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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- dense |
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- generated_from_trainer |
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- dataset_size:58800 |
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- loss:MultipleNegativesRankingLoss |
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base_model: Shuu12121/CodeModernBERT-Finch |
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widget: |
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- source_sentence: 'Returns boolean indicating whether the requestUrl matches against |
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the paths configured. |
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@param requestedUrl - url requested by user |
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@param opts - unless configuration |
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@returns {boolean}' |
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sentences: |
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- "def xmoe2_v1_l4k_global_only():\n \"\"\"\"\"\"\n hparams = xmoe2_v1_l4k()\n\ |
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\ hparams.decoder_layers = [\n \"att\" if l == \"local_att\" else l for\ |
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\ l in hparams.decoder_layers]\n return hparams" |
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- "function matchesPath(requestedUrl, opts) {\n var paths = !opts.path || Array.isArray(opts.path)\ |
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\ ?\n opts.path : [opts.path];\n\n if (paths) {\n return paths.some(function(p)\ |
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\ {\n return (typeof p === 'string' && p === requestedUrl.pathname) ||\n\ |
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\ (p instanceof RegExp && !! p.exec(requestedUrl.pathname));\n });\n\ |
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\ }\n\n return false;\n}" |
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- "public static function factory($accessToken, $currentTeam)\n {\n $client\ |
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\ = Client::factory($accessToken);\n\n return new self($client, $currentTeam);\n\ |
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\ }" |
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- source_sentence: '// New creates a new ImageGraphics including an image.RGBA of |
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dimension w x h |
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// with background bgcol. If font is nil it will use a builtin font. |
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// If fontsize is empty useful default are used.' |
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sentences: |
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- "func New(width, height int, bgcol color.RGBA, font *truetype.Font, fontsize map[chart.FontSize]float64)\ |
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\ *ImageGraphics {\n\timg := image.NewRGBA(image.Rect(0, 0, width, height))\n\t\ |
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gc := draw2dimg.NewGraphicContext(img)\n\tgc.SetLineJoin(draw2d.BevelJoin)\n\t\ |
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gc.SetLineCap(draw2d.SquareCap)\n\tgc.SetStrokeColor(image.Black)\n\tgc.SetFillColor(bgcol)\n\ |
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\tgc.Translate(0.5, 0.5)\n\tgc.Clear()\n\tif font == nil {\n\t\tfont = defaultFont\n\ |
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\t}\n\tif len(fontsize) == 0 {\n\t\tfontsize = ConstructFontSizes(13)\n\t}\n\t\ |
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return &ImageGraphics{Image: img, x0: 0, y0: 0, w: width, h: height,\n\t\tbg:\ |
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\ bgcol, gc: gc, font: font, fs: fontsize}\n}" |
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- "public static void requestDataLogsForApp(final Context context, final UUID appUuid)\ |
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\ {\n final Intent requestIntent = new Intent(INTENT_DL_REQUEST_DATA);\n\ |
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\ requestIntent.putExtra(APP_UUID, appUuid);\n context.sendBroadcast(requestIntent);\n\ |
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\ }" |
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- "final protected function setWriteMode($mode)\n {\n if (!in_array($mode,\ |
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\ [static::WRITE_MODE_INSERT, static::WRITE_MODE_UPSERT, static::WRITE_MODE_UPDATE]))\ |
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\ {\n throw new \\InvalidArgumentException(sprintf('Passed write mode\ |
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\ \"%s\" is invalid!', $mode));\n }\n $this->writeMode = $mode;\n\ |
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\ }" |
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- source_sentence: 'Builds the path for a closed arc, returning a PolygonOptions that |
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can be |
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further customised before use. |
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@param center |
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@param start |
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@param end |
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@param arcType Pass in either ArcType.CHORD or ArcType.ROUND |
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@return PolygonOptions with the paths element populated.' |
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sentences: |
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- "function getJavaScriptCallbackParameterListSimple(parameters) {\n var result\ |
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\ = []\n\n parameters.forEach(function(parameter){\n if (!parameter.out)\ |
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\ return\n result.push(\"/*\" + getIdlType(parameter.type) + \"*/ \"+ parameter.name)\n\ |
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\ })\n\n return result.join(\", \")\n}" |
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- "public Color getBackground() {\r\n\t\tpredraw();\r\n\t\tFloatBuffer buffer =\ |
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\ BufferUtils.createFloatBuffer(16);\r\n\t\tGL.glGetFloat(SGL.GL_COLOR_CLEAR_VALUE,\ |
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\ buffer);\r\n\t\tpostdraw();\r\n\r\n\t\treturn new Color(buffer);\r\n\t}" |
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- "public static final PolygonOptions buildClosedArc(LatLong center, LatLong start,\ |
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\ LatLong end, ArcType arcType) {\n MVCArray res = buildArcPoints(center,\ |
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\ start, end);\n if (ArcType.ROUND.equals(arcType)) {\n res.push(center);\n\ |
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\ }\n return new PolygonOptions().paths(res);\n }" |
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- source_sentence: "Read data from a spread sheet. Return the data in a dict with\n\ |
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\ column numbers as keys.\n\n sheet: xlrd.sheet.Sheet instance\n \ |
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\ Ready for use.\n\n startstops: list\n Four StartStop objects defining\ |
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\ the data to read. See\n :func:`~channelpack.pullxl.prepread`.\n\n \ |
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\ usecols: str or seqence of ints or None\n The columns to use, 0-based.\ |
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\ 0 is the spread sheet column\n \"A\". Can be given as a string also -\ |
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\ 'C:E, H' for columns C, D,\n E and H.\n\n Values in the returned dict\ |
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\ are numpy arrays. Types are set based on\n the types in the spread sheet." |
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sentences: |
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- "public function handleScanNotify(callable $callback)\n {\n $notify\ |
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\ = $this->getNotify();\n\n if (!$notify->isValid()) {\n throw\ |
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\ new FaultException('Invalid request payloads.', 400);\n }\n\n \ |
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\ $notify = $notify->getNotify();\n\n try {\n $prepayId = call_user_func_array($callback,\ |
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\ [$notify->get('product_id'), $notify->get('openid'), $notify]);\n \ |
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\ $response = [\n 'return_code' => 'SUCCESS',\n \ |
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\ 'appid' => $this->merchant->app_id,\n 'mch_id' =>\ |
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\ $this->merchant->merchant_id,\n 'nonce_str' => uniqid(),\n\ |
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\ 'prepay_id' => strval($prepayId),\n 'result_code'\ |
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\ => 'SUCCESS',\n ];\n $response['sign'] = generate_sign($response,\ |
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\ $this->merchant->key);\n } catch (\\Exception $e) {\n $response\ |
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\ = [\n 'return_code' => 'SUCCESS',\n 'return_msg'\ |
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\ => $e->getCode(),\n 'result_code' => 'FAIL',\n \ |
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\ 'err_code_des' => $e->getMessage(),\n ];\n }\n\n \ |
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\ return new Response(XML::build($response));\n }" |
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- "def _sheet_asdict(sheet, startstops, usecols=None):\n \"\"\"\n \"\"\"\n\ |
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\n _, _, start, stop = startstops\n usecols = _sanitize_usecols(usecols)\n\ |
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\n if usecols is not None:\n iswithin = start.col <= min(usecols) and\ |
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\ stop.col > max(usecols)\n mess = 'Column in usecols outside defined data\ |
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\ range, got '\n assert iswithin, mess + str(usecols)\n else: \ |
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\ # usecols is None.\n usecols = tuple(range(start.col,\ |
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\ stop.col))\n\n # cols = usecols or range(start.col, stop.col)\n D = dict()\n\ |
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\n for c in usecols:\n cells = sheet.col(c, start_rowx=start.row, end_rowx=stop.row)\n\ |
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\ types = set([cell.ctype for cell in cells])\n\n # Replace empty\ |
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\ values with nan if appropriate:\n if (not types - NANABLE) and xlrd.XL_CELL_NUMBER\ |
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\ in types:\n D[c] = np.array([np.nan if cell.value == '' else cell.value\n\ |
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\ for cell in cells])\n elif xlrd.XL_CELL_DATE\ |
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\ in types:\n dm = sheet.book.datemode\n vals = []\n \ |
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\ for cell in cells:\n if cell.ctype == xlrd.XL_CELL_DATE:\n\ |
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\ dtuple = xlrd.xldate_as_tuple(cell.value, dm)\n \ |
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\ vals.append(datetime.datetime(*dtuple))\n elif cell.ctype\ |
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\ in NONABLES:\n vals.append(None)\n else:\n\ |
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\ vals.append(cell.value)\n D[c] = np.array(vals)\n\ |
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\ else:\n vals = [None if cell.ctype in NONABLES else cell.value\n\ |
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\ for cell in cells]\n D[c] = np.array(vals)\n\n\ |
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\ return D" |
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- "func (o Option) RequiresOption(name string) bool {\n\tfor _, o := range o.Requires\ |
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\ {\n\t\tif o == name {\n\t\t\treturn true\n\t\t}\n\t}\n\n\treturn false\n}" |
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- source_sentence: '// reBuild partially rebuilds a site given the filesystem events. |
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// It returns whetever the content source was changed. |
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// TODO(bep) clean up/rewrite this method.' |
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sentences: |
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- "func WebPageImageResolver(doc *goquery.Document) ([]candidate, int) {\n\timgs\ |
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\ := doc.Find(\"img\")\n\n\tvar candidates []candidate\n\tsignificantSurface :=\ |
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\ 320 * 200\n\tsignificantSurfaceCount := 0\n\tsrc := \"\"\n\timgs.Each(func(i\ |
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\ int, tag *goquery.Selection) {\n\t\tvar surface int\n\t\tsrc = getImageSrc(tag)\n\ |
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\t\tif src == \"\" {\n\t\t\treturn\n\t\t}\n\n\t\twidth, _ := tag.Attr(\"width\"\ |
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)\n\t\theight, _ := tag.Attr(\"height\")\n\t\tif width != \"\" {\n\t\t\tw, _ :=\ |
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\ strconv.Atoi(width)\n\t\t\tif height != \"\" {\n\t\t\t\th, _ := strconv.Atoi(height)\n\ |
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\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\ |
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\ {\n\t\t\tif height != \"\" {\n\t\t\t\tsurface, _ = strconv.Atoi(height)\n\t\t\ |
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\t} else {\n\t\t\t\tsurface = 0\n\t\t\t}\n\t\t}\n\n\t\tif surface > significantSurface\ |
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\ {\n\t\t\tsignificantSurfaceCount++\n\t\t}\n\n\t\ttagscore := score(tag)\n\t\t\ |
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if tagscore >= 0 {\n\t\t\tc := candidate{\n\t\t\t\turl: src,\n\t\t\t\tsurface:\ |
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\ surface,\n\t\t\t\tscore: score(tag),\n\t\t\t}\n\t\t\tcandidates = append(candidates,\ |
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\ c)\n\t\t}\n\t})\n\n\tif len(candidates) == 0 {\n\t\treturn nil, 0\n\t}\n\n\t\ |
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return candidates, significantSurfaceCount\n\n}" |
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- "@SuppressWarnings(\"rawtypes\")\n\tpublic void open(Map conf, TopologyContext\ |
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\ context,\n\t\t\tSpoutOutputCollector collector) {\n\t\tif(this.jmsProvider ==\ |
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\ null){\n\t\t\tthrow new IllegalStateException(\"JMS provider has not been set.\"\ |
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);\n\t\t}\n\t\tif(this.tupleProducer == null){\n\t\t\tthrow new IllegalStateException(\"\ |
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JMS Tuple Producer has not been set.\");\n\t\t}\n\t\tInteger topologyTimeout =\ |
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\ (Integer)conf.get(\"topology.message.timeout.secs\");\n\t\t// TODO fine a way\ |
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\ to get the default timeout from storm, so we're not hard-coding to 30 seconds\ |
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\ (it could change)\n\t\ttopologyTimeout = topologyTimeout == null ? 30 : topologyTimeout;\n\ |
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\t\tif( (topologyTimeout.intValue() * 1000 )> this.recoveryPeriod){\n\t\t LOG.warn(\"\ |
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*** WARNING *** : \" +\n\t\t \t\t\"Recovery period (\"+ this.recoveryPeriod\ |
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\ + \" ms.) is less then the configured \" +\n\t\t \t\t\"'topology.message.timeout.secs'\ |
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\ of \" + topologyTimeout + \n\t\t \t\t\" secs. This could lead to a message\ |
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\ replay flood!\");\n\t\t}\n\t\tthis.queue = new LinkedBlockingQueue<Message>();\n\ |
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\t\tthis.toCommit = new TreeSet<JmsMessageID>();\n this.pendingMessages\ |
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\ = new HashMap<JmsMessageID, Message>();\n\t\tthis.collector = collector;\n\t\ |
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\ttry {\n\t\t\tConnectionFactory cf = this.jmsProvider.connectionFactory();\n\t\ |
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\t\tDestination dest = this.jmsProvider.destination();\n\t\t\tthis.connection\ |
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\ = cf.createConnection();\n\t\t\tthis.session = connection.createSession(false,\n\ |
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\t\t\t\t\tthis.jmsAcknowledgeMode);\n\t\t\tMessageConsumer consumer = session.createConsumer(dest);\n\ |
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\t\t\tconsumer.setMessageListener(this);\n\t\t\tthis.connection.start();\n\t\t\ |
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\tif (this.isDurableSubscription() && this.recoveryPeriod > 0){\n\t\t\t this.recoveryTimer\ |
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\ = new Timer();\n\t\t\t this.recoveryTimer.scheduleAtFixedRate(new RecoveryTask(),\ |
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\ 10, this.recoveryPeriod);\n\t\t\t}\n\t\t\t\n\t\t} catch (Exception e) {\n\t\t\ |
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\tLOG.warn(\"Error creating JMS connection.\", e);\n\t\t}\n\n\t}" |
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- "func (s *Site) processPartial(events []fsnotify.Event) (whatChanged, error) {\n\ |
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\n\tevents = s.filterFileEvents(events)\n\tevents = s.translateFileEvents(events)\n\ |
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\n\ts.Log.DEBUG.Printf(\"Rebuild for events %q\", events)\n\n\th := s.h\n\n\t\ |
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// First we need to determine what changed\n\n\tvar (\n\t\tsourceChanged \ |
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\ = []fsnotify.Event{}\n\t\tsourceReallyChanged = []fsnotify.Event{}\n\t\tcontentFilesChanged\ |
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\ []string\n\t\ttmplChanged = []fsnotify.Event{}\n\t\tdataChanged \ |
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\ = []fsnotify.Event{}\n\t\ti18nChanged = []fsnotify.Event{}\n\t\t\ |
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shortcodesChanged = make(map[string]bool)\n\t\tsourceFilesChanged = make(map[string]bool)\n\ |
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\n\t\t// prevent spamming the log on changes\n\t\tlogger = helpers.NewDistinctFeedbackLogger()\n\ |
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\t)\n\n\tcachePartitions := make([]string, len(events))\n\n\tfor i, ev := range\ |
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\ events {\n\t\tcachePartitions[i] = resources.ResourceKeyPartition(ev.Name)\n\ |
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\n\t\tif s.isContentDirEvent(ev) {\n\t\t\tlogger.Println(\"Source changed\", ev)\n\ |
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\t\t\tsourceChanged = append(sourceChanged, ev)\n\t\t}\n\t\tif s.isLayoutDirEvent(ev)\ |
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\ {\n\t\t\tlogger.Println(\"Template changed\", ev)\n\t\t\ttmplChanged = append(tmplChanged,\ |
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\ ev)\n\n\t\t\tif strings.Contains(ev.Name, \"shortcodes\") {\n\t\t\t\tshortcode\ |
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\ := filepath.Base(ev.Name)\n\t\t\t\tshortcode = strings.TrimSuffix(shortcode,\ |
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\ filepath.Ext(shortcode))\n\t\t\t\tshortcodesChanged[shortcode] = true\n\t\t\t\ |
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}\n\t\t}\n\t\tif s.isDataDirEvent(ev) {\n\t\t\tlogger.Println(\"Data changed\"\ |
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, ev)\n\t\t\tdataChanged = append(dataChanged, ev)\n\t\t}\n\t\tif s.isI18nEvent(ev)\ |
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\ {\n\t\t\tlogger.Println(\"i18n changed\", ev)\n\t\t\ti18nChanged = append(dataChanged,\ |
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\ ev)\n\t\t}\n\t}\n\n\t// These in memory resource caches will be rebuilt on demand.\n\ |
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\tfor _, s := range s.h.Sites {\n\t\ts.ResourceSpec.ResourceCache.DeletePartitions(cachePartitions...)\n\ |
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\t}\n\n\tif len(tmplChanged) > 0 || len(i18nChanged) > 0 {\n\t\tsites := s.h.Sites\n\ |
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\t\tfirst := sites[0]\n\n\t\ts.h.init.Reset()\n\n\t\t// TOD(bep) globals clean\n\ |
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\t\tif err := first.Deps.LoadResources(); err != nil {\n\t\t\treturn whatChanged{},\ |
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\ err\n\t\t}\n\n\t\tfor i := 1; i < len(sites); i++ {\n\t\t\tsite := sites[i]\n\ |
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\t\t\tvar err error\n\t\t\tdepsCfg := deps.DepsCfg{\n\t\t\t\tLanguage: site.language,\n\ |
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\t\t\t\tMediaTypes: site.mediaTypesConfig,\n\t\t\t\tOutputFormats: site.outputFormatsConfig,\n\ |
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\t\t\t}\n\t\t\tsite.Deps, err = first.Deps.ForLanguage(depsCfg, func(d *deps.Deps)\ |
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\ error {\n\t\t\t\td.Site = &site.Info\n\t\t\t\treturn nil\n\t\t\t})\n\t\t\tif\ |
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\ err != nil {\n\t\t\t\treturn whatChanged{}, err\n\t\t\t}\n\t\t}\n\t}\n\n\tif\ |
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\ len(dataChanged) > 0 {\n\t\ts.h.init.data.Reset()\n\t}\n\n\tfor _, ev := range\ |
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\ sourceChanged {\n\t\tremoved := false\n\n\t\tif ev.Op&fsnotify.Remove == fsnotify.Remove\ |
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\ {\n\t\t\tremoved = true\n\t\t}\n\n\t\t// Some editors (Vim) sometimes issue\ |
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\ only a Rename operation when writing an existing file\n\t\t// Sometimes a rename\ |
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\ operation means that file has been renamed other times it means\n\t\t// it's\ |
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\ been updated\n\t\tif ev.Op&fsnotify.Rename == fsnotify.Rename {\n\t\t\t// If\ |
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\ the file is still on disk, it's only been updated, if it's not, it's been moved\n\ |
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\t\t\tif ex, err := afero.Exists(s.Fs.Source, ev.Name); !ex || err != nil {\n\t\ |
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\t\t\tremoved = true\n\t\t\t}\n\t\t}\n\t\tif removed && IsContentFile(ev.Name)\ |
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\ {\n\t\t\th.removePageByFilename(ev.Name)\n\t\t}\n\n\t\tsourceReallyChanged =\ |
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\ append(sourceReallyChanged, ev)\n\t\tsourceFilesChanged[ev.Name] = true\n\t\ |
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}\n\n\tfor shortcode := range shortcodesChanged {\n\t\t// There are certain scenarios\ |
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\ that, when a shortcode changes,\n\t\t// it isn't sufficient to just rerender\ |
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\ the already parsed shortcode.\n\t\t// One example is if the user adds a new\ |
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\ shortcode to the content file first,\n\t\t// and then creates the shortcode\ |
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\ on the file system.\n\t\t// To handle these scenarios, we must do a full reprocessing\ |
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\ of the\n\t\t// pages that keeps a reference to the changed shortcode.\n\t\t\ |
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pagesWithShortcode := h.findPagesByShortcode(shortcode)\n\t\tfor _, p := range\ |
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\ pagesWithShortcode {\n\t\t\tcontentFilesChanged = append(contentFilesChanged,\ |
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\ p.File().Filename())\n\t\t}\n\t}\n\n\tif len(sourceReallyChanged) > 0 || len(contentFilesChanged)\ |
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\ > 0 {\n\t\tvar filenamesChanged []string\n\t\tfor _, e := range sourceReallyChanged\ |
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\ {\n\t\t\tfilenamesChanged = append(filenamesChanged, e.Name)\n\t\t}\n\t\tif\ |
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\ len(contentFilesChanged) > 0 {\n\t\t\tfilenamesChanged = append(filenamesChanged,\ |
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\ contentFilesChanged...)\n\t\t}\n\n\t\tfilenamesChanged = helpers.UniqueStrings(filenamesChanged)\n\ |
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\n\t\tif err := s.readAndProcessContent(filenamesChanged...); err != nil {\n\t\ |
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\t\treturn whatChanged{}, err\n\t\t}\n\n\t}\n\n\tchanged := whatChanged{\n\t\t\ |
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source: len(sourceChanged) > 0 || len(shortcodesChanged) > 0,\n\t\tother: len(tmplChanged)\ |
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\ > 0 || len(i18nChanged) > 0 || len(dataChanged) > 0,\n\t\tfiles: sourceFilesChanged,\n\ |
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\t}\n\n\treturn changed, nil\n\n}" |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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--- |
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# SentenceTransformer based on Shuu12121/CodeModernBERT-Finch |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [Shuu12121/CodeModernBERT-Finch](https://huggingface.co/Shuu12121/CodeModernBERT-Finch) <!-- at revision cb1142a6a402471e02d11005b239f349c6d79be0 --> |
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- **Maximum Sequence Length:** 1024 tokens |
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- **Output Dimensionality:** 512 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'}) |
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(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}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sentence_transformers_model_id") |
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# Run inference |
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sentences = [ |
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'// 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.', |
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'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}', |
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'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}', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 512] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities) |
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# tensor([[1.0000, 0.6671, 0.2242], |
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# [0.6671, 1.0000, 0.3125], |
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# [0.2242, 0.3125, 1.0000]]) |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 58,800 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | label | |
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|:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------| |
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| type | string | string | float | |
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| 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> | |
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* Samples: |
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| sentence_0 | sentence_1 | label | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| |
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| <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> | |
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| <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> | |
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| <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> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 200 |
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- `per_device_eval_batch_size`: 200 |
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- `fp16`: True |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 200 |
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- `per_device_eval_batch_size`: 200 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 3 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `hub_revision`: None |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `liger_kernel_config`: None |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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- `router_mapping`: {} |
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- `learning_rate_mapping`: {} |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:------:|:----:|:-------------:| |
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| 1.7007 | 500 | 0.2697 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 5.0.0 |
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- Transformers: 4.53.1 |
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- PyTorch: 2.7.0+cu128 |
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- Accelerate: 1.7.0 |
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- Datasets: 3.6.0 |
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- Tokenizers: 0.21.2 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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