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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}
}
```
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