xml);\n\
+    \            $parser->loadContainerStatic($node, $xinclude->getBody());\n    \
+    \    }else{\n            throw new XIncludeException('Cannot load the XInclude\
+    \ DOM XML', $xinclude);\n        }\n    }"
+- source_sentence: "Check for new unread messages and send them to the custom api\n\
+    \n    @param client_id: ID of client user"
+  sentences:
+  - "public function getLatMap()\n    {\n        if (null === $this->latMap) {\n \
+    \           $this->latMap = $this->getTransliterationMap(Settings::ALPHABET_LAT);\n\
+    \        }\n\n        return $this->latMap;\n    }"
+  - "def check_new_messages(client_id):\n    \"\"\"Check for new unread messages and\
+    \ send them to the custom api\n\n    @param client_id: ID of client user\n   \
+    \ \"\"\"\n    # Return if driver is not defined or if whatsapp is not logged in.\n\
+    \    # Stop the timer as well\n    if client_id not in drivers or not drivers[client_id]\
+    \ or not drivers[client_id].is_logged_in():\n        timers[client_id].stop()\n\
+    \        return\n\n    # Acquire a lock on thread\n    if not acquire_semaphore(client_id,\
+    \ True):\n        return\n\n    try:\n        # Get all unread messages\n    \
+    \    res = drivers[client_id].get_unread()\n        # Mark all of them as seen\n\
+    \        for message_group in res:\n            message_group.chat.send_seen()\n\
+    \        # Release thread lock\n        release_semaphore(client_id)\n       \
+    \ # If we have new messages, do something with it\n        if res:\n         \
+    \   print(res)\n    except:\n        pass\n    finally:\n        # Release lock\
+    \ anyway, safekeeping\n        release_semaphore(client_id)"
+  - "def get_uppermost_library_root_state(self):\n        \"\"\"Find state_copy of\
+    \ uppermost LibraryState\n\n        Method checks if there is a parent library\
+    \ root state and assigns it to be the current library root state till\n      \
+    \  there is no further parent library root state.\n        \"\"\"\n\n        library_root_state\
+    \ = self.get_next_upper_library_root_state()\n        parent_library_root_state\
+    \ = library_root_state\n        # initial a library root state has to be found\
+    \ and if there is no further parent root state\n        # parent_library_root_state\
+    \ and library_root_state are no more identical\n        while parent_library_root_state\
+    \ and library_root_state is parent_library_root_state:\n            if library_root_state:\n\
+    \                parent_library_root_state = library_root_state.parent.get_next_upper_library_root_state()\n\
+    \n            if parent_library_root_state:\n                library_root_state\
+    \ = parent_library_root_state\n\n        return library_root_state"
+- source_sentence: If MultiTenantMiddleware is used, filter queryset by request.site_id
+  sentences:
+  - "def reduce_ticks(ax, which, maxticks=3):\n    \"\"\"Given a pyplot axis, resamples\
+    \ its `which`-axis ticks such that are at most\n    `maxticks` left.\n\n    Parameters\n\
+    \    ----------\n    ax : axis\n        The axis to adjust.\n    which : {'x'\
+    \ | 'y'}\n        Which axis to adjust.\n    maxticks : {3, int}\n        Maximum\
+    \ number of ticks to use.\n\n    Returns\n    -------\n    array\n        An array\
+    \ of the selected ticks.\n    \"\"\"\n    ticks = getattr(ax, 'get_{}ticks'.format(which))()\n\
+    \    if len(ticks) > maxticks:\n        # make sure the left/right value is not\
+    \ at the edge\n        minax, maxax = getattr(ax, 'get_{}lim'.format(which))()\n\
+    \        dw = abs(maxax-minax)/10.\n        start_idx, end_idx = 0, len(ticks)\n\
+    \        if ticks[0] < minax + dw:\n            start_idx += 1\n        if ticks[-1]\
+    \ > maxax - dw:\n            end_idx -= 1\n        # get reduction factor\n  \
+    \      fac = int(len(ticks) / maxticks)\n        ticks = ticks[start_idx:end_idx:fac]\n\
+    \    return ticks"
+  - "function (isPublic, name, data, ttl, published_at, coreid) {\n        var rawFn\
+    \ = function (msg) {\n            try {\n                msg.setMaxAge(parseInt((ttl\
+    \ && (ttl >= 0)) ? ttl : 60));\n                if (published_at) {\n        \
+    \            msg.setTimestamp(moment(published_at).toDate());\n              \
+    \  }\n            }\n            catch (ex) {\n                logger.error(\"\
+    onCoreHeard - \" + ex);\n            }\n            return msg;\n        };\n\n\
+    \        var msgName = (isPublic) ? \"PublicEvent\" : \"PrivateEvent\";\n    \
+    \    var userID = (this.userID || \"\").toLowerCase() + \"/\";\n        name =\
+    \ (name) ? name.toString() : name;\n        if (name && name.indexOf && (name.indexOf(userID)\
+    \ == 0)) {\n            name = name.substring(userID.length);\n        }\n\n \
+    \       data = (data) ? data.toString() : data;\n        this.sendNONTypeMessage(msgName,\
+    \ { event_name: name, _raw: rawFn }, data);\n    }"
+  - "def get_queryset(self):\n        '''\n        If MultiTenantMiddleware is used,\
+    \ filter queryset by request.site_id\n        '''\n        queryset = super(PageList,\
+    \ self).get_queryset()\n        if hasattr(self.request, 'site_id'):\n       \
+    \     queryset = queryset.filter(site_id=self.request.site_id)\n        return\
+    \ queryset"
+datasets:
+- benjamintli/code-retrieval-combined-v2
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: SentenceTransformer based on answerdotai/ModernBERT-base
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: eval
+      type: eval
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.873
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.9366666666666666
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.9543333333333334
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.973
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.873
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.31222222222222223
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.19086666666666663
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.0973
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.873
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.9366666666666666
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.9543333333333334
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.973
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9240732170821061
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9082900793650796
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9093847853022148
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on answerdotai/ModernBERT-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [code-retrieval-combined-v2](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2) dataset. 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:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
+- **Maximum Sequence Length:** 1024 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [code-retrieval-combined-v2](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2)
+<!-- - **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': 1024, 'do_lower_case': False, 'architecture': 'OptimizedModule'})
+  (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})
+)
+```
+
+## 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("modernbert-code-v2")
+# Run inference
+queries = [
+    "If MultiTenantMiddleware is used, filter queryset by request.site_id",
+]
+documents = [
+    "def get_queryset(self):\n        '''\n        If MultiTenantMiddleware is used, filter queryset by request.site_id\n        '''\n        queryset = super(PageList, self).get_queryset()\n        if hasattr(self.request, 'site_id'):\n            queryset = queryset.filter(site_id=self.request.site_id)\n        return queryset",
+    'def reduce_ticks(ax, which, maxticks=3):\n    """Given a pyplot axis, resamples its `which`-axis ticks such that are at most\n    `maxticks` left.\n\n    Parameters\n    ----------\n    ax : axis\n        The axis to adjust.\n    which : {\'x\' | \'y\'}\n        Which axis to adjust.\n    maxticks : {3, int}\n        Maximum number of ticks to use.\n\n    Returns\n    -------\n    array\n        An array of the selected ticks.\n    """\n    ticks = getattr(ax, \'get_{}ticks\'.format(which))()\n    if len(ticks) > maxticks:\n        # make sure the left/right value is not at the edge\n        minax, maxax = getattr(ax, \'get_{}lim\'.format(which))()\n        dw = abs(maxax-minax)/10.\n        start_idx, end_idx = 0, len(ticks)\n        if ticks[0] < minax + dw:\n            start_idx += 1\n        if ticks[-1] > maxax - dw:\n            end_idx -= 1\n        # get reduction factor\n        fac = int(len(ticks) / maxticks)\n        ticks = ticks[start_idx:end_idx:fac]\n    return ticks',
+    'function (isPublic, name, data, ttl, published_at, coreid) {\n        var rawFn = function (msg) {\n            try {\n                msg.setMaxAge(parseInt((ttl && (ttl >= 0)) ? ttl : 60));\n                if (published_at) {\n                    msg.setTimestamp(moment(published_at).toDate());\n                }\n            }\n            catch (ex) {\n                logger.error("onCoreHeard - " + ex);\n            }\n            return msg;\n        };\n\n        var msgName = (isPublic) ? "PublicEvent" : "PrivateEvent";\n        var userID = (this.userID || "").toLowerCase() + "/";\n        name = (name) ? name.toString() : name;\n        if (name && name.indexOf && (name.indexOf(userID) == 0)) {\n            name = name.substring(userID.length);\n        }\n\n        data = (data) ? data.toString() : data;\n        this.sendNONTypeMessage(msgName, { event_name: name, _raw: rawFn }, data);\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([[ 0.9183, -0.0231, -0.0561]])
+```
+
+<!--
+### 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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Dataset: `eval`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.873      |
+| cosine_accuracy@3   | 0.9367     |
+| cosine_accuracy@5   | 0.9543     |
+| cosine_accuracy@10  | 0.973      |
+| cosine_precision@1  | 0.873      |
+| cosine_precision@3  | 0.3122     |
+| cosine_precision@5  | 0.1909     |
+| cosine_precision@10 | 0.0973     |
+| cosine_recall@1     | 0.873      |
+| cosine_recall@3     | 0.9367     |
+| cosine_recall@5     | 0.9543     |
+| cosine_recall@10    | 0.973      |
+| **cosine_ndcg@10**  | **0.9241** |
+| cosine_mrr@10       | 0.9083     |
+| cosine_map@100      | 0.9094     |
+
+<!--
+## 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
+
+#### code-retrieval-combined-v2
+
+* Dataset: [code-retrieval-combined-v2](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2) at [2b971a6](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2/tree/2b971a6d597823ab7ff10b898ae6f3c0fdbbfa23)
+* Size: 283,621 training samples
+* Columns: <code>query</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                              | positive                                                                             |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 44.94 tokens</li><li>max: 856 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 181.2 tokens</li><li>max: 1024 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                                                                                                                                                                              | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Start the asyncio event loop and runs the application.</code>                                                                                                                                                                                                                                | <code>def main():<br>    """Start the asyncio event loop and runs the application."""<br>    # Helper method so that the coroutine exits cleanly if an exception<br>    # happens (which would leave resources dangling)<br>    async def _run_application(loop):<br>        try:<br>            return await cli_handler(loop)<br><br>        except KeyboardInterrupt:<br>            pass  # User pressed Ctrl+C, just ignore it<br><br>        except SystemExit:<br>            pass  # sys.exit() was used - do nothing<br><br>        except:  # pylint: disable=bare-except  # noqa<br>            import traceback<br><br>            traceback.print_exc(file=sys.stderr)<br>            sys.stderr.writelines(<br>                '\n>>> An error occurred, full stack trace above\n')<br><br>        return 1<br><br>    try:<br>        loop = asyncio.get_event_loop()<br>        return loop.run_until_complete(_run_application(loop))<br>    except KeyboardInterrupt:<br>        pass<br><br>    return 1</code>                                                                                               |
+  | <code>Initialize the pool manager with the number of pools, the entry sizes for each<br>pool, and the maximum depth of the free pool.<br><br>@param bufferEntrySizes the memory sizes of each entry in the pools<br>@param bufferEntryDepths the maximum number of entries in the free pool</code> | <code>public void initialize(int[] bufferEntrySizes, int[] bufferEntryDepths) {<br>        if (TraceComponent.isAnyTracingEnabled() && tc.isEntryEnabled()) {<br>            Tr.entry(tc, "initialize");<br>        }<br><br>        // order both lists from smallest to largest, based only on Entry Sizes<br>        int len = bufferEntrySizes.length;<br>        int[] bSizes = new int[len];<br>        int[] bDepths = new int[len];<br>        int sizeCompare;<br>        int depth;<br>        int sizeSort;<br>        int j;<br><br>        for (int i = 0; i < len; i++) {<br>            sizeCompare = bufferEntrySizes[i];<br>            depth = bufferEntryDepths[i];<br>            // go backwards, for speed, since first Array List is<br>            // probably already ordered small to large<br>            for (j = i - 1; j >= 0; j--) {<br>                sizeSort = bSizes[j];<br>                if (sizeCompare > sizeSort) {<br>                    // add the bigger one after the smaller one<br>                    bSizes[j + 1] = sizeCompare;<br>                    bDepths[j ...</code> |
+  | <code>// List lists all of the documents in an index. The documents are returned in<br>// increasing ID order.</code>                                                                                                                                                                              | <code>func (x *Index) List(c context.Context, opts *ListOptions) *Iterator {<br>	t := &Iterator{<br>		c:             c,<br>		index:         x,<br>		count:         -1,<br>		listInclusive: true,<br>		more:          moreList,<br>		limit:         -1,<br>	}<br>	if opts != nil {<br>		t.listStartID = opts.StartID<br>		if opts.Limit > 0 {<br>			t.limit = opts.Limit<br>		}<br>		t.idsOnly = opts.IDsOnly<br>	}<br>	return t<br>}</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 128,
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Evaluation Dataset
+
+#### code-retrieval-combined-v2
+
+* Dataset: [code-retrieval-combined-v2](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2) at [2b971a6](https://huggingface.co/datasets/benjamintli/code-retrieval-combined-v2/tree/2b971a6d597823ab7ff10b898ae6f3c0fdbbfa23)
+* Size: 31,516 evaluation samples
+* Columns: <code>query</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                              | positive                                                                              |
+  |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                                |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 42.73 tokens</li><li>max: 834 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 180.42 tokens</li><li>max: 1024 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>This gets the version of OpenALPR<br><br>        :return: Version information</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                         | <code>def get_version(self):<br>        """<br>        This gets the version of OpenALPR<br><br>        :return: Version information<br>        """<br><br>        ptr = self._get_version_func(self.alpr_pointer)<br>        version_number = ctypes.cast(ptr, ctypes.c_char_p).value<br>        version_number = _convert_from_charp(version_number)<br>        self._free_json_mem_func(ctypes.c_void_p(ptr))<br>        return version_number</code> |
+  | <code>Remove all unnecessary comments from a lexer or parser file</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | <code>public String stripUnnecessaryComments(String javaContent, AntlrOptions options) {<br>		if (!options.isOptimizeCodeQuality()) {<br>			return javaContent;<br>		}<br>		javaContent = stripMachineDependentPaths(javaContent);<br>		if (options.isStripAllComments()) {<br>			javaContent = stripAllComments(javaContent);<br>		}<br>		return javaContent;<br>	}</code>                                                                              |
+  | <code>Serialize reply to array or JSON.<br><br>@param {Object}        packet<br>@param {String}        packet.method "get", "search", "post", "put", "delete", "sub", "unsub".<br>@param {String}        packet.resource<br>@param {String}        packet.id<br>@param {*}             packet.body<br>@param {Number}        [packet.status]<br>@param {Number\|String} [packet.date]<br>@param {Object}        [packet.headers]<br>@param {Boolean}       [json] true to generate JSON instead of array.<br>@returns {Array\|String\|null}</code> | <code>function reply(packet, json) {<br>  return _create(packet, packet.status \|\| 500, (METHODS[packet.method] \|\| '') + packet.resource, json);<br>}</code>                                                                                                                                                                                                                                                                                          |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 128,
+      "gather_across_devices": false,
+      "directions": [
+          "query_to_doc"
+      ],
+      "partition_mode": "joint",
+      "hardness_mode": null,
+      "hardness_strength": 0.0
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 1024
+- `per_device_eval_batch_size`: 1024
+- `learning_rate`: 8e-05
+- `num_train_epochs`: 1
+- `warmup_steps`: 0.05
+- `bf16`: True
+- `dataloader_num_workers`: 4
+- `load_best_model_at_end`: True
+- `push_to_hub`: True
+- `hub_model_id`: modernbert-code-v2
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 1024
+- `per_device_eval_batch_size`: 1024
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 8e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: None
+- `warmup_ratio`: None
+- `warmup_steps`: 0.05
+- `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`: True
+- `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`: 4
+- `dataloader_prefetch_factor`: None
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `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`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: modernbert-code-v2
+- `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`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch      | Step    | Training Loss | Validation Loss | eval_cosine_ndcg@10 |
+|:----------:|:-------:|:-------------:|:---------------:|:-------------------:|
+| 0.0722     | 20      | 3.9983        | 1.3745          | 0.7545              |
+| 0.1444     | 40      | 1.0297        | 0.7864          | 0.8493              |
+| 0.2166     | 60      | 0.6830        | 0.5917          | 0.8833              |
+| 0.2888     | 80      | 0.5476        | 0.5128          | 0.8973              |
+| 0.3610     | 100     | 0.4891        | 0.4641          | 0.9028              |
+| 0.4332     | 120     | 0.4436        | 0.4370          | 0.9098              |
+| 0.5054     | 140     | 0.4304        | 0.4151          | 0.9154              |
+| 0.5776     | 160     | 0.4101        | 0.3948          | 0.9161              |
+| 0.6498     | 180     | 0.3910        | 0.3829          | 0.9190              |
+| 0.7220     | 200     | 0.3794        | 0.3729          | 0.9188              |
+| 0.7942     | 220     | 0.3668        | 0.3650          | 0.9207              |
+| 0.8664     | 240     | 0.3683        | 0.3573          | 0.9230              |
+| **0.9386** | **260** | **0.359**     | **0.3534**      | **0.9241**          |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.12.12
+- Sentence Transformers: 5.3.0
+- Transformers: 5.0.0
+- PyTorch: 2.10.0+cu128
+- Accelerate: 1.13.0
+- Datasets: 4.0.0
+- 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",
+}
+```
+
+#### CachedMultipleNegativesRankingLoss
+```bibtex
+@misc{gao2021scaling,
+    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
+    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
+    year={2021},
+    eprint={2101.06983},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## 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_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
+    "sentence_transformers": "5.3.0",
+    "transformers": "5.0.0",
+    "pytorch": "2.10.0+cu128"
+  },
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 1024,
+    "do_lower_case": false
+}