--- 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 \ \ $min = preg_replace(['/[\\n\\r]/', '/\\>[^\\S ]+/s', '/[^\\S ]+\\', '<', '\\\\1'], trim($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) - **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) ### 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]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `eval` * Evaluated with [InformationRetrievalEvaluator](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 | ## 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: query and positive * Approximate statistics based on the first 1000 samples: | | query | positive | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | positive | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Start the asyncio event loop and runs the application. | def main():
"""Start the asyncio event loop and runs the application."""
# Helper method so that the coroutine exits cleanly if an exception
# happens (which would leave resources dangling)
async def _run_application(loop):
try:
return await cli_handler(loop)

except KeyboardInterrupt:
pass # User pressed Ctrl+C, just ignore it

except SystemExit:
pass # sys.exit() was used - do nothing

except: # pylint: disable=bare-except # noqa
import traceback

traceback.print_exc(file=sys.stderr)
sys.stderr.writelines(
'\n>>> An error occurred, full stack trace above\n')

return 1

try:
loop = asyncio.get_event_loop()
return loop.run_until_complete(_run_application(loop))
except KeyboardInterrupt:
pass

return 1 | | Initialize the pool manager with the number of pools, the entry sizes for each
pool, and the maximum depth of the free pool.

@param bufferEntrySizes the memory sizes of each entry in the pools
@param bufferEntryDepths the maximum number of entries in the free pool | public void initialize(int[] bufferEntrySizes, int[] bufferEntryDepths) {
if (TraceComponent.isAnyTracingEnabled() && tc.isEntryEnabled()) {
Tr.entry(tc, "initialize");
}

// order both lists from smallest to largest, based only on Entry Sizes
int len = bufferEntrySizes.length;
int[] bSizes = new int[len];
int[] bDepths = new int[len];
int sizeCompare;
int depth;
int sizeSort;
int j;

for (int i = 0; i < len; i++) {
sizeCompare = bufferEntrySizes[i];
depth = bufferEntryDepths[i];
// go backwards, for speed, since first Array List is
// probably already ordered small to large
for (j = i - 1; j >= 0; j--) {
sizeSort = bSizes[j];
if (sizeCompare > sizeSort) {
// add the bigger one after the smaller one
bSizes[j + 1] = sizeCompare;
bDepths[j ... | | // List lists all of the documents in an index. The documents are returned in
// increasing ID order. | func (x *Index) List(c context.Context, opts *ListOptions) *Iterator {
t := &Iterator{
c: c,
index: x,
count: -1,
listInclusive: true,
more: moreList,
limit: -1,
}
if opts != nil {
t.listStartID = opts.StartID
if opts.Limit > 0 {
t.limit = opts.Limit
}
t.idsOnly = opts.IDsOnly
}
return t
} | * Loss: [CachedMultipleNegativesRankingLoss](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: query and positive * Approximate statistics based on the first 1000 samples: | | query | positive | |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | positive | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | This gets the version of OpenALPR

:return: Version information | def get_version(self):
"""
This gets the version of OpenALPR

:return: Version information
"""

ptr = self._get_version_func(self.alpr_pointer)
version_number = ctypes.cast(ptr, ctypes.c_char_p).value
version_number = _convert_from_charp(version_number)
self._free_json_mem_func(ctypes.c_void_p(ptr))
return version_number | | Remove all unnecessary comments from a lexer or parser file | public String stripUnnecessaryComments(String javaContent, AntlrOptions options) {
if (!options.isOptimizeCodeQuality()) {
return javaContent;
}
javaContent = stripMachineDependentPaths(javaContent);
if (options.isStripAllComments()) {
javaContent = stripAllComments(javaContent);
}
return javaContent;
} | | Serialize reply to array or JSON.

@param {Object} packet
@param {String} packet.method "get", "search", "post", "put", "delete", "sub", "unsub".
@param {String} packet.resource
@param {String} packet.id
@param {*} packet.body
@param {Number} [packet.status]
@param {Number\|String} [packet.date]
@param {Object} [packet.headers]
@param {Boolean} [json] true to generate JSON instead of array.
@returns {Array\|String\|null} | function reply(packet, json) {
return _create(packet, packet.status \|\| 500, (METHODS[packet.method] \|\| '') + packet.resource, json);
} | * Loss: [CachedMultipleNegativesRankingLoss](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
Click to expand - `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`: {}
### 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} } ```