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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:58800
- loss:MultipleNegativesRankingLoss
base_model: Shuu12121/CodeModernBERT-Finch
widget:
- source_sentence: 'Returns boolean indicating whether the requestUrl matches against
the paths configured.
@param requestedUrl - url requested by user
@param opts - unless configuration
@returns {boolean}'
sentences:
- "def xmoe2_v1_l4k_global_only():\n \"\"\"\"\"\"\n hparams = xmoe2_v1_l4k()\n\
\ hparams.decoder_layers = [\n \"att\" if l == \"local_att\" else l for\
\ l in hparams.decoder_layers]\n return hparams"
- "function matchesPath(requestedUrl, opts) {\n var paths = !opts.path || Array.isArray(opts.path)\
\ ?\n opts.path : [opts.path];\n\n if (paths) {\n return paths.some(function(p)\
\ {\n return (typeof p === 'string' && p === requestedUrl.pathname) ||\n\
\ (p instanceof RegExp && !! p.exec(requestedUrl.pathname));\n });\n\
\ }\n\n return false;\n}"
- "public static function factory($accessToken, $currentTeam)\n {\n $client\
\ = Client::factory($accessToken);\n\n return new self($client, $currentTeam);\n\
\ }"
- source_sentence: '// New creates a new ImageGraphics including an image.RGBA of
dimension w x h
// with background bgcol. If font is nil it will use a builtin font.
// If fontsize is empty useful default are used.'
sentences:
- "func New(width, height int, bgcol color.RGBA, font *truetype.Font, fontsize map[chart.FontSize]float64)\
\ *ImageGraphics {\n\timg := image.NewRGBA(image.Rect(0, 0, width, height))\n\t\
gc := draw2dimg.NewGraphicContext(img)\n\tgc.SetLineJoin(draw2d.BevelJoin)\n\t\
gc.SetLineCap(draw2d.SquareCap)\n\tgc.SetStrokeColor(image.Black)\n\tgc.SetFillColor(bgcol)\n\
\tgc.Translate(0.5, 0.5)\n\tgc.Clear()\n\tif font == nil {\n\t\tfont = defaultFont\n\
\t}\n\tif len(fontsize) == 0 {\n\t\tfontsize = ConstructFontSizes(13)\n\t}\n\t\
return &ImageGraphics{Image: img, x0: 0, y0: 0, w: width, h: height,\n\t\tbg:\
\ bgcol, gc: gc, font: font, fs: fontsize}\n}"
- "public static void requestDataLogsForApp(final Context context, final UUID appUuid)\
\ {\n final Intent requestIntent = new Intent(INTENT_DL_REQUEST_DATA);\n\
\ requestIntent.putExtra(APP_UUID, appUuid);\n context.sendBroadcast(requestIntent);\n\
\ }"
- "final protected function setWriteMode($mode)\n {\n if (!in_array($mode,\
\ [static::WRITE_MODE_INSERT, static::WRITE_MODE_UPSERT, static::WRITE_MODE_UPDATE]))\
\ {\n throw new \\InvalidArgumentException(sprintf('Passed write mode\
\ \"%s\" is invalid!', $mode));\n }\n $this->writeMode = $mode;\n\
\ }"
- source_sentence: 'Builds the path for a closed arc, returning a PolygonOptions that
can be
further customised before use.
@param center
@param start
@param end
@param arcType Pass in either ArcType.CHORD or ArcType.ROUND
@return PolygonOptions with the paths element populated.'
sentences:
- "function getJavaScriptCallbackParameterListSimple(parameters) {\n var result\
\ = []\n\n parameters.forEach(function(parameter){\n if (!parameter.out)\
\ return\n result.push(\"/*\" + getIdlType(parameter.type) + \"*/ \"+ parameter.name)\n\
\ })\n\n return result.join(\", \")\n}"
- "public Color getBackground() {\r\n\t\tpredraw();\r\n\t\tFloatBuffer buffer =\
\ BufferUtils.createFloatBuffer(16);\r\n\t\tGL.glGetFloat(SGL.GL_COLOR_CLEAR_VALUE,\
\ buffer);\r\n\t\tpostdraw();\r\n\r\n\t\treturn new Color(buffer);\r\n\t}"
- "public static final PolygonOptions buildClosedArc(LatLong center, LatLong start,\
\ LatLong end, ArcType arcType) {\n MVCArray res = buildArcPoints(center,\
\ start, end);\n if (ArcType.ROUND.equals(arcType)) {\n res.push(center);\n\
\ }\n return new PolygonOptions().paths(res);\n }"
- source_sentence: "Read data from a spread sheet. Return the data in a dict with\n\
\ column numbers as keys.\n\n sheet: xlrd.sheet.Sheet instance\n \
\ Ready for use.\n\n startstops: list\n Four StartStop objects defining\
\ the data to read. See\n :func:`~channelpack.pullxl.prepread`.\n\n \
\ usecols: str or seqence of ints or None\n The columns to use, 0-based.\
\ 0 is the spread sheet column\n \"A\". Can be given as a string also -\
\ 'C:E, H' for columns C, D,\n E and H.\n\n Values in the returned dict\
\ are numpy arrays. Types are set based on\n the types in the spread sheet."
sentences:
- "public function handleScanNotify(callable $callback)\n {\n $notify\
\ = $this->getNotify();\n\n if (!$notify->isValid()) {\n throw\
\ new FaultException('Invalid request payloads.', 400);\n }\n\n \
\ $notify = $notify->getNotify();\n\n try {\n $prepayId = call_user_func_array($callback,\
\ [$notify->get('product_id'), $notify->get('openid'), $notify]);\n \
\ $response = [\n 'return_code' => 'SUCCESS',\n \
\ 'appid' => $this->merchant->app_id,\n 'mch_id' =>\
\ $this->merchant->merchant_id,\n 'nonce_str' => uniqid(),\n\
\ 'prepay_id' => strval($prepayId),\n 'result_code'\
\ => 'SUCCESS',\n ];\n $response['sign'] = generate_sign($response,\
\ $this->merchant->key);\n } catch (\\Exception $e) {\n $response\
\ = [\n 'return_code' => 'SUCCESS',\n 'return_msg'\
\ => $e->getCode(),\n 'result_code' => 'FAIL',\n \
\ 'err_code_des' => $e->getMessage(),\n ];\n }\n\n \
\ return new Response(XML::build($response));\n }"
- "def _sheet_asdict(sheet, startstops, usecols=None):\n \"\"\"\n \"\"\"\n\
\n _, _, start, stop = startstops\n usecols = _sanitize_usecols(usecols)\n\
\n if usecols is not None:\n iswithin = start.col <= min(usecols) and\
\ stop.col > max(usecols)\n mess = 'Column in usecols outside defined data\
\ range, got '\n assert iswithin, mess + str(usecols)\n else: \
\ # usecols is None.\n usecols = tuple(range(start.col,\
\ stop.col))\n\n # cols = usecols or range(start.col, stop.col)\n D = dict()\n\
\n for c in usecols:\n cells = sheet.col(c, start_rowx=start.row, end_rowx=stop.row)\n\
\ types = set([cell.ctype for cell in cells])\n\n # Replace empty\
\ values with nan if appropriate:\n if (not types - NANABLE) and xlrd.XL_CELL_NUMBER\
\ in types:\n D[c] = np.array([np.nan if cell.value == '' else cell.value\n\
\ for cell in cells])\n elif xlrd.XL_CELL_DATE\
\ in types:\n dm = sheet.book.datemode\n vals = []\n \
\ for cell in cells:\n if cell.ctype == xlrd.XL_CELL_DATE:\n\
\ dtuple = xlrd.xldate_as_tuple(cell.value, dm)\n \
\ vals.append(datetime.datetime(*dtuple))\n elif cell.ctype\
\ in NONABLES:\n vals.append(None)\n else:\n\
\ vals.append(cell.value)\n D[c] = np.array(vals)\n\
\ else:\n vals = [None if cell.ctype in NONABLES else cell.value\n\
\ for cell in cells]\n D[c] = np.array(vals)\n\n\
\ return D"
- "func (o Option) RequiresOption(name string) bool {\n\tfor _, o := range o.Requires\
\ {\n\t\tif o == name {\n\t\t\treturn true\n\t\t}\n\t}\n\n\treturn false\n}"
- source_sentence: '// reBuild partially rebuilds a site given the filesystem events.
// It returns whetever the content source was changed.
// TODO(bep) clean up/rewrite this method.'
sentences:
- "func WebPageImageResolver(doc *goquery.Document) ([]candidate, int) {\n\timgs\
\ := doc.Find(\"img\")\n\n\tvar candidates []candidate\n\tsignificantSurface :=\
\ 320 * 200\n\tsignificantSurfaceCount := 0\n\tsrc := \"\"\n\timgs.Each(func(i\
\ int, tag *goquery.Selection) {\n\t\tvar surface int\n\t\tsrc = getImageSrc(tag)\n\
\t\tif src == \"\" {\n\t\t\treturn\n\t\t}\n\n\t\twidth, _ := tag.Attr(\"width\"\
)\n\t\theight, _ := tag.Attr(\"height\")\n\t\tif width != \"\" {\n\t\t\tw, _ :=\
\ strconv.Atoi(width)\n\t\t\tif height != \"\" {\n\t\t\t\th, _ := strconv.Atoi(height)\n\
\t\t\t\tsurface = w * h\n\t\t\t} else {\n\t\t\t\tsurface = w\n\t\t\t}\n\t\t} else\
\ {\n\t\t\tif height != \"\" {\n\t\t\t\tsurface, _ = strconv.Atoi(height)\n\t\t\
\t} else {\n\t\t\t\tsurface = 0\n\t\t\t}\n\t\t}\n\n\t\tif surface > significantSurface\
\ {\n\t\t\tsignificantSurfaceCount++\n\t\t}\n\n\t\ttagscore := score(tag)\n\t\t\
if tagscore >= 0 {\n\t\t\tc := candidate{\n\t\t\t\turl: src,\n\t\t\t\tsurface:\
\ surface,\n\t\t\t\tscore: score(tag),\n\t\t\t}\n\t\t\tcandidates = append(candidates,\
\ c)\n\t\t}\n\t})\n\n\tif len(candidates) == 0 {\n\t\treturn nil, 0\n\t}\n\n\t\
return candidates, significantSurfaceCount\n\n}"
- "@SuppressWarnings(\"rawtypes\")\n\tpublic void open(Map conf, TopologyContext\
\ context,\n\t\t\tSpoutOutputCollector collector) {\n\t\tif(this.jmsProvider ==\
\ null){\n\t\t\tthrow new IllegalStateException(\"JMS provider has not been set.\"\
);\n\t\t}\n\t\tif(this.tupleProducer == null){\n\t\t\tthrow new IllegalStateException(\"\
JMS Tuple Producer has not been set.\");\n\t\t}\n\t\tInteger topologyTimeout =\
\ (Integer)conf.get(\"topology.message.timeout.secs\");\n\t\t// TODO fine a way\
\ to get the default timeout from storm, so we're not hard-coding to 30 seconds\
\ (it could change)\n\t\ttopologyTimeout = topologyTimeout == null ? 30 : topologyTimeout;\n\
\t\tif( (topologyTimeout.intValue() * 1000 )> this.recoveryPeriod){\n\t\t LOG.warn(\"\
*** WARNING *** : \" +\n\t\t \t\t\"Recovery period (\"+ this.recoveryPeriod\
\ + \" ms.) is less then the configured \" +\n\t\t \t\t\"'topology.message.timeout.secs'\
\ of \" + topologyTimeout + \n\t\t \t\t\" secs. This could lead to a message\
\ replay flood!\");\n\t\t}\n\t\tthis.queue = new LinkedBlockingQueue<Message>();\n\
\t\tthis.toCommit = new TreeSet<JmsMessageID>();\n this.pendingMessages\
\ = new HashMap<JmsMessageID, Message>();\n\t\tthis.collector = collector;\n\t\
\ttry {\n\t\t\tConnectionFactory cf = this.jmsProvider.connectionFactory();\n\t\
\t\tDestination dest = this.jmsProvider.destination();\n\t\t\tthis.connection\
\ = cf.createConnection();\n\t\t\tthis.session = connection.createSession(false,\n\
\t\t\t\t\tthis.jmsAcknowledgeMode);\n\t\t\tMessageConsumer consumer = session.createConsumer(dest);\n\
\t\t\tconsumer.setMessageListener(this);\n\t\t\tthis.connection.start();\n\t\t\
\tif (this.isDurableSubscription() && this.recoveryPeriod > 0){\n\t\t\t this.recoveryTimer\
\ = new Timer();\n\t\t\t this.recoveryTimer.scheduleAtFixedRate(new RecoveryTask(),\
\ 10, this.recoveryPeriod);\n\t\t\t}\n\t\t\t\n\t\t} catch (Exception e) {\n\t\t\
\tLOG.warn(\"Error creating JMS connection.\", e);\n\t\t}\n\n\t}"
- "func (s *Site) processPartial(events []fsnotify.Event) (whatChanged, error) {\n\
\n\tevents = s.filterFileEvents(events)\n\tevents = s.translateFileEvents(events)\n\
\n\ts.Log.DEBUG.Printf(\"Rebuild for events %q\", events)\n\n\th := s.h\n\n\t\
// First we need to determine what changed\n\n\tvar (\n\t\tsourceChanged \
\ = []fsnotify.Event{}\n\t\tsourceReallyChanged = []fsnotify.Event{}\n\t\tcontentFilesChanged\
\ []string\n\t\ttmplChanged = []fsnotify.Event{}\n\t\tdataChanged \
\ = []fsnotify.Event{}\n\t\ti18nChanged = []fsnotify.Event{}\n\t\t\
shortcodesChanged = make(map[string]bool)\n\t\tsourceFilesChanged = make(map[string]bool)\n\
\n\t\t// prevent spamming the log on changes\n\t\tlogger = helpers.NewDistinctFeedbackLogger()\n\
\t)\n\n\tcachePartitions := make([]string, len(events))\n\n\tfor i, ev := range\
\ events {\n\t\tcachePartitions[i] = resources.ResourceKeyPartition(ev.Name)\n\
\n\t\tif s.isContentDirEvent(ev) {\n\t\t\tlogger.Println(\"Source changed\", ev)\n\
\t\t\tsourceChanged = append(sourceChanged, ev)\n\t\t}\n\t\tif s.isLayoutDirEvent(ev)\
\ {\n\t\t\tlogger.Println(\"Template changed\", ev)\n\t\t\ttmplChanged = append(tmplChanged,\
\ ev)\n\n\t\t\tif strings.Contains(ev.Name, \"shortcodes\") {\n\t\t\t\tshortcode\
\ := filepath.Base(ev.Name)\n\t\t\t\tshortcode = strings.TrimSuffix(shortcode,\
\ filepath.Ext(shortcode))\n\t\t\t\tshortcodesChanged[shortcode] = true\n\t\t\t\
}\n\t\t}\n\t\tif s.isDataDirEvent(ev) {\n\t\t\tlogger.Println(\"Data changed\"\
, ev)\n\t\t\tdataChanged = append(dataChanged, ev)\n\t\t}\n\t\tif s.isI18nEvent(ev)\
\ {\n\t\t\tlogger.Println(\"i18n changed\", ev)\n\t\t\ti18nChanged = append(dataChanged,\
\ ev)\n\t\t}\n\t}\n\n\t// These in memory resource caches will be rebuilt on demand.\n\
\tfor _, s := range s.h.Sites {\n\t\ts.ResourceSpec.ResourceCache.DeletePartitions(cachePartitions...)\n\
\t}\n\n\tif len(tmplChanged) > 0 || len(i18nChanged) > 0 {\n\t\tsites := s.h.Sites\n\
\t\tfirst := sites[0]\n\n\t\ts.h.init.Reset()\n\n\t\t// TOD(bep) globals clean\n\
\t\tif err := first.Deps.LoadResources(); err != nil {\n\t\t\treturn whatChanged{},\
\ err\n\t\t}\n\n\t\tfor i := 1; i < len(sites); i++ {\n\t\t\tsite := sites[i]\n\
\t\t\tvar err error\n\t\t\tdepsCfg := deps.DepsCfg{\n\t\t\t\tLanguage: site.language,\n\
\t\t\t\tMediaTypes: site.mediaTypesConfig,\n\t\t\t\tOutputFormats: site.outputFormatsConfig,\n\
\t\t\t}\n\t\t\tsite.Deps, err = first.Deps.ForLanguage(depsCfg, func(d *deps.Deps)\
\ error {\n\t\t\t\td.Site = &site.Info\n\t\t\t\treturn nil\n\t\t\t})\n\t\t\tif\
\ err != nil {\n\t\t\t\treturn whatChanged{}, err\n\t\t\t}\n\t\t}\n\t}\n\n\tif\
\ len(dataChanged) > 0 {\n\t\ts.h.init.data.Reset()\n\t}\n\n\tfor _, ev := range\
\ sourceChanged {\n\t\tremoved := false\n\n\t\tif ev.Op&fsnotify.Remove == fsnotify.Remove\
\ {\n\t\t\tremoved = true\n\t\t}\n\n\t\t// Some editors (Vim) sometimes issue\
\ only a Rename operation when writing an existing file\n\t\t// Sometimes a rename\
\ operation means that file has been renamed other times it means\n\t\t// it's\
\ been updated\n\t\tif ev.Op&fsnotify.Rename == fsnotify.Rename {\n\t\t\t// If\
\ the file is still on disk, it's only been updated, if it's not, it's been moved\n\
\t\t\tif ex, err := afero.Exists(s.Fs.Source, ev.Name); !ex || err != nil {\n\t\
\t\t\tremoved = true\n\t\t\t}\n\t\t}\n\t\tif removed && IsContentFile(ev.Name)\
\ {\n\t\t\th.removePageByFilename(ev.Name)\n\t\t}\n\n\t\tsourceReallyChanged =\
\ append(sourceReallyChanged, ev)\n\t\tsourceFilesChanged[ev.Name] = true\n\t\
}\n\n\tfor shortcode := range shortcodesChanged {\n\t\t// There are certain scenarios\
\ that, when a shortcode changes,\n\t\t// it isn't sufficient to just rerender\
\ the already parsed shortcode.\n\t\t// One example is if the user adds a new\
\ shortcode to the content file first,\n\t\t// and then creates the shortcode\
\ on the file system.\n\t\t// To handle these scenarios, we must do a full reprocessing\
\ of the\n\t\t// pages that keeps a reference to the changed shortcode.\n\t\t\
pagesWithShortcode := h.findPagesByShortcode(shortcode)\n\t\tfor _, p := range\
\ pagesWithShortcode {\n\t\t\tcontentFilesChanged = append(contentFilesChanged,\
\ p.File().Filename())\n\t\t}\n\t}\n\n\tif len(sourceReallyChanged) > 0 || len(contentFilesChanged)\
\ > 0 {\n\t\tvar filenamesChanged []string\n\t\tfor _, e := range sourceReallyChanged\
\ {\n\t\t\tfilenamesChanged = append(filenamesChanged, e.Name)\n\t\t}\n\t\tif\
\ len(contentFilesChanged) > 0 {\n\t\t\tfilenamesChanged = append(filenamesChanged,\
\ contentFilesChanged...)\n\t\t}\n\n\t\tfilenamesChanged = helpers.UniqueStrings(filenamesChanged)\n\
\n\t\tif err := s.readAndProcessContent(filenamesChanged...); err != nil {\n\t\
\t\treturn whatChanged{}, err\n\t\t}\n\n\t}\n\n\tchanged := whatChanged{\n\t\t\
source: len(sourceChanged) > 0 || len(shortcodesChanged) > 0,\n\t\tother: len(tmplChanged)\
\ > 0 || len(i18nChanged) > 0 || len(dataChanged) > 0,\n\t\tfiles: sourceFilesChanged,\n\
\t}\n\n\treturn changed, nil\n\n}"
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on Shuu12121/CodeModernBERT-Finch
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Shuu12121/CodeModernBERT-Finch](https://huggingface.co/Shuu12121/CodeModernBERT-Finch). It maps sentences & paragraphs to a 512-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-Finch](https://huggingface.co/Shuu12121/CodeModernBERT-Finch) <!-- at revision 501a2f58bac8d2a246fe0d65267f7e03b6a3ef84 -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 512 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, 'architecture': 'ModernBertModel'})
(1): Pooling({'word_embedding_dimension': 512, '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 = [
'// reBuild partially rebuilds a site given the filesystem events.\n// It returns whetever the content source was changed.\n// TODO(bep) clean up/rewrite this method.',
'func (s *Site) processPartial(events []fsnotify.Event) (whatChanged, error) {\n\n\tevents = s.filterFileEvents(events)\n\tevents = s.translateFileEvents(events)\n\n\ts.Log.DEBUG.Printf("Rebuild for events %q", events)\n\n\th := s.h\n\n\t// First we need to determine what changed\n\n\tvar (\n\t\tsourceChanged = []fsnotify.Event{}\n\t\tsourceReallyChanged = []fsnotify.Event{}\n\t\tcontentFilesChanged []string\n\t\ttmplChanged = []fsnotify.Event{}\n\t\tdataChanged = []fsnotify.Event{}\n\t\ti18nChanged = []fsnotify.Event{}\n\t\tshortcodesChanged = make(map[string]bool)\n\t\tsourceFilesChanged = make(map[string]bool)\n\n\t\t// prevent spamming the log on changes\n\t\tlogger = helpers.NewDistinctFeedbackLogger()\n\t)\n\n\tcachePartitions := make([]string, len(events))\n\n\tfor i, ev := range events {\n\t\tcachePartitions[i] = resources.ResourceKeyPartition(ev.Name)\n\n\t\tif s.isContentDirEvent(ev) {\n\t\t\tlogger.Println("Source changed", ev)\n\t\t\tsourceChanged = append(sourceChanged, ev)\n\t\t}\n\t\tif s.isLayoutDirEvent(ev) {\n\t\t\tlogger.Println("Template changed", ev)\n\t\t\ttmplChanged = append(tmplChanged, ev)\n\n\t\t\tif strings.Contains(ev.Name, "shortcodes") {\n\t\t\t\tshortcode := filepath.Base(ev.Name)\n\t\t\t\tshortcode = strings.TrimSuffix(shortcode, filepath.Ext(shortcode))\n\t\t\t\tshortcodesChanged[shortcode] = true\n\t\t\t}\n\t\t}\n\t\tif s.isDataDirEvent(ev) {\n\t\t\tlogger.Println("Data changed", ev)\n\t\t\tdataChanged = append(dataChanged, ev)\n\t\t}\n\t\tif s.isI18nEvent(ev) {\n\t\t\tlogger.Println("i18n changed", ev)\n\t\t\ti18nChanged = append(dataChanged, ev)\n\t\t}\n\t}\n\n\t// These in memory resource caches will be rebuilt on demand.\n\tfor _, s := range s.h.Sites {\n\t\ts.ResourceSpec.ResourceCache.DeletePartitions(cachePartitions...)\n\t}\n\n\tif len(tmplChanged) > 0 || len(i18nChanged) > 0 {\n\t\tsites := s.h.Sites\n\t\tfirst := sites[0]\n\n\t\ts.h.init.Reset()\n\n\t\t// TOD(bep) globals clean\n\t\tif err := first.Deps.LoadResources(); err != nil {\n\t\t\treturn whatChanged{}, err\n\t\t}\n\n\t\tfor i := 1; i < len(sites); i++ {\n\t\t\tsite := sites[i]\n\t\t\tvar err error\n\t\t\tdepsCfg := deps.DepsCfg{\n\t\t\t\tLanguage: site.language,\n\t\t\t\tMediaTypes: site.mediaTypesConfig,\n\t\t\t\tOutputFormats: site.outputFormatsConfig,\n\t\t\t}\n\t\t\tsite.Deps, err = first.Deps.ForLanguage(depsCfg, func(d *deps.Deps) error {\n\t\t\t\td.Site = &site.Info\n\t\t\t\treturn nil\n\t\t\t})\n\t\t\tif err != nil {\n\t\t\t\treturn whatChanged{}, err\n\t\t\t}\n\t\t}\n\t}\n\n\tif len(dataChanged) > 0 {\n\t\ts.h.init.data.Reset()\n\t}\n\n\tfor _, ev := range sourceChanged {\n\t\tremoved := false\n\n\t\tif ev.Op&fsnotify.Remove == fsnotify.Remove {\n\t\t\tremoved = true\n\t\t}\n\n\t\t// Some editors (Vim) sometimes issue only a Rename operation when writing an existing file\n\t\t// Sometimes a rename operation means that file has been renamed other times it means\n\t\t// it\'s been updated\n\t\tif ev.Op&fsnotify.Rename == fsnotify.Rename {\n\t\t\t// If the file is still on disk, it\'s only been updated, if it\'s not, it\'s been moved\n\t\t\tif ex, err := afero.Exists(s.Fs.Source, ev.Name); !ex || err != nil {\n\t\t\t\tremoved = true\n\t\t\t}\n\t\t}\n\t\tif removed && IsContentFile(ev.Name) {\n\t\t\th.removePageByFilename(ev.Name)\n\t\t}\n\n\t\tsourceReallyChanged = append(sourceReallyChanged, ev)\n\t\tsourceFilesChanged[ev.Name] = true\n\t}\n\n\tfor shortcode := range shortcodesChanged {\n\t\t// There are certain scenarios that, when a shortcode changes,\n\t\t// it isn\'t sufficient to just rerender the already parsed shortcode.\n\t\t// One example is if the user adds a new shortcode to the content file first,\n\t\t// and then creates the shortcode on the file system.\n\t\t// To handle these scenarios, we must do a full reprocessing of the\n\t\t// pages that keeps a reference to the changed shortcode.\n\t\tpagesWithShortcode := h.findPagesByShortcode(shortcode)\n\t\tfor _, p := range pagesWithShortcode {\n\t\t\tcontentFilesChanged = append(contentFilesChanged, p.File().Filename())\n\t\t}\n\t}\n\n\tif len(sourceReallyChanged) > 0 || len(contentFilesChanged) > 0 {\n\t\tvar filenamesChanged []string\n\t\tfor _, e := range sourceReallyChanged {\n\t\t\tfilenamesChanged = append(filenamesChanged, e.Name)\n\t\t}\n\t\tif len(contentFilesChanged) > 0 {\n\t\t\tfilenamesChanged = append(filenamesChanged, contentFilesChanged...)\n\t\t}\n\n\t\tfilenamesChanged = helpers.UniqueStrings(filenamesChanged)\n\n\t\tif err := s.readAndProcessContent(filenamesChanged...); err != nil {\n\t\t\treturn whatChanged{}, err\n\t\t}\n\n\t}\n\n\tchanged := whatChanged{\n\t\tsource: len(sourceChanged) > 0 || len(shortcodesChanged) > 0,\n\t\tother: len(tmplChanged) > 0 || len(i18nChanged) > 0 || len(dataChanged) > 0,\n\t\tfiles: sourceFilesChanged,\n\t}\n\n\treturn changed, nil\n\n}',
'func WebPageImageResolver(doc *goquery.Document) ([]candidate, int) {\n\timgs := doc.Find("img")\n\n\tvar candidates []candidate\n\tsignificantSurface := 320 * 200\n\tsignificantSurfaceCount := 0\n\tsrc := ""\n\timgs.Each(func(i int, tag *goquery.Selection) {\n\t\tvar surface int\n\t\tsrc = getImageSrc(tag)\n\t\tif src == "" {\n\t\t\treturn\n\t\t}\n\n\t\twidth, _ := tag.Attr("width")\n\t\theight, _ := tag.Attr("height")\n\t\tif width != "" {\n\t\t\tw, _ := strconv.Atoi(width)\n\t\t\tif height != "" {\n\t\t\t\th, _ := strconv.Atoi(height)\n\t\t\t\tsurface = w * h\n\t\t\t} else {\n\t\t\t\tsurface = w\n\t\t\t}\n\t\t} else {\n\t\t\tif height != "" {\n\t\t\t\tsurface, _ = strconv.Atoi(height)\n\t\t\t} else {\n\t\t\t\tsurface = 0\n\t\t\t}\n\t\t}\n\n\t\tif surface > significantSurface {\n\t\t\tsignificantSurfaceCount++\n\t\t}\n\n\t\ttagscore := score(tag)\n\t\tif tagscore >= 0 {\n\t\t\tc := candidate{\n\t\t\t\turl: src,\n\t\t\t\tsurface: surface,\n\t\t\t\tscore: score(tag),\n\t\t\t}\n\t\t\tcandidates = append(candidates, c)\n\t\t}\n\t})\n\n\tif len(candidates) == 0 {\n\t\treturn nil, 0\n\t}\n\n\treturn candidates, significantSurfaceCount\n\n}',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6781, 0.3028],
# [0.6781, 1.0000, 0.3359],
# [0.3028, 0.3359, 1.0000]])
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Bias, Risks and Limitations
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 58,800 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: 55.73 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 179.65 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>// CASNext is a non-callback, loop-based version of CAS method.<br>//<br>// Usage is like this:<br>//<br>// var state memcached.CASState<br>// for client.CASNext(vb, key, exp, &state) {<br>// state.Value = some_mutation(state.Value)<br>// }<br>// if state.Err != nil { ... }</code> | <code>func (c *Client) CASNext(vb uint16, k string, exp int, state *CASState) bool {<br> if state.initialized {<br> if !state.Exists {<br> // Adding a new key:<br> if state.Value == nil {<br> state.Cas = 0<br> return false // no-op (delete of non-existent value)<br> }<br> state.resp, state.Err = c.Add(vb, k, 0, exp, state.Value)<br> } else {<br> // Updating / deleting a key:<br> req := &gomemcached.MCRequest{<br> Opcode: gomemcached.DELETE,<br> VBucket: vb,<br> Key: []byte(k),<br> Cas: state.Cas}<br> if state.Value != nil {<br> req.Opcode = gomemcached.SET<br> req.Opaque = 0<br> req.Extras = []byte{0, 0, 0, 0, 0, 0, 0, 0}<br> req.Body = state.Value<br><br> flags := 0<br> exp := 0 // ??? Should we use initialexp here instead?<br> binary.BigEndian.PutUint64(req.Extras, uint64(flags)<<32|uint64(exp))<br> }<br> state.resp, state.Err = c.Send(req)<br> }<br><br> // If the response status is KEY_EEXISTS or NOT_STORED there's a conflict and we'll need to<br> // get the new value (below). Otherwise, we're done (either ...</code> | <code>1.0</code> |
| <code>// RestoreResourcePools restores a bulk of resource pools, usually from a backup.</code> | <code>func (f *Facade) RestoreResourcePools(ctx datastore.Context, pools []pool.ResourcePool) error {<br> defer ctx.Metrics().Stop(ctx.Metrics().Start("Facade.RestoreResourcePools"))<br> // Do not DFSLock here, ControlPlaneDao does that<br> var alog audit.Logger<br> for _, pool := range pools {<br> alog = f.auditLogger.Message(ctx, "Adding ResourcePool").Action(audit.Add).Entity(&pool)<br> pool.DatabaseVersion = 0<br> if err := f.addResourcePool(ctx, &pool); err != nil {<br> if err == ErrPoolExists {<br> if err := f.updateResourcePool(ctx, &pool); err != nil {<br> glog.Errorf("Could not restore resource pool %s via update: %s", pool.ID, err)<br> return alog.Error(err)<br> }<br> } else {<br> glog.Errorf("Could not restore resource pool %s via add: %s", pool.ID, err)<br> return alog.Error(err)<br> }<br> }<br> alog.Succeeded()<br> }<br> return nil<br>}</code> | <code>1.0</code> |
| <code>// run starts a goroutine to handle client connects and broadcast events.</code> | <code>func (s *Streamer) run() {<br> go func() {<br> for {<br> select {<br> case cl := <-s.connecting:<br> s.clients[cl] = true<br><br> case cl := <-s.disconnecting:<br> delete(s.clients, cl)<br><br> case event := <-s.event:<br> for cl := range s.clients {<br> // TODO: non-blocking broadcast<br> //select {<br> //case cl <- event: // Try to send event to client<br> //default:<br> // fmt.Println("Channel full. Discarding value")<br> //}<br> cl <- event<br> }<br> }<br> }<br> }()<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`: 40
- `per_device_eval_batch_size`: 40
- `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`: 40
- `per_device_eval_batch_size`: 40
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `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
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.3401 | 500 | 0.256 |
| 0.6803 | 1000 | 0.1615 |
| 1.0204 | 1500 | 0.1329 |
| 1.3605 | 2000 | 0.034 |
| 1.7007 | 2500 | 0.032 |
| 2.0408 | 3000 | 0.0271 |
| 2.3810 | 3500 | 0.0081 |
| 2.7211 | 4000 | 0.0074 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 5.0.0
- Transformers: 4.53.1
- PyTorch: 2.7.0+cu128
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## 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",
}
```
#### 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}
}
```
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