Upload ModernBERT model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,621 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:2022217
+- loss:MultipleNegativesRankingLoss
+base_model: Shuu12121/CodeModernBERT-Crow
+widget:
+- source_sentence: 'Clone value to a new instance
+
+
+    @private
+
+    @param {*} val
+
+    @returns {*}'
+  sentences:
+  - "function _copy(val) {\n    const type = $type(val);\n\n    if (type == 'object')\
+    \ {\n        val = _extend({}, val, true);\n    } else if (type == 'array') {\n\
+    \        val = val.slice(0);\n    }\n\n    return val;\n}"
+  - "function (data) {\n    data = data || {};\n\n    this.category = data.hasOwnProperty('category')\
+    \ ? data.category : 'No category';\n    this.id = data.hasOwnProperty('id') ?\
+    \ data.id : '';\n    this.group = data.hasOwnProperty('group') ? data.group :\
+    \ '';\n    this.lines = data.hasOwnProperty('lines') ? data.lines : 0;\n    this.name\
+    \ = data.hasOwnProperty('name') ? data.name : '';\n    this.options = data.hasOwnProperty('options')\
+    \ ? data.options : {};\n    this.origin = data.hasOwnProperty('origin') ? data.origin\
+    \ : '';\n    this.resources = data.hasOwnProperty('resources') ? data.resources\
+    \ : Object.assign({}, getDefaultOptions().resources);\n    this.usage = data.hasOwnProperty('usage')\
+    \ ? data.usage : [];\n    this.viewId = data.hasOwnProperty('viewId') ? data.viewId\
+    \ : '';\n}"
+  - "public function relativeMove($model, $position)\n    {\n        $conditionAttributes\
+    \ = (array)$this->conditionAttributes;\n        $owner = $this->owner;\n\n   \
+    \     if (!empty($conditionAttributes)) {\n            $sameCondition = true;\n\
+    \            foreach ($conditionAttributes as $attr) {\n                if ($owner->getAttribute($attr)\
+    \ != $model->getAttribute($attr)) {\n                    $sameCondition = false;\n\
+    \                    break;\n                }\n            }\n            if\
+    \ (!$sameCondition) {\n                // move in other condition category\n \
+    \               $this->moveToTop();\n                // update condition attribute\n\
+    \                $condition = [];\n                foreach ($conditionAttributes\
+    \ as $attr) {\n                    $condition[$attr] = $model->getAttribute($attr);\n\
+    \                }\n                $condition[$this->sortAttribute] = $owner->find()->andWhere($this->getCondition())->count()\
+    \ - 1;\n                $owner->updateAttributes($condition);\n            }\n\
+    \        }\n        // calculate pos change\n        $currentPos = $owner->getAttribute($this->sortAttribute);\n\
+    \        $destinationPos = $model->getAttribute($this->sortAttribute);\n     \
+    \   if ($position == 'after') {\n            $newPos = $destinationPos > $currentPos\
+    \ ? $destinationPos - 1 : $destinationPos;\n        } else {\n            $newPos\
+    \ = $destinationPos > $currentPos ? $destinationPos : $destinationPos + 1;\n \
+    \       }\n        $this->moveToPosition($newPos);\n    }"
+- source_sentence: '/*
+
+    Realize an asynchronous squeue command on slurm according a parameter (or not).
+
+    Data are formated into a literal.
+
+    @paramSqueue {string} optional. For example : '' -o "%j %i" '' // not implemented
+    yet'
+  sentences:
+  - "function(paramSqueue) {\n    if (!paramSqueue) paramSqueue = '';\n    paramSqueue\
+    \ = ''; // to remove when it will be take into account in the implementation\n\
+    \    var emitter = new events.EventEmitter();\n    var squeueRes_dict = {\n  \
+    \      'id': [],\n        'partition': [],\n        'nameUUID': [],\n        'status':\
+    \ []\n    }\n\n    // squeue command\n    var exec_cmd = require('child_process').exec;\n\
+    \    exec_cmd(queueBinary + '  -o \\\"\\%i \\%P \\%j \\%t\\\" ' + paramSqueue,\
+    \ function(err, stdout, stderr) {\n        if (err) {\n            emitter.emit('listError',\
+    \ err);\n            return;\n        }\n        var squeueRes_str = ('' + stdout).replace(/\\\
+    \"/g, ''); // squeue results\n        squeueRes_str.split('\\n')\n           \
+    \ .filter(function(jobArray, i) {\n                return jobArray.length > 0\
+    \ && i > 0;\n            })\n            .map(function(jobLine, i) { // for each\
+    \ job\n                return test = jobLine.split(' ').filter(function(val) {\n\
+    \                    return val != ''; // keep values that are not empty\n   \
+    \             });\n            })\n            .map(function(jobArray, i) { //\
+    \ save each field in the corresponding array of dict\n                squeueRes_dict.id.push(jobArray[0]);\
+    \ // job ID gived by slurm\n                squeueRes_dict.partition.push(jobArray[1]);\
+    \ // gpu, cpu, etc.\n                squeueRes_dict.nameUUID.push(jobArray[2]);\
+    \ // unique job ID gived by Nslurm (uuid)\n                squeueRes_dict.status.push(jobArray[3]);\
+    \ // P, R, CF, CG, etc.\n            });\n        emitter.emit('data', squeueRes_dict);\n\
+    \    });\n    return emitter;\n}"
+  - "function(node, state, leaving) {\n        for (let i = 0; i < state.currentSegments.length;\
+    \ ++i) {\n            const segInternal = state.currentSegments[i].internal;\n\
+    \n            if (leaving) {\n                segInternal.exitNodes.push(node);\n\
+    \            } else {\n                segInternal.nodes.push(node);\n       \
+    \     }\n        }\n\n        debug([\n            `${state.currentSegments.map(getId).join(\"\
+    ,\")})`,\n            `${node.type}${leaving ? \":exit\" : \"\"}`\n        ].join(\"\
+    \ \"));\n    }"
+  - "private void checkForGenerator(final Class<?> clazz, Field field, GeneratedValue\
+    \ generatedValue, String schemaName)\n\n    {\n\n        TableGenerator tableGenerator\
+    \ = field.getAnnotation(TableGenerator.class);\n        SequenceGenerator sequenceGenerator\
+    \ = field.getAnnotation(SequenceGenerator.class);\n        if (tableGenerator\
+    \ == null || !tableGenerator.name().equals(generatedValue.generator()))\n    \
+    \    {\n            tableGenerator = clazz.getAnnotation(TableGenerator.class);\n\
+    \        }\n        if (sequenceGenerator == null || !sequenceGenerator.name().equals(generatedValue.generator()))\n\
+    \        {\n            sequenceGenerator = clazz.getAnnotation(SequenceGenerator.class);\n\
+    \        }\n\n        if ((tableGenerator == null && sequenceGenerator == null)\n\
+    \                || (tableGenerator != null && !tableGenerator.name().equals(generatedValue.generator()))\n\
+    \                || (sequenceGenerator != null && !sequenceGenerator.name().equals(generatedValue.generator())))\n\
+    \        {\n\n            throw new RuleValidationException(\"Unknown Id.generator:\
+    \ \" + generatedValue.generator());\n\n        }\n        else if ((tableGenerator\
+    \ != null && !tableGenerator.schema().isEmpty() && !tableGenerator.schema().equals(\n\
+    \                schemaName))\n                || (sequenceGenerator != null &&\
+    \ !sequenceGenerator.schema().isEmpty() && !sequenceGenerator.schema()\n     \
+    \                   .equals(schemaName)))\n        {\n\n            throw new\
+    \ RuleValidationException(\"Generator \" + generatedValue.generator() + \" in\
+    \ entity : \"\n                    + clazz.getName() + \" has different schema\
+    \ name ,it should be same as entity have\");\n\n        }\n\n    }"
+- source_sentence: '@param      $param1
+
+    @param null $param2
+
+    @param null $param3
+
+
+    @return array|int|mixed
+
+    @throws DbException'
+  sentences:
+  - "func (s *GetReservationUtilizationOutput) SetUtilizationsByTime(v []*UtilizationByTime)\
+    \ *GetReservationUtilizationOutput {\n\ts.UtilizationsByTime = v\n\treturn s\n\
+    }"
+  - "public static function ask($param1, $param2 = null, $param3 = null) {\n     \
+    \   self::init();\n        if(is_array($param1)) {\n            return self::smartSelect($param1,\
+    \ $param2, $param3);\n        } else {\n            switch(substr($param1, 0,\
+    \ 1)) {\n                case '>':\n                case '/':\n              \
+    \      return self::smartQuery($param1, $param2);\n                    break;\n\
+    \                case '?':\n                    return self::smartSelect(substr($param1,\
+    \ 1), $param2, $param3);\n                    break;\n                default:\n\
+    \                    if(is_array($param3)) {\n                        return self::smartUpdate($param1,\
+    \ $param2, $param3);\n                    } else {\n                        return\
+    \ self::smartInsert($param1, $param2);\n                    }\n            }\n\
+    \        }\n    }"
+  - "void printStates() {\n           int     c;    // input \"character\"\n     \
+    \      int     n;    // state number\n\n           System.out.print(\"state |\
+    \           i n p u t     s y m b o l s \\n\");\n           System.out.print(\"\
+    \      | Acc  LA    Tag\");\n           for (c=0; c<fRB.fSetBuilder.getNumCharCategories();\
+    \ c++) {\n               RBBINode.printInt(c, 3);\n           }\n           System.out.print(\"\
+    \\n\");\n           System.out.print(\"      |---------------\");\n          \
+    \ for (c=0; c<fRB.fSetBuilder.getNumCharCategories(); c++) {\n               System.out.print(\"\
+    ---\");\n           }\n           System.out.print(\"\\n\");\n\n           for\
+    \ (n=0; n<fDStates.size(); n++) {\n               RBBIStateDescriptor sd = fDStates.get(n);\n\
+    \               RBBINode.printInt(n, 5);\n               System.out.print(\" |\
+    \ \");\n\n               RBBINode.printInt(sd.fAccepting, 3);\n              \
+    \ RBBINode.printInt(sd.fLookAhead, 4);\n               RBBINode.printInt(sd.fTagsIdx,\
+    \ 6);\n               System.out.print(\" \");\n               for (c=0; c<fRB.fSetBuilder.getNumCharCategories();\
+    \ c++) {\n                   RBBINode.printInt(sd.fDtran[c], 3);\n           \
+    \    }\n               System.out.print(\"\\n\");\n           }\n           System.out.print(\"\
+    \\n\\n\");\n       }"
+- source_sentence: 'Performs a forward, allowing page-relative paths and setting all
+    values
+
+    compatible with &lt;ao:forward&gt; tag.
+
+
+    @param  args  The arguments for the page, make unmodifiable and accessible as
+    request-scope var "arg"
+
+
+    @see #forward(java.lang.String, javax.servlet.RequestDispatcher, javax.servlet.http.HttpServletRequest,
+    javax.servlet.http.HttpServletResponse, java.util.Map)'
+  sentences:
+  - "public static void forward(\n\t\tServletContext servletContext,\n\t\tString page,\n\
+    \t\tHttpServletRequest request,\n\t\tHttpServletResponse response,\n\t\tMap<String,?>\
+    \ args\n\t) throws ServletException, IOException {\n\t\t// Resolve the dispatcher\n\
+    \t\tString contextRelativePath = ServletUtil.getAbsolutePath(getCurrentPagePath(request),\
+    \ page);\n\t\tRequestDispatcher dispatcher = servletContext.getRequestDispatcher(contextRelativePath);\n\
+    \t\tif(dispatcher==null) throw new LocalizedServletException(accessor, \"Dispatcher.dispatcherNotFound\"\
+    , contextRelativePath);\n\t\tforward(contextRelativePath, dispatcher, request,\
+    \ response, args);\n\t}"
+  - "public function getConnection($name = ''): QueryBuilderInterface\n\t{\n\t\t//\
+    \ If the parameter is a string, use it as an array index\n\t\tif (is_scalar($name)\
+    \ && isset($this->connections[$name]))\n\t\t{\n\t\t\treturn $this->connections[$name];\n\
+    \t\t}\n\t\telse if (empty($name) && ! empty($this->connections)) // Otherwise,\
+    \ return the last one\n\t\t{\n\t\t\treturn end($this->connections);\n\t\t}\n\n\
+    \t\t// You should actually connect before trying to get a connection...\n\t\t\
+    throw new InvalidArgumentException('The specified connection does not exist');\n\
+    \t}"
+  - "func (c *Context) Untrack(class, id string) error {\n\tfullID := payload.BuildID(class,\
+    \ id)\n\tlogger.Tracef(\"Calling untrack on payload context %q\", fullID)\n\n\t\
+    res, err := c.api.Untrack(fullID)\n\tif err != nil {\n\t\treturn errors.Trace(err)\n\
+    \t}\n\t// TODO(ericsnow) We should not ignore a 0-len result.\n\tif len(res) >\
+    \ 0 && res[0].Error != nil {\n\t\treturn errors.Trace(res[0].Error)\n\t}\n\tdelete(c.payloads,\
+    \ id)\n\n\treturn nil\n}"
+- source_sentence: /* PRIVATE
+  sentences:
+  - "void activate(ProtocolVersion helloVersion) throws IOException {\n        if\
+    \ (activeProtocols == null) {\n            activeProtocols = getActiveProtocols();\n\
+    \        }\n\n        if (activeProtocols.collection().isEmpty() ||\n        \
+    \        activeProtocols.max.v == ProtocolVersion.NONE.v) {\n            throw\
+    \ new SSLHandshakeException(\n                    \"No appropriate protocol (protocol\
+    \ is disabled or \" +\n                    \"cipher suites are inappropriate)\"\
+    );\n        }\n\n        if (activeCipherSuites == null) {\n            activeCipherSuites\
+    \ = getActiveCipherSuites();\n        }\n\n        if (activeCipherSuites.collection().isEmpty())\
+    \ {\n            throw new SSLHandshakeException(\"No appropriate cipher suite\"\
+    );\n        }\n\n        // temporary protocol version until the actual protocol\
+    \ version\n        // is negotiated in the Hello exchange. This affects the record\n\
+    \        // version we sent with the ClientHello.\n        if (!isInitialHandshake)\
+    \ {\n            protocolVersion = activeProtocolVersion;\n        } else {\n\
+    \            protocolVersion = activeProtocols.max;\n        }\n\n        if (helloVersion\
+    \ == null || helloVersion.v == ProtocolVersion.NONE.v) {\n            helloVersion\
+    \ = activeProtocols.helloVersion;\n        }\n\n        // We accumulate digests\
+    \ of the handshake messages so that\n        // we can read/write CertificateVerify\
+    \ and Finished messages,\n        // getting assurance against some particular\
+    \ active attacks.\n        Set<String> localSupportedHashAlgorithms =\n      \
+    \      SignatureAndHashAlgorithm.getHashAlgorithmNames(\n                getLocalSupportedSignAlgs());\n\
+    \        handshakeHash = new HandshakeHash(!isClient, needCertVerify,\n      \
+    \      localSupportedHashAlgorithms);\n\n        // Generate handshake input/output\
+    \ stream.\n        input = new HandshakeInStream(handshakeHash);\n        if (conn\
+    \ != null) {\n            output = new HandshakeOutStream(protocolVersion, helloVersion,\n\
+    \                                        handshakeHash, conn);\n            conn.getAppInputStream().r.setHandshakeHash(handshakeHash);\n\
+    \            conn.getAppInputStream().r.setHelloVersion(helloVersion);\n     \
+    \       conn.getAppOutputStream().r.setHelloVersion(helloVersion);\n        }\
+    \ else {\n            output = new HandshakeOutStream(protocolVersion, helloVersion,\n\
+    \                                        handshakeHash, engine);\n           \
+    \ engine.inputRecord.setHandshakeHash(handshakeHash);\n            engine.inputRecord.setHelloVersion(helloVersion);\n\
+    \            engine.outputRecord.setHelloVersion(helloVersion);\n        }\n\n\
+    \        // move state to activated\n        state = -1;\n    }"
+  - "function _error(coreIndex, cores) {\n  var errMsg =\n    '[cpu-stats] Error:\
+    \ Core \"' + coreIndex + '\" not found, use one of ' +\n    '[0, ' + (cores -\
+    \ 1) + '], ' +\n    'since your system has a total of ' + cores + ' cores.';\n\
+    \  console.log(errMsg);\n}"
+  - "function _drawLine(v0, v1, color) {\n        var p = new Primitive();\n\n   \
+    \     p.vertices = [v0, v1];\n        p.color = toColor(color);\n\n        renderer.addPrimitive(p);\n\
+    \    }"
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on Shuu12121/CodeModernBERT-Crow
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Shuu12121/CodeModernBERT-Crow](https://huggingface.co/Shuu12121/CodeModernBERT-Crow). 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-Crow](https://huggingface.co/Shuu12121/CodeModernBERT-Crow) <!-- at revision fa8af97f22c9bf631506435c994e724f82747e67 -->
+- **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 = [
+    '/* PRIVATE',
+    'function _error(coreIndex, cores) {\n  var errMsg =\n    \'[cpu-stats] Error: Core "\' + coreIndex + \'" not found, use one of \' +\n    \'[0, \' + (cores - 1) + \'], \' +\n    \'since your system has a total of \' + cores + \' cores.\';\n  console.log(errMsg);\n}',
+    'function _drawLine(v0, v1, color) {\n        var p = new Primitive();\n\n        p.vertices = [v0, v1];\n        p.color = toColor(color);\n\n        renderer.addPrimitive(p);\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
+
+*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: 2,022,217 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: 3 tokens</li><li>mean: 48.56 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 171.79 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>// GetNodeID returns the NodeID field if it's non-nil, zero value otherwise.</code> | <code>func (a *App) GetNodeID() string {<br>	if a == nil || a.NodeID == nil {<br>		return ""<br>	}<br>	return *a.NodeID<br>}</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | <code>1.0</code> |
+  | <code>// _NET_WM_STRUT_PARTIAL set</code>                                                 | <code>func WmStrutPartialSet(xu *xgbutil.XUtil, win xproto.Window,<br>	struts *WmStrutPartial) error {<br><br>	rawStruts := make([]uint, 12)<br>	rawStruts[0] = struts.Left<br>	rawStruts[1] = struts.Right<br>	rawStruts[2] = struts.Top<br>	rawStruts[3] = struts.Bottom<br>	rawStruts[4] = struts.LeftStartY<br>	rawStruts[5] = struts.LeftEndY<br>	rawStruts[6] = struts.RightStartY<br>	rawStruts[7] = struts.RightEndY<br>	rawStruts[8] = struts.TopStartX<br>	rawStruts[9] = struts.TopEndX<br>	rawStruts[10] = struts.BottomStartX<br>	rawStruts[11] = struts.BottomEndX<br><br>	return xprop.ChangeProp32(xu, win, "_NET_WM_STRUT_PARTIAL", "CARDINAL",<br>		rawStruts...)<br>}</code> | <code>1.0</code> |
+  | <code>//GetQyAccessToken 获取access_token</code>                                            | <code>func (ctx *Context) GetQyAccessToken() (accessToken string, err error) {<br>	ctx.accessTokenLock.Lock()<br>	defer ctx.accessTokenLock.Unlock()<br><br>	accessTokenCacheKey := fmt.Sprintf("qy_access_token_%s", ctx.AppID)<br>	val := ctx.Cache.Get(accessTokenCacheKey)<br>	if val != nil {<br>		accessToken = val.(string)<br>		return<br>	}<br><br>	//从微信服务器获取<br>	var resQyAccessToken ResQyAccessToken<br>	resQyAccessToken, err = ctx.GetQyAccessTokenFromServer()<br>	if err != nil {<br>		return<br>	}<br><br>	accessToken = resQyAccessToken.AccessToken<br>	return<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"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `num_train_epochs`: 5
+- `fp16`: True
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 256
+- `per_device_eval_batch_size`: 256
+- `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`: 5
+- `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}
+- `tp_size`: 0
+- `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
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss |
+|:------:|:-----:|:-------------:|
+| 0.0633 | 500   | 0.8015        |
+| 0.1266 | 1000  | 0.1036        |
+| 0.1899 | 1500  | 0.0973        |
+| 0.2532 | 2000  | 0.0921        |
+| 0.3165 | 2500  | 0.0876        |
+| 0.3797 | 3000  | 0.0861        |
+| 0.4430 | 3500  | 0.0843        |
+| 0.5063 | 4000  | 0.0841        |
+| 0.5696 | 4500  | 0.0788        |
+| 0.6329 | 5000  | 0.0794        |
+| 0.6962 | 5500  | 0.0782        |
+| 0.7595 | 6000  | 0.077         |
+| 0.8228 | 6500  | 0.0749        |
+| 0.8861 | 7000  | 0.0749        |
+| 0.9494 | 7500  | 0.0724        |
+| 1.0127 | 8000  | 0.0658        |
+| 1.0759 | 8500  | 0.0385        |
+| 1.1392 | 9000  | 0.0381        |
+| 1.2025 | 9500  | 0.0383        |
+| 1.2658 | 10000 | 0.0381        |
+| 1.3291 | 10500 | 0.0382        |
+| 1.3924 | 11000 | 0.0384        |
+| 1.4557 | 11500 | 0.0384        |
+| 1.5190 | 12000 | 0.039         |
+| 1.5823 | 12500 | 0.0391        |
+| 1.6456 | 13000 | 0.0401        |
+| 1.7089 | 13500 | 0.0383        |
+| 1.7722 | 14000 | 0.0392        |
+| 1.8354 | 14500 | 0.0371        |
+| 1.8987 | 15000 | 0.0387        |
+| 1.9620 | 15500 | 0.0385        |
+| 2.0253 | 16000 | 0.0298        |
+| 2.0886 | 16500 | 0.0171        |
+| 2.1519 | 17000 | 0.0174        |
+| 2.2152 | 17500 | 0.0171        |
+| 2.2785 | 18000 | 0.0169        |
+| 2.3418 | 18500 | 0.0174        |
+| 2.4051 | 19000 | 0.0177        |
+| 2.4684 | 19500 | 0.0175        |
+| 2.5316 | 20000 | 0.0171        |
+| 2.5949 | 20500 | 0.017         |
+| 2.6582 | 21000 | 0.0172        |
+| 2.7215 | 21500 | 0.0178        |
+| 2.7848 | 22000 | 0.0167        |
+| 2.8481 | 22500 | 0.0176        |
+| 2.9114 | 23000 | 0.0175        |
+| 2.9747 | 23500 | 0.0178        |
+| 3.0380 | 24000 | 0.0129        |
+| 3.1013 | 24500 | 0.0099        |
+| 3.1646 | 25000 | 0.0097        |
+| 3.2278 | 25500 | 0.0097        |
+| 3.2911 | 26000 | 0.0101        |
+| 3.3544 | 26500 | 0.0098        |
+| 3.4177 | 27000 | 0.0099        |
+| 3.4810 | 27500 | 0.0096        |
+| 3.5443 | 28000 | 0.0095        |
+| 3.6076 | 28500 | 0.0094        |
+| 3.6709 | 29000 | 0.0097        |
+| 3.7342 | 29500 | 0.01          |
+| 3.7975 | 30000 | 0.0096        |
+| 3.8608 | 30500 | 0.0098        |
+| 3.9241 | 31000 | 0.0095        |
+| 3.9873 | 31500 | 0.0094        |
+| 4.0506 | 32000 | 0.0079        |
+| 4.1139 | 32500 | 0.0074        |
+| 4.1772 | 33000 | 0.0072        |
+| 4.2405 | 33500 | 0.0073        |
+| 4.3038 | 34000 | 0.0071        |
+| 4.3671 | 34500 | 0.0073        |
+| 4.4304 | 35000 | 0.007         |
+| 4.4937 | 35500 | 0.0072        |
+| 4.5570 | 36000 | 0.0071        |
+| 4.6203 | 36500 | 0.0071        |
+| 4.6835 | 37000 | 0.0072        |
+| 4.7468 | 37500 | 0.0072        |
+| 4.8101 | 38000 | 0.0069        |
+| 4.8734 | 38500 | 0.007         |
+| 4.9367 | 39000 | 0.007         |
+| 5.0    | 39500 | 0.007         |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.51.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.5.0
+- Tokenizers: 0.21.0
+
+## 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.*
+-->

added_tokens.json

@@ -0,0 +1,6 @@
+{
+  "</s>": 50001,
+  "<mask>": 50003,
+  "<s>": 50000,
+  "<unk>": 50002
+}

config.json

@@ -0,0 +1,48 @@
+{
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 50000,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "cls",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50001,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "hidden_activation": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "local_attention": 128,
+  "local_attention_rope_theta": 10000,
+  "local_attention_window": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "repad_logits_with_grad": false,
+  "rope_theta": 160000,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "vocab_size": 50004
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.51.3",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13ed3dfbdd7c3c6b2ec6ef6b78415c437fae28dd14d0eaf1111d5e5e10baba11
+size 606681112

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
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,97 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "3": {
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+      "lstrip": true,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": null,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}