--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:412178 - loss:MultipleNegativesRankingLoss base_model: answerdotai/ModernBERT-base widget: - source_sentence: "Clip off all parts from all bounding boxes that are outside of\ \ the image.\n\n Returns\n -------\n imgaug.BoundingBoxesOnImage\n\ \ Bounding boxes, clipped to fall within the image dimensions." sentences: - "def model_best(y1, y2, samples=1000, progressbar=True):\n \"\"\"\n Bayesian\ \ Estimation Supersedes the T-Test\n\n This model runs a Bayesian hypothesis\ \ comparing if y1 and y2 come\n from the same distribution. Returns are assumed\ \ to be T-distributed.\n\n In addition, computes annual volatility and Sharpe\ \ of in and\n out-of-sample periods.\n\n This model replicates the example\ \ used in:\n Kruschke, John. (2012) Bayesian estimation supersedes the t\n\ \ test. Journal of Experimental Psychology: General.\n\n Parameters\n \ \ ----------\n y1 : array-like\n Array of returns (e.g. in-sample)\n\ \ y2 : array-like\n Array of returns (e.g. out-of-sample)\n samples\ \ : int, optional\n Number of posterior samples to draw.\n\n Returns\n\ \ -------\n model : pymc.Model object\n PyMC3 model containing all\ \ random variables.\n trace : pymc3.sampling.BaseTrace object\n A PyMC3\ \ trace object that contains samples for each parameter\n of the posterior.\n\ \n See Also\n --------\n plot_stoch_vol : plotting of tochastic volatility\ \ model\n \"\"\"\n\n y = np.concatenate((y1, y2))\n\n mu_m = np.mean(y)\n\ \ mu_p = 0.000001 * 1 / np.std(y)**2\n\n sigma_low = np.std(y) / 1000\n\ \ sigma_high = np.std(y) * 1000\n with pm.Model() as model:\n group1_mean\ \ = pm.Normal('group1_mean', mu=mu_m, tau=mu_p,\n \ \ testval=y1.mean())\n group2_mean = pm.Normal('group2_mean', mu=mu_m,\ \ tau=mu_p,\n testval=y2.mean())\n group1_std\ \ = pm.Uniform('group1_std', lower=sigma_low,\n \ \ upper=sigma_high, testval=y1.std())\n group2_std = pm.Uniform('group2_std',\ \ lower=sigma_low,\n upper=sigma_high, testval=y2.std())\n\ \ nu = pm.Exponential('nu_minus_two', 1 / 29., testval=4.) + 2.\n\n \ \ returns_group1 = pm.StudentT('group1', nu=nu, mu=group1_mean,\n \ \ lam=group1_std**-2, observed=y1)\n returns_group2\ \ = pm.StudentT('group2', nu=nu, mu=group2_mean,\n \ \ lam=group2_std**-2, observed=y2)\n\n diff_of_means = pm.Deterministic('difference\ \ of means',\n group2_mean - group1_mean)\n\ \ pm.Deterministic('difference of stds',\n group2_std\ \ - group1_std)\n pm.Deterministic('effect size', diff_of_means /\n \ \ pm.math.sqrt((group1_std**2 +\n \ \ group2_std**2) / 2))\n\n pm.Deterministic('group1_annual_volatility',\n\ \ returns_group1.distribution.variance**.5 *\n \ \ np.sqrt(252))\n pm.Deterministic('group2_annual_volatility',\n\ \ returns_group2.distribution.variance**.5 *\n \ \ np.sqrt(252))\n\n pm.Deterministic('group1_sharpe',\ \ returns_group1.distribution.mean /\n returns_group1.distribution.variance**.5\ \ *\n np.sqrt(252))\n pm.Deterministic('group2_sharpe',\ \ returns_group2.distribution.mean /\n returns_group2.distribution.variance**.5\ \ *\n np.sqrt(252))\n\n trace = pm.sample(samples,\ \ progressbar=progressbar)\n return model, trace" - "def clip_out_of_image(self):\n \"\"\"\n Clip off all parts from\ \ all bounding boxes that are outside of the image.\n\n Returns\n \ \ -------\n imgaug.BoundingBoxesOnImage\n Bounding boxes,\ \ clipped to fall within the image dimensions.\n\n \"\"\"\n bbs_cut\ \ = [bb.clip_out_of_image(self.shape)\n for bb in self.bounding_boxes\ \ if bb.is_partly_within_image(self.shape)]\n return BoundingBoxesOnImage(bbs_cut,\ \ shape=self.shape)" - "def _initPermanence(self, potential, connectedPct):\n \"\"\"\n Initializes\ \ the permanences of a column. The method\n returns a 1-D array the size of\ \ the input, where each entry in the\n array represents the initial permanence\ \ value between the input bit\n at the particular index in the array, and the\ \ column represented by\n the 'index' parameter.\n\n Parameters:\n ----------------------------\n\ \ :param potential: A numpy array specifying the potential pool of the column.\n\ \ Permanence values will only be generated for input bits\n\ \ corresponding to indices for which the mask value is 1.\n\ \ :param connectedPct: A value between 0 or 1 governing the chance, for each\n\ \ permanence, that the initial permanence value will\n\ \ be a value that is considered connected.\n \"\"\"\ \n # Determine which inputs bits will start out as connected\n # to the\ \ inputs. Initially a subset of the input bits in a\n # column's potential\ \ pool will be connected. This number is\n # given by the parameter \"connectedPct\"\ \n perm = numpy.zeros(self._numInputs, dtype=realDType)\n for i in xrange(self._numInputs):\n\ \ if (potential[i] < 1):\n continue\n\n if (self._random.getReal64()\ \ <= connectedPct):\n perm[i] = self._initPermConnected()\n else:\n\ \ perm[i] = self._initPermNonConnected()\n\n # Clip off low values.\ \ Since we use a sparse representation\n # to store the permanence values this\ \ helps reduce memory\n # requirements.\n perm[perm < self._synPermTrimThreshold]\ \ = 0\n\n return perm" - source_sentence: "Perform a weighted average over dicts that are each on a different\ \ node\n Input: local_name2valcount: dict mapping key -> (value, count)\n \ \ Returns: key -> mean" sentences: - "def MotionBlur(k=5, angle=(0, 360), direction=(-1.0, 1.0), order=1, name=None,\ \ deterministic=False, random_state=None):\n \"\"\"\n Augmenter that sharpens\ \ images and overlays the result with the original image.\n\n dtype support::\n\ \n See ``imgaug.augmenters.convolutional.Convolve``.\n\n Parameters\n\ \ ----------\n k : int or tuple of int or list of int or imgaug.parameters.StochasticParameter,\ \ optional\n Kernel size to use.\n\n * If a single int, then\ \ that value will be used for the height\n and width of the kernel.\n\ \ * If a tuple of two ints ``(a, b)``, then the kernel size will be\n\ \ sampled from the interval ``[a..b]``.\n * If a list,\ \ then a random value will be sampled from that list per image.\n *\ \ If a StochasticParameter, then ``N`` samples will be drawn from\n \ \ that parameter per ``N`` input images, each representing the kernel\n \ \ size for the nth image.\n\n angle : number or tuple of number or\ \ list of number or imgaug.parameters.StochasticParameter, optional\n Angle\ \ of the motion blur in degrees (clockwise, relative to top center direction).\n\ \n * If a number, exactly that value will be used.\n * If\ \ a tuple ``(a, b)``, a random value from the range ``a <= x <= b`` will\n \ \ be sampled per image.\n * If a list, then a random value\ \ will be sampled from that list per image.\n * If a StochasticParameter,\ \ a value will be sampled from the\n parameter per image.\n\n \ \ direction : number or tuple of number or list of number or imgaug.parameters.StochasticParameter,\ \ optional\n Forward/backward direction of the motion blur. Lower values\ \ towards -1.0 will point the motion blur towards\n the back (with angle\ \ provided via `angle`). Higher values towards 1.0 will point the motion blur\ \ forward.\n A value of 0.0 leads to a uniformly (but still angled) motion\ \ blur.\n\n * If a number, exactly that value will be used.\n \ \ * If a tuple ``(a, b)``, a random value from the range ``a <= x <= b``\ \ will\n be sampled per image.\n * If a list, then a random\ \ value will be sampled from that list per image.\n * If a StochasticParameter,\ \ a value will be sampled from the\n parameter per image.\n\n \ \ order : int or iterable of int or imgaug.ALL or imgaug.parameters.StochasticParameter,\ \ optional\n Interpolation order to use when rotating the kernel according\ \ to `angle`.\n See :func:`imgaug.augmenters.geometric.Affine.__init__`.\n\ \ Recommended to be ``0`` or ``1``, with ``0`` being faster, but less continuous/smooth\ \ as `angle` is changed,\n particularly around multiple of 45 degrees.\n\ \n name : None or str, optional\n See :func:`imgaug.augmenters.meta.Augmenter.__init__`.\n\ \n deterministic : bool, optional\n See :func:`imgaug.augmenters.meta.Augmenter.__init__`.\n\ \n random_state : None or int or numpy.random.RandomState, optional\n \ \ See :func:`imgaug.augmenters.meta.Augmenter.__init__`.\n\n Examples\n \ \ --------\n >>> aug = iaa.MotionBlur(k=15)\n\n Create a motion blur augmenter\ \ with kernel size of 15x15.\n\n >>> aug = iaa.MotionBlur(k=15, angle=[-45,\ \ 45])\n\n Create a motion blur augmenter with kernel size of 15x15 and a blur\ \ angle of either -45 or 45 degrees (randomly\n picked per image).\n\n \"\ \"\"\n # TODO allow (1, None) and set to identity matrix if k == 1\n k_param\ \ = iap.handle_discrete_param(k, \"k\", value_range=(3, None), tuple_to_uniform=True,\ \ list_to_choice=True,\n allow_floats=False)\n\ \ angle_param = iap.handle_continuous_param(angle, \"angle\", value_range=None,\ \ tuple_to_uniform=True,\n list_to_choice=True)\n\ \ direction_param = iap.handle_continuous_param(direction, \"direction\", value_range=(-1.0-1e-6,\ \ 1.0+1e-6),\n tuple_to_uniform=True,\ \ list_to_choice=True)\n\n def create_matrices(image, nb_channels, random_state_func):\n\ \ # avoid cyclic import between blur and geometric\n from . import\ \ geometric as iaa_geometric\n\n # force discrete for k_sample via int()\ \ in case of stochastic parameter\n k_sample = int(k_param.draw_sample(random_state=random_state_func))\n\ \ angle_sample = angle_param.draw_sample(random_state=random_state_func)\n\ \ direction_sample = direction_param.draw_sample(random_state=random_state_func)\n\ \n k_sample = k_sample if k_sample % 2 != 0 else k_sample + 1\n \ \ direction_sample = np.clip(direction_sample, -1.0, 1.0)\n direction_sample\ \ = (direction_sample + 1.0) / 2.0\n\n matrix = np.zeros((k_sample, k_sample),\ \ dtype=np.float32)\n matrix[:, k_sample//2] = np.linspace(float(direction_sample),\ \ 1.0 - float(direction_sample), num=k_sample)\n rot = iaa_geometric.Affine(rotate=angle_sample,\ \ order=order)\n matrix = (rot.augment_image((matrix * 255).astype(np.uint8))\ \ / 255.0).astype(np.float32)\n\n return [matrix/np.sum(matrix)] * nb_channels\n\ \n if name is None:\n name = \"Unnamed%s\" % (ia.caller_name(),)\n\n\ \ return iaa_convolutional.Convolve(create_matrices, name=name, deterministic=deterministic,\n\ \ random_state=random_state)" - "def rolling_sharpe(returns, rolling_sharpe_window):\n \"\"\"\n Determines\ \ the rolling Sharpe ratio of a strategy.\n\n Parameters\n ----------\n\ \ returns : pd.Series\n Daily returns of the strategy, noncumulative.\n\ \ - See full explanation in tears.create_full_tear_sheet.\n rolling_sharpe_window\ \ : int\n Length of rolling window, in days, over which to compute.\n\n\ \ Returns\n -------\n pd.Series\n Rolling Sharpe ratio.\n\n \ \ Note\n -----\n See https://en.wikipedia.org/wiki/Sharpe_ratio for more\ \ details.\n \"\"\"\n\n return returns.rolling(rolling_sharpe_window).mean()\ \ \\\n / returns.rolling(rolling_sharpe_window).std() \\\n * np.sqrt(APPROX_BDAYS_PER_YEAR)" - "def mpi_weighted_mean(comm, local_name2valcount):\n \"\"\"\n Perform a\ \ weighted average over dicts that are each on a different node\n Input: local_name2valcount:\ \ dict mapping key -> (value, count)\n Returns: key -> mean\n \"\"\"\n \ \ all_name2valcount = comm.gather(local_name2valcount)\n if comm.rank ==\ \ 0:\n name2sum = defaultdict(float)\n name2count = defaultdict(float)\n\ \ for n2vc in all_name2valcount:\n for (name, (val, count))\ \ in n2vc.items():\n try:\n val = float(val)\n\ \ except ValueError:\n if comm.rank == 0:\n\ \ warnings.warn('WARNING: tried to compute mean on non-float\ \ {}={}'.format(name, val))\n else:\n name2sum[name]\ \ += val * count\n name2count[name] += count\n return\ \ {name : name2sum[name] / name2count[name] for name in name2sum}\n else:\n\ \ return {}" - source_sentence: "Generate and return the following encoder related substitution\ \ variables:\n\n encoderSpecsStr:\n For the base description file, this string\ \ defines the default\n encoding dicts for each encoder. For example:\n \ \ '__gym_encoder' : { 'fieldname': 'gym',\n 'n': 13,\n \ \ 'name': 'gym',\n 'type': 'SDRCategoryEncoder',\n 'w': 7},\n\ \ '__address_encoder' : { 'fieldname': 'address',\n 'n': 13,\n\ \ 'name': 'address',\n 'type': 'SDRCategoryEncoder',\n \ \ 'w': 7}\n\n encoderSchemaStr:\n For the base description file, this\ \ is a list containing a\n DeferredDictLookup entry for each encoder. For example:\n\ \ [DeferredDictLookup('__gym_encoder'),\n DeferredDictLookup('__address_encoder'),\n\ \ DeferredDictLookup('__timestamp_timeOfDay_encoder'),\n DeferredDictLookup('__timestamp_dayOfWeek_encoder'),\n\ \ DeferredDictLookup('__consumption_encoder')],\n\n permEncoderChoicesStr:\n\ \ For the permutations file, this defines the possible\n encoder dicts for\ \ each encoder. For example:\n '__timestamp_dayOfWeek_encoder': [\n \ \ None,\n {'fieldname':'timestamp',\n \ \ 'name': 'timestamp_timeOfDay',\n 'type':'DateEncoder'\n\ \ 'dayOfWeek': (7,1)\n },\n \ \ {'fieldname':'timestamp',\n 'name': 'timestamp_timeOfDay',\n\ \ 'type':'DateEncoder'\n 'dayOfWeek':\ \ (7,3)\n },\n ],\n\n '__field_consumption_encoder':\ \ [\n None,\n {'fieldname':'consumption',\n\ \ 'name': 'consumption',\n 'type':'AdaptiveScalarEncoder',\n\ \ 'n': 13,\n 'w': 7,\n \ \ }\n ]\n\n\n\n Parameters:\n --------------------------------------------------\n\ \ includedFields: item from the 'includedFields' section of the\n \ \ description JSON object. This is a list of dicts, each\n \ \ dict defining the field name, type, and optional min\n \ \ and max values.\n\n retval: (encoderSpecsStr, encoderSchemaStr permEncoderChoicesStr)" sentences: - "def _generateEncoderStringsV1(includedFields):\n \"\"\" Generate and return\ \ the following encoder related substitution variables:\n\n encoderSpecsStr:\n\ \ For the base description file, this string defines the default\n encoding\ \ dicts for each encoder. For example:\n '__gym_encoder' : { 'fieldname':\ \ 'gym',\n 'n': 13,\n 'name': 'gym',\n 'type': 'SDRCategoryEncoder',\n\ \ 'w': 7},\n '__address_encoder' : { 'fieldname': 'address',\n\ \ 'n': 13,\n 'name': 'address',\n 'type': 'SDRCategoryEncoder',\n\ \ 'w': 7}\n\n encoderSchemaStr:\n For the base description file,\ \ this is a list containing a\n DeferredDictLookup entry for each encoder.\ \ For example:\n [DeferredDictLookup('__gym_encoder'),\n DeferredDictLookup('__address_encoder'),\n\ \ DeferredDictLookup('__timestamp_timeOfDay_encoder'),\n DeferredDictLookup('__timestamp_dayOfWeek_encoder'),\n\ \ DeferredDictLookup('__consumption_encoder')],\n\n permEncoderChoicesStr:\n\ \ For the permutations file, this defines the possible\n encoder dicts for\ \ each encoder. For example:\n '__timestamp_dayOfWeek_encoder': [\n \ \ None,\n {'fieldname':'timestamp',\n \ \ 'name': 'timestamp_timeOfDay',\n 'type':'DateEncoder'\n\ \ 'dayOfWeek': (7,1)\n },\n \ \ {'fieldname':'timestamp',\n 'name': 'timestamp_timeOfDay',\n\ \ 'type':'DateEncoder'\n 'dayOfWeek':\ \ (7,3)\n },\n ],\n\n '__field_consumption_encoder':\ \ [\n None,\n {'fieldname':'consumption',\n\ \ 'name': 'consumption',\n 'type':'AdaptiveScalarEncoder',\n\ \ 'n': 13,\n 'w': 7,\n \ \ }\n ]\n\n\n\n Parameters:\n --------------------------------------------------\n\ \ includedFields: item from the 'includedFields' section of the\n \ \ description JSON object. This is a list of dicts, each\n \ \ dict defining the field name, type, and optional min\n \ \ and max values.\n\n retval: (encoderSpecsStr, encoderSchemaStr permEncoderChoicesStr)\n\ \n\n \"\"\"\n\n # ------------------------------------------------------------------------\n\ \ # First accumulate the possible choices for each encoder\n encoderChoicesList\ \ = []\n for fieldInfo in includedFields:\n\n fieldName = fieldInfo['fieldName']\n\ \n # Get the list of encoder choices for this field\n (choicesList, aggFunction)\ \ = _generateEncoderChoicesV1(fieldInfo)\n encoderChoicesList.extend(choicesList)\n\ \n\n # ------------------------------------------------------------------------\n\ \ # Generate the string containing the encoder specs and encoder schema. See\n\ \ # the function comments for an example of the encoderSpecsStr and\n # encoderSchemaStr\n\ \ #\n encoderSpecsList = []\n for encoderChoices in encoderChoicesList:\n \ \ # Use the last choice as the default in the base file because the 1st is\n\ \ # often None\n encoder = encoderChoices[-1]\n\n # Check for bad characters\n\ \ for c in _ILLEGAL_FIELDNAME_CHARACTERS:\n if encoder['name'].find(c)\ \ >= 0:\n raise _ExpGeneratorException(\"Illegal character in field: %r\ \ (%r)\" % (\n c, encoder['name']))\n\n encoderSpecsList.append(\"\ %s: \\n%s%s\" % (\n _quoteAndEscape(encoder['name']),\n 2*_ONE_INDENT,\n\ \ pprint.pformat(encoder, indent=2*_INDENT_STEP)))\n\n encoderSpecsStr\ \ = ',\\n '.join(encoderSpecsList)\n\n\n # ------------------------------------------------------------------------\n\ \ # Generate the string containing the permutation encoder choices. See the\n\ \ # function comments above for an example of the permEncoderChoicesStr\n\n\ \ permEncoderChoicesList = []\n for encoderChoices in encoderChoicesList:\n\ \ permEncoderChoicesList.append(\"%s: %s,\" % (\n _quoteAndEscape(encoderChoices[-1]['name']),\n\ \ pprint.pformat(encoderChoices, indent=2*_INDENT_STEP)))\n permEncoderChoicesStr\ \ = '\\n'.join(permEncoderChoicesList)\n permEncoderChoicesStr = _indentLines(permEncoderChoicesStr,\ \ 1,\n indentFirstLine=False)\n\n # Return\ \ results\n return (encoderSpecsStr, permEncoderChoicesStr)" - "def shift(self, top=None, right=None, bottom=None, left=None):\n \"\"\"\ \n Shift/move the line strings from one or more image sides.\n\n \ \ Parameters\n ----------\n top : None or int, optional\n \ \ Amount of pixels by which to shift all bounding boxes from the\n \ \ top.\n\n right : None or int, optional\n Amount of pixels\ \ by which to shift all bounding boxes from the\n right.\n\n \ \ bottom : None or int, optional\n Amount of pixels by which to shift\ \ all bounding boxes from the\n bottom.\n\n left : None or int,\ \ optional\n Amount of pixels by which to shift all bounding boxes\ \ from the\n left.\n\n Returns\n -------\n imgaug.augmentables.lines.LineStringsOnImage\n\ \ Shifted line strings.\n\n \"\"\"\n lss_new = [ls.shift(top=top,\ \ right=right, bottom=bottom, left=left)\n for ls in self.line_strings]\n\ \ return LineStringsOnImage(lss_new, shape=self.shape)" - "def cross_entropy_reward_loss(logits, actions, rewards, name=None):\n \"\"\ \"Calculate the loss for Policy Gradient Network.\n\n Parameters\n ----------\n\ \ logits : tensor\n The network outputs without softmax. This function\ \ implements softmax inside.\n actions : tensor or placeholder\n The\ \ agent actions.\n rewards : tensor or placeholder\n The rewards.\n\n\ \ Returns\n --------\n Tensor\n The TensorFlow loss function.\n\ \n Examples\n ----------\n >>> states_batch_pl = tf.placeholder(tf.float32,\ \ shape=[None, D])\n >>> network = InputLayer(states_batch_pl, name='input')\n\ \ >>> network = DenseLayer(network, n_units=H, act=tf.nn.relu, name='relu1')\n\ \ >>> network = DenseLayer(network, n_units=3, name='out')\n >>> probs =\ \ network.outputs\n >>> sampling_prob = tf.nn.softmax(probs)\n >>> actions_batch_pl\ \ = tf.placeholder(tf.int32, shape=[None])\n >>> discount_rewards_batch_pl\ \ = tf.placeholder(tf.float32, shape=[None])\n >>> loss = tl.rein.cross_entropy_reward_loss(probs,\ \ actions_batch_pl, discount_rewards_batch_pl)\n >>> train_op = tf.train.RMSPropOptimizer(learning_rate,\ \ decay_rate).minimize(loss)\n\n \"\"\"\n cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=actions,\ \ logits=logits, name=name)\n\n return tf.reduce_sum(tf.multiply(cross_entropy,\ \ rewards))" - source_sentence: "Translate an index into coordinates, using the given coordinate\ \ system.\n\n Similar to ``numpy.unravel_index``.\n\n :param index: (int) The\ \ index of the point. The coordinates are expressed as a \n single index\ \ by using the dimensions as a mixed radix definition. For \n example,\ \ in dimensions 42x10, the point [1, 4] is index \n 1*420 + 4*10 = 460.\n\ \n :param dimensions (list of ints) The coordinate system.\n\n :returns: (list)\ \ of coordinates of length ``len(dimensions)``." sentences: - "def coordinatesFromIndex(index, dimensions):\n \"\"\"\n Translate an index\ \ into coordinates, using the given coordinate system.\n\n Similar to ``numpy.unravel_index``.\n\ \n :param index: (int) The index of the point. The coordinates are expressed\ \ as a \n single index by using the dimensions as a mixed radix definition.\ \ For \n example, in dimensions 42x10, the point [1, 4] is index \n \ \ 1*420 + 4*10 = 460.\n\n :param dimensions (list of ints) The coordinate\ \ system.\n\n :returns: (list) of coordinates of length ``len(dimensions)``.\n\ \ \"\"\"\n coordinates = [0] * len(dimensions)\n\n shifted = index\n for i\ \ in xrange(len(dimensions) - 1, 0, -1):\n coordinates[i] = shifted % dimensions[i]\n\ \ shifted = shifted / dimensions[i]\n\n coordinates[0] = shifted\n\n return\ \ coordinates" - "def step(self, observation, **extra_feed):\n \"\"\"\n Compute next\ \ action(s) given the observation(s)\n\n Parameters:\n ----------\n\ \n observation observation data (either single or a batch)\n\n \ \ **extra_feed additional data such as state or mask (names of the arguments\ \ should match the ones in constructor, see __init__)\n\n Returns:\n \ \ -------\n (action, value estimate, next state, negative log likelihood\ \ of the action under current policy parameters) tuple\n \"\"\"\n\n \ \ a, v, state, neglogp = self._evaluate([self.action, self.vf, self.state,\ \ self.neglogp], observation, **extra_feed)\n if state.size == 0:\n \ \ state = None\n return a, v, state, neglogp" - "def pretty_eta(seconds_left):\n \"\"\"Print the number of seconds in human\ \ readable format.\n\n Examples:\n 2 days\n 2 hours and 37 minutes\n\ \ less than a minute\n\n Paramters\n ---------\n seconds_left: int\n\ \ Number of seconds to be converted to the ETA\n Returns\n -------\n\ \ eta: str\n String representing the pretty ETA.\n \"\"\"\n minutes_left\ \ = seconds_left // 60\n seconds_left %= 60\n hours_left = minutes_left\ \ // 60\n minutes_left %= 60\n days_left = hours_left // 24\n hours_left\ \ %= 24\n\n def helper(cnt, name):\n return \"{} {}{}\".format(str(cnt),\ \ name, ('s' if cnt > 1 else ''))\n\n if days_left > 0:\n msg = helper(days_left,\ \ 'day')\n if hours_left > 0:\n msg += ' and ' + helper(hours_left,\ \ 'hour')\n return msg\n if hours_left > 0:\n msg = helper(hours_left,\ \ 'hour')\n if minutes_left > 0:\n msg += ' and ' + helper(minutes_left,\ \ 'minute')\n return msg\n if minutes_left > 0:\n return helper(minutes_left,\ \ 'minute')\n return 'less than a minute'" - source_sentence: Validates control dictionary for the experiment context sentences: - "def load_file_list(path=None, regx='\\.jpg', printable=True, keep_prefix=False):\n\ \ r\"\"\"Return a file list in a folder by given a path and regular expression.\n\ \n Parameters\n ----------\n path : str or None\n A folder path,\ \ if `None`, use the current directory.\n regx : str\n The regx of file\ \ name.\n printable : boolean\n Whether to print the files infomation.\n\ \ keep_prefix : boolean\n Whether to keep path in the file name.\n\n\ \ Examples\n ----------\n >>> file_list = tl.files.load_file_list(path=None,\ \ regx='w1pre_[0-9]+\\.(npz)')\n\n \"\"\"\n if path is None:\n path\ \ = os.getcwd()\n file_list = os.listdir(path)\n return_list = []\n for\ \ _, f in enumerate(file_list):\n if re.search(regx, f):\n return_list.append(f)\n\ \ # return_list.sort()\n if keep_prefix:\n for i, f in enumerate(return_list):\n\ \ return_list[i] = os.path.join(path, f)\n\n if printable:\n \ \ logging.info('Match file list = %s' % return_list)\n logging.info('Number\ \ of files = %d' % len(return_list))\n return return_list" - "def getCompletingSwarms(self):\n \"\"\"Return the list of all completing swarms.\n\ \n Parameters:\n ---------------------------------------------------------------------\n\ \ retval: list of active swarm Ids\n \"\"\"\n swarmIds = []\n for\ \ swarmId, info in self._state['swarms'].iteritems():\n if info['status']\ \ == 'completing':\n swarmIds.append(swarmId)\n\n return swarmIds" - "def __validateExperimentControl(self, control):\n \"\"\" Validates control\ \ dictionary for the experiment context\"\"\"\n # Validate task list\n taskList\ \ = control.get('tasks', None)\n if taskList is not None:\n taskLabelsList\ \ = []\n\n for task in taskList:\n validateOpfJsonValue(task, \"opfTaskSchema.json\"\ )\n validateOpfJsonValue(task['taskControl'], \"opfTaskControlSchema.json\"\ )\n\n taskLabel = task['taskLabel']\n\n assert isinstance(taskLabel,\ \ types.StringTypes), \\\n \"taskLabel type: %r\" % type(taskLabel)\n\ \ assert len(taskLabel) > 0, \"empty string taskLabel not is allowed\"\n\ \n taskLabelsList.append(taskLabel.lower())\n\n taskLabelDuplicates\ \ = filter(lambda x: taskLabelsList.count(x) > 1,\n \ \ taskLabelsList)\n assert len(taskLabelDuplicates) == 0, \\\n \ \ \"Duplcate task labels are not allowed: %s\" % taskLabelDuplicates\n\ \n return" pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on answerdotai/ModernBERT-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the code_search_net dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) - **Maximum Sequence Length:** 4096 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - code_search_net ### 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': 4096, 'do_lower_case': False}) with Transformer model: ModernBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("juanwisz/modernbert-python-code-retrieval") # Run inference sentences = [ 'Validates control dictionary for the experiment context', 'def __validateExperimentControl(self, control):\n """ Validates control dictionary for the experiment context"""\n # Validate task list\n taskList = control.get(\'tasks\', None)\n if taskList is not None:\n taskLabelsList = []\n\n for task in taskList:\n validateOpfJsonValue(task, "opfTaskSchema.json")\n validateOpfJsonValue(task[\'taskControl\'], "opfTaskControlSchema.json")\n\n taskLabel = task[\'taskLabel\']\n\n assert isinstance(taskLabel, types.StringTypes), \\\n "taskLabel type: %r" % type(taskLabel)\n assert len(taskLabel) > 0, "empty string taskLabel not is allowed"\n\n taskLabelsList.append(taskLabel.lower())\n\n taskLabelDuplicates = filter(lambda x: taskLabelsList.count(x) > 1,\n taskLabelsList)\n assert len(taskLabelDuplicates) == 0, \\\n "Duplcate task labels are not allowed: %s" % taskLabelDuplicates\n\n return', 'def load_file_list(path=None, regx=\'\\.jpg\', printable=True, keep_prefix=False):\n r"""Return a file list in a folder by given a path and regular expression.\n\n Parameters\n ----------\n path : str or None\n A folder path, if `None`, use the current directory.\n regx : str\n The regx of file name.\n printable : boolean\n Whether to print the files infomation.\n keep_prefix : boolean\n Whether to keep path in the file name.\n\n Examples\n ----------\n >>> file_list = tl.files.load_file_list(path=None, regx=\'w1pre_[0-9]+\\.(npz)\')\n\n """\n if path is None:\n path = os.getcwd()\n file_list = os.listdir(path)\n return_list = []\n for _, f in enumerate(file_list):\n if re.search(regx, f):\n return_list.append(f)\n # return_list.sort()\n if keep_prefix:\n for i, f in enumerate(return_list):\n return_list[i] = os.path.join(path, f)\n\n if printable:\n logging.info(\'Match file list = %s\' % return_list)\n logging.info(\'Number of files = %d\' % len(return_list))\n return return_list', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Dataset #### code_search_net * Dataset: code_search_net * Size: 412,178 training samples * Columns: query and positive * Approximate statistics based on the first 1000 samples: | | query | positive | |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | positive | |:------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Extracts the list of arguments that start with any of the specified prefix values | def findArgs(args, prefixes):
"""
Extracts the list of arguments that start with any of the specified prefix values
"""
return list([
arg for arg in args
if len([p for p in prefixes if arg.lower().startswith(p.lower())]) > 0
]) | | Removes any arguments in the supplied list that are contained in the specified blacklist | def stripArgs(args, blacklist):
"""
Removes any arguments in the supplied list that are contained in the specified blacklist
"""
blacklist = [b.lower() for b in blacklist]
return list([arg for arg in args if arg.lower() not in blacklist]) | | Executes a child process and captures its output | def capture(command, input=None, cwd=None, shell=False, raiseOnError=False):
"""
Executes a child process and captures its output
"""

# Attempt to execute the child process
proc = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd, shell=shell, universal_newlines=True)
(stdout, stderr) = proc.communicate(input)

# If the child process failed and we were asked to raise an exception, do so
if raiseOnError == True and proc.returncode != 0:
raise Exception(
'child process ' + str(command) +
' failed with exit code ' + str(proc.returncode) +
'\nstdout: "' + stdout + '"' +
'\nstderr: "' + stderr + '"'
)

return CommandOutput(proc.returncode, stdout, stderr) | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### code_search_net * Dataset: code_search_net * Size: 23,107 evaluation samples * Columns: query and positive * Approximate statistics based on the first 1000 samples: | | query | positive | |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | positive | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Train a deepq model.

Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of ra... | def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
callback=None,
load_path=None,
**network_kwargs
):
"""Train a deepq model.

Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the ... | | Save model to a pickle located at `path` | def save_act(self, path=None):
"""Save model to a pickle located at `path`"""
if path is None:
path = os.path.join(logger.get_dir(), "model.pkl")

with tempfile.TemporaryDirectory() as td:
save_variables(os.path.join(td, "model"))
arc_name = os.path.join(td, "packed.zip")
with zipfile.ZipFile(arc_name, 'w') as zipf:
for root, dirs, files in os.walk(td):
for fname in files:
file_path = os.path.join(root, fname)
if file_path != arc_name:
zipf.write(file_path, os.path.relpath(file_path, td))
with open(arc_name, "rb") as f:
model_data = f.read()
with open(path, "wb") as f:
cloudpickle.dump((model_data, self._act_params), f) | | CNN from Nature paper. | def nature_cnn(unscaled_images, **conv_kwargs):
"""
CNN from Nature paper.
"""
scaled_images = tf.cast(unscaled_images, tf.float32) / 255.
activ = tf.nn.relu
h = activ(conv(scaled_images, 'c1', nf=32, rf=8, stride=4, init_scale=np.sqrt(2),
**conv_kwargs))
h2 = activ(conv(h, 'c2', nf=64, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs))
h3 = activ(conv(h2, 'c3', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), **conv_kwargs))
h3 = conv_to_fc(h3)
return activ(fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2))) | * Loss: [MultipleNegativesRankingLoss](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 - `eval_strategy`: epoch - `per_device_train_batch_size`: 4 - `gradient_accumulation_steps`: 4 - `learning_rate`: 2e-05 - `num_train_epochs`: 10 - `warmup_steps`: 1000 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 4 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 1000 - `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 - `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 - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | |:------:|:-----:|:-------------:|:---------------:| | 0.0078 | 200 | 0.634 | - | | 0.0155 | 400 | 0.0046 | - | | 0.0233 | 600 | 0.0009 | - | | 0.0311 | 800 | 0.0004 | - | | 0.0388 | 1000 | 0.0001 | - | | 0.0466 | 1200 | 0.0002 | - | | 0.0543 | 1400 | 0.0001 | - | | 0.0621 | 1600 | 0.0001 | - | | 0.0699 | 1800 | 0.0001 | - | | 0.0776 | 2000 | 0.0 | - | | 0.0854 | 2200 | 0.0 | - | | 0.0932 | 2400 | 0.0 | - | | 0.1009 | 2600 | 0.0 | - | | 0.1087 | 2800 | 0.0005 | - | | 0.1165 | 3000 | 0.0005 | - | | 0.1242 | 3200 | 0.0002 | - | | 0.1320 | 3400 | 0.0 | - | | 0.1397 | 3600 | 0.0 | - | | 0.1475 | 3800 | 0.0 | - | | 0.1553 | 4000 | 0.0001 | - | | 0.1630 | 4200 | 0.0 | - | | 0.1708 | 4400 | 0.0001 | - | | 0.1786 | 4600 | 0.0001 | - | | 0.1863 | 4800 | 0.0 | - | | 0.1941 | 5000 | 0.0 | - | | 0.2019 | 5200 | 0.0 | - | | 0.2096 | 5400 | 0.0 | - | | 0.2174 | 5600 | 0.0 | - | | 0.2251 | 5800 | 0.0 | - | | 0.2329 | 6000 | 0.0004 | - | | 0.2407 | 6200 | 0.0 | - | | 0.2484 | 6400 | 0.0001 | - | | 0.2562 | 6600 | 0.0 | - | | 0.2640 | 6800 | 0.0 | - | | 0.2717 | 7000 | 0.0 | - | | 0.2795 | 7200 | 0.0 | - | | 0.2873 | 7400 | 0.0 | - | | 0.2950 | 7600 | 0.0 | - | | 0.3028 | 7800 | 0.0 | - | | 0.3105 | 8000 | 0.0 | - | | 0.3183 | 8200 | 0.0 | - | | 0.3261 | 8400 | 0.0004 | - | | 0.3338 | 8600 | 0.0 | - | | 0.3416 | 8800 | 0.0 | - | | 0.3494 | 9000 | 0.0 | - | | 0.3571 | 9200 | 0.0 | - | | 0.3649 | 9400 | 0.0 | - | | 0.3727 | 9600 | 0.0 | - | | 0.3804 | 9800 | 0.0 | - | | 0.3882 | 10000 | 0.0 | - | | 0.3959 | 10200 | 0.0 | - | | 0.4037 | 10400 | 0.0 | - | | 0.4115 | 10600 | 0.0 | - | | 0.4192 | 10800 | 0.0 | - | | 0.4270 | 11000 | 0.0 | - | | 0.4348 | 11200 | 0.0 | - | | 0.4425 | 11400 | 0.0 | - | | 0.4503 | 11600 | 0.0 | - | | 0.4581 | 11800 | 0.0 | - | | 0.4658 | 12000 | 0.0 | - | | 0.4736 | 12200 | 0.0 | - | | 0.4813 | 12400 | 0.0 | - | | 0.4891 | 12600 | 0.0005 | - | | 0.4969 | 12800 | 0.0 | - | | 0.5046 | 13000 | 0.0 | - | | 0.5124 | 13200 | 0.0001 | - | | 0.5202 | 13400 | 0.0 | - | | 0.5279 | 13600 | 0.0 | - | | 0.5357 | 13800 | 0.0 | - | | 0.5435 | 14000 | 0.0 | - | | 0.5512 | 14200 | 0.0 | - | | 0.5590 | 14400 | 0.0004 | - | | 0.5667 | 14600 | 0.0 | - | | 0.5745 | 14800 | 0.0 | - | | 0.5823 | 15000 | 0.0 | - | | 0.5900 | 15200 | 0.0 | - | | 0.5978 | 15400 | 0.0 | - | | 0.6056 | 15600 | 0.0 | - | | 0.6133 | 15800 | 0.0 | - | | 0.6211 | 16000 | 0.0 | - | | 0.6289 | 16200 | 0.0 | - | | 0.6366 | 16400 | 0.0006 | - | | 0.6444 | 16600 | 0.0 | - | | 0.6521 | 16800 | 0.0005 | - | | 0.6599 | 17000 | 0.0 | - | | 0.6677 | 17200 | 0.0 | - | | 0.6754 | 17400 | 0.0 | - | | 0.6832 | 17600 | 0.0 | - | | 0.6910 | 17800 | 0.0 | - | | 0.6987 | 18000 | 0.0005 | - | | 0.7065 | 18200 | 0.0001 | - | | 0.7143 | 18400 | 0.0 | - | | 0.7220 | 18600 | 0.0 | - | | 0.7298 | 18800 | 0.0 | - | | 0.7375 | 19000 | 0.0 | - | | 0.7453 | 19200 | 0.0 | - | | 0.7531 | 19400 | 0.0 | - | | 0.7608 | 19600 | 0.0 | - | | 0.7686 | 19800 | 0.0001 | - | | 0.7764 | 20000 | 0.0 | - | | 0.7841 | 20200 | 0.0 | - | | 0.7919 | 20400 | 0.0 | - | | 0.7997 | 20600 | 0.0004 | - | | 0.8074 | 20800 | 0.0 | - | | 0.8152 | 21000 | 0.0 | - | | 0.8229 | 21200 | 0.0 | - | | 0.8307 | 21400 | 0.0009 | - | | 0.8385 | 21600 | 0.0 | - | | 0.8462 | 21800 | 0.0 | - | | 0.8540 | 22000 | 0.0 | - | | 0.8618 | 22200 | 0.0 | - | | 0.8695 | 22400 | 0.0002 | - | | 0.8773 | 22600 | 0.0 | - | | 0.8851 | 22800 | 0.0 | - | | 0.8928 | 23000 | 0.0001 | - | | 0.9006 | 23200 | 0.0 | - | | 0.9083 | 23400 | 0.0 | - | | 0.9161 | 23600 | 0.0 | - | | 0.9239 | 23800 | 0.0 | - | | 0.9316 | 24000 | 0.0 | - | | 0.9394 | 24200 | 0.0 | - | | 0.9472 | 24400 | 0.0 | - | | 0.9549 | 24600 | 0.0 | - | | 0.9627 | 24800 | 0.0 | - | | 0.9704 | 25000 | 0.0 | - | | 0.9782 | 25200 | 0.0 | - | | 0.9860 | 25400 | 0.0 | - | | 0.9937 | 25600 | 0.0 | - | | 1.0 | 25762 | - | 0.0001 | | 1.0015 | 25800 | 0.0005 | - | | 1.0092 | 26000 | 0.0 | - | | 1.0170 | 26200 | 0.0 | - | | 1.0248 | 26400 | 0.0 | - | | 1.0325 | 26600 | 0.0 | - | | 1.0403 | 26800 | 0.0 | - | | 1.0481 | 27000 | 0.0 | - | | 1.0558 | 27200 | 0.0 | - | | 1.0636 | 27400 | 0.0 | - | | 1.0713 | 27600 | 0.0 | - | | 1.0791 | 27800 | 0.0 | - | | 1.0869 | 28000 | 0.0 | - | | 1.0946 | 28200 | 0.0 | - | | 1.1024 | 28400 | 0.0 | - | | 1.1102 | 28600 | 0.0 | - | | 1.1179 | 28800 | 0.0 | - | | 1.1257 | 29000 | 0.0 | - | | 1.1335 | 29200 | 0.0 | - | | 1.1412 | 29400 | 0.0 | - | | 1.1490 | 29600 | 0.0 | - | | 1.1567 | 29800 | 0.0 | - | | 1.1645 | 30000 | 0.0 | - | | 1.1723 | 30200 | 0.0 | - | | 1.1800 | 30400 | 0.0 | - | | 1.1878 | 30600 | 0.0 | - | | 1.1956 | 30800 | 0.0 | - | | 1.2033 | 31000 | 0.0 | - | | 1.2111 | 31200 | 0.0 | - | | 1.2189 | 31400 | 0.0 | - | | 1.2266 | 31600 | 0.0004 | - | | 1.2344 | 31800 | 0.0004 | - | | 1.2421 | 32000 | 0.0 | - | | 1.2499 | 32200 | 0.0 | - | | 1.2577 | 32400 | 0.0 | - | | 1.2654 | 32600 | 0.0 | - | | 1.2732 | 32800 | 0.0 | - | | 1.2810 | 33000 | 0.0 | - | | 1.2887 | 33200 | 0.0 | - | | 1.2965 | 33400 | 0.0 | - | | 1.3043 | 33600 | 0.0 | - | | 1.3120 | 33800 | 0.0 | - | | 1.3198 | 34000 | 0.0 | - | | 1.3275 | 34200 | 0.0 | - | | 1.3353 | 34400 | 0.0 | - | | 1.3431 | 34600 | 0.0 | - | | 1.3508 | 34800 | 0.0004 | - | | 1.3586 | 35000 | 0.0005 | - | | 1.3664 | 35200 | 0.0004 | - | | 1.3741 | 35400 | 0.0011 | - | | 1.3819 | 35600 | 0.0 | - | | 1.3897 | 35800 | 0.0 | - | | 1.3974 | 36000 | 0.0 | - | | 1.4052 | 36200 | 0.0 | - | | 1.4129 | 36400 | 0.0 | - | | 1.4207 | 36600 | 0.0 | - | | 1.4285 | 36800 | 0.0 | - | | 1.4362 | 37000 | 0.0 | - | | 1.4440 | 37200 | 0.0001 | - | | 1.4518 | 37400 | 0.0 | - | | 1.4595 | 37600 | 0.0 | - | | 1.4673 | 37800 | 0.0 | - | | 1.4751 | 38000 | 0.0 | - | | 1.4828 | 38200 | 0.0004 | - | | 1.4906 | 38400 | 0.0003 | - | | 1.4983 | 38600 | 0.0 | - | | 1.5061 | 38800 | 0.0 | - | | 1.5139 | 39000 | 0.0 | - | | 1.5216 | 39200 | 0.0 | - | | 1.5294 | 39400 | 0.0004 | - | | 1.5372 | 39600 | 0.0004 | - | | 1.5449 | 39800 | 0.0 | - | | 1.5527 | 40000 | 0.0 | - | | 1.5605 | 40200 | 0.0 | - | | 1.5682 | 40400 | 0.0 | - | | 1.5760 | 40600 | 0.0009 | - | | 1.5837 | 40800 | 0.0 | - | | 1.5915 | 41000 | 0.0009 | - | | 1.5993 | 41200 | 0.0 | - | | 1.6070 | 41400 | 0.0 | - | | 1.6148 | 41600 | 0.0 | - | | 1.6226 | 41800 | 0.0 | - | | 1.6303 | 42000 | 0.0 | - | | 1.6381 | 42200 | 0.0 | - | | 1.6459 | 42400 | 0.0 | - | | 1.6536 | 42600 | 0.0 | - | | 1.6614 | 42800 | 0.0 | - | | 1.6691 | 43000 | 0.0 | - | | 1.6769 | 43200 | 0.0 | - | | 1.6847 | 43400 | 0.0 | - | | 1.6924 | 43600 | 0.0 | - | | 1.7002 | 43800 | 0.0 | - | | 1.7080 | 44000 | 0.0 | - | | 1.7157 | 44200 | 0.0 | - | | 1.7235 | 44400 | 0.0 | - | | 1.7313 | 44600 | 0.0 | - | | 1.7390 | 44800 | 0.0 | - | | 1.7468 | 45000 | 0.0 | - | | 1.7545 | 45200 | 0.0 | - | | 1.7623 | 45400 | 0.0 | - | | 1.7701 | 45600 | 0.0 | - | | 1.7778 | 45800 | 0.0 | - | | 1.7856 | 46000 | 0.0 | - | | 1.7934 | 46200 | 0.0 | - | | 1.8011 | 46400 | 0.0 | - | | 1.8089 | 46600 | 0.0 | - | | 1.8167 | 46800 | 0.0 | - | | 1.8244 | 47000 | 0.0 | - | | 1.8322 | 47200 | 0.0 | - | | 1.8399 | 47400 | 0.0 | - | | 1.8477 | 47600 | 0.0 | - | | 1.8555 | 47800 | 0.0004 | - | | 1.8632 | 48000 | 0.0 | - | | 1.8710 | 48200 | 0.0 | - | | 1.8788 | 48400 | 0.0 | - | | 1.8865 | 48600 | 0.0 | - | | 1.8943 | 48800 | 0.0 | - | | 1.9021 | 49000 | 0.0004 | - | | 1.9098 | 49200 | 0.0 | - | | 1.9176 | 49400 | 0.0 | - | | 1.9253 | 49600 | 0.0004 | - | | 1.9331 | 49800 | 0.0 | - | | 1.9409 | 50000 | 0.0 | - | | 1.9486 | 50200 | 0.0 | - | | 1.9564 | 50400 | 0.0 | - | | 1.9642 | 50600 | 0.0004 | - | | 1.9719 | 50800 | 0.0 | - | | 1.9797 | 51000 | 0.0 | - | | 1.9875 | 51200 | 0.0 | - | | 1.9952 | 51400 | 0.0004 | - | | 2.0 | 51524 | - | 0.0001 | | 2.0030 | 51600 | 0.0 | - | | 2.0107 | 51800 | 0.0 | - | | 2.0185 | 52000 | 0.0 | - | | 2.0262 | 52200 | 0.0 | - | | 2.0340 | 52400 | 0.0004 | - | | 2.0418 | 52600 | 0.0004 | - | | 2.0495 | 52800 | 0.0 | - | | 2.0573 | 53000 | 0.0008 | - | | 2.0651 | 53200 | 0.0 | - | | 2.0728 | 53400 | 0.0 | - | | 2.0806 | 53600 | 0.0 | - | | 2.0883 | 53800 | 0.0 | - | | 2.0961 | 54000 | 0.0 | - | | 2.1039 | 54200 | 0.0 | - | | 2.1116 | 54400 | 0.0 | - | | 2.1194 | 54600 | 0.0 | - | | 2.1272 | 54800 | 0.0 | - | | 2.1349 | 55000 | 0.0 | - | | 2.1427 | 55200 | 0.0 | - | | 2.1505 | 55400 | 0.0 | - | | 2.1582 | 55600 | 0.0 | - | | 2.1660 | 55800 | 0.0 | - | | 2.1737 | 56000 | 0.0 | - | | 2.1815 | 56200 | 0.0 | - | | 2.1893 | 56400 | 0.0 | - | | 2.1970 | 56600 | 0.0 | - | | 2.2048 | 56800 | 0.0 | - | | 2.2126 | 57000 | 0.0 | - | | 2.2203 | 57200 | 0.0 | - | | 2.2281 | 57400 | 0.0 | - | | 2.2359 | 57600 | 0.0 | - | | 2.2436 | 57800 | 0.0 | - | | 2.2514 | 58000 | 0.0004 | - | | 2.2591 | 58200 | 0.0 | - | | 2.2669 | 58400 | 0.0004 | - | | 2.2747 | 58600 | 0.0 | - | | 2.2824 | 58800 | 0.0 | - | | 2.2902 | 59000 | 0.0 | - | | 2.2980 | 59200 | 0.0 | - | | 2.3057 | 59400 | 0.0 | - | | 2.3135 | 59600 | 0.0 | - | | 2.3213 | 59800 | 0.0004 | - | | 2.3290 | 60000 | 0.0 | - | | 2.3368 | 60200 | 0.0004 | - | | 2.3445 | 60400 | 0.0 | - | | 2.3523 | 60600 | 0.0 | - | | 2.3601 | 60800 | 0.0 | - | | 2.3678 | 61000 | 0.0 | - | | 2.3756 | 61200 | 0.0 | - | | 2.3834 | 61400 | 0.0 | - | | 2.3911 | 61600 | 0.0 | - | | 2.3989 | 61800 | 0.0 | - | | 2.4067 | 62000 | 0.0005 | - | | 2.4144 | 62200 | 0.0 | - | | 2.4222 | 62400 | 0.0 | - | | 2.4299 | 62600 | 0.0 | - | | 2.4377 | 62800 | 0.0 | - | | 2.4455 | 63000 | 0.0 | - | | 2.4532 | 63200 | 0.0 | - | | 2.4610 | 63400 | 0.0 | - | | 2.4688 | 63600 | 0.0 | - | | 2.4765 | 63800 | 0.0 | - | | 2.4843 | 64000 | 0.0 | - | | 2.4921 | 64200 | 0.0 | - | | 2.4998 | 64400 | 0.0 | - | | 2.5076 | 64600 | 0.0 | - | | 2.5153 | 64800 | 0.0 | - | | 2.5231 | 65000 | 0.0 | - | | 2.5309 | 65200 | 0.0 | - | | 2.5386 | 65400 | 0.0 | - | | 2.5464 | 65600 | 0.0004 | - | | 2.5542 | 65800 | 0.0 | - | | 2.5619 | 66000 | 0.0 | - | | 2.5697 | 66200 | 0.0 | - | | 2.5775 | 66400 | 0.0 | - | | 2.5852 | 66600 | 0.0 | - | | 2.5930 | 66800 | 0.0 | - | | 2.6007 | 67000 | 0.0 | - | | 2.6085 | 67200 | 0.0 | - | | 2.6163 | 67400 | 0.0 | - | | 2.6240 | 67600 | 0.0 | - | | 2.6318 | 67800 | 0.0 | - | | 2.6396 | 68000 | 0.0 | - | | 2.6473 | 68200 | 0.0 | - | | 2.6551 | 68400 | 0.0 | - | | 2.6629 | 68600 | 0.0 | - | | 2.6706 | 68800 | 0.0004 | - | | 2.6784 | 69000 | 0.0 | - | | 2.6861 | 69200 | 0.0 | - | | 2.6939 | 69400 | 0.0 | - | | 2.7017 | 69600 | 0.0004 | - | | 2.7094 | 69800 | 0.0004 | - | | 2.7172 | 70000 | 0.0 | - | | 2.7250 | 70200 | 0.0 | - | | 2.7327 | 70400 | 0.0 | - | | 2.7405 | 70600 | 0.0 | - | | 2.7483 | 70800 | 0.0 | - | | 2.7560 | 71000 | 0.0004 | - | | 2.7638 | 71200 | 0.0 | - | | 2.7715 | 71400 | 0.0 | - | | 2.7793 | 71600 | 0.0 | - | | 2.7871 | 71800 | 0.0 | - | | 2.7948 | 72000 | 0.0 | - | | 2.8026 | 72200 | 0.0 | - | | 2.8104 | 72400 | 0.0 | - | | 2.8181 | 72600 | 0.0 | - | | 2.8259 | 72800 | 0.0 | - | | 2.8337 | 73000 | 0.0004 | - | | 2.8414 | 73200 | 0.0 | - | | 2.8492 | 73400 | 0.0 | - | | 2.8569 | 73600 | 0.0 | - | | 2.8647 | 73800 | 0.0004 | - | | 2.8725 | 74000 | 0.0 | - | | 2.8802 | 74200 | 0.0 | - | | 2.8880 | 74400 | 0.0 | - | | 2.8958 | 74600 | 0.0 | - | | 2.9035 | 74800 | 0.0 | - | | 2.9113 | 75000 | 0.0 | - | | 2.9191 | 75200 | 0.0 | - | | 2.9268 | 75400 | 0.0004 | - | | 2.9346 | 75600 | 0.0 | - | | 2.9423 | 75800 | 0.0 | - | | 2.9501 | 76000 | 0.0 | - | | 2.9579 | 76200 | 0.0 | - | | 2.9656 | 76400 | 0.0 | - | | 2.9734 | 76600 | 0.0004 | - | | 2.9812 | 76800 | 0.0 | - | | 2.9889 | 77000 | 0.0 | - | | 2.9967 | 77200 | 0.0 | - | | 3.0 | 77286 | - | 0.0000 |
### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.3.1 - Transformers: 4.48.0 - PyTorch: 2.5.1+cu121 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```