Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,1107 @@
+---
+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) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
+- **Maximum Sequence Length:** 4096 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - code_search_net
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### code_search_net
+
+* Dataset: code_search_net
+* Size: 412,178 training samples
+* Columns: <code>query</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                               | positive                                                                              |
+  |:--------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                                |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 73.72 tokens</li><li>max: 2258 tokens</li></ul> | <ul><li>min: 46 tokens</li><li>mean: 300.87 tokens</li><li>max: 3119 tokens</li></ul> |
+* Samples:
+  | query                                                                                                 | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+  |:------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Extracts the list of arguments that start with any of the specified prefix values</code>        | <code>def findArgs(args, prefixes):<br>		"""<br>		Extracts the list of arguments that start with any of the specified prefix values<br>		"""<br>		return list([<br>			arg for arg in args<br>			if len([p for p in prefixes if arg.lower().startswith(p.lower())]) > 0<br>		])</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+  | <code>Removes any arguments in the supplied list that are contained in the specified blacklist</code> | <code>def stripArgs(args, blacklist):<br>		"""<br>		Removes any arguments in the supplied list that are contained in the specified blacklist<br>		"""<br>		blacklist = [b.lower() for b in blacklist]<br>		return list([arg for arg in args if arg.lower() not in blacklist])</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  | <code>Executes a child process and captures its output</code>                                         | <code>def capture(command, input=None, cwd=None, shell=False, raiseOnError=False):<br>		"""<br>		Executes a child process and captures its output<br>		"""<br>		<br>		# Attempt to execute the child process<br>		proc = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd, shell=shell, universal_newlines=True)<br>		(stdout, stderr) = proc.communicate(input)<br>		<br>		# If the child process failed and we were asked to raise an exception, do so<br>		if raiseOnError == True and proc.returncode != 0:<br>			raise Exception(<br>				'child process ' + str(command) +<br>				' failed with exit code ' + str(proc.returncode) +<br>				'\nstdout: "' + stdout + '"' +<br>				'\nstderr: "' + stderr + '"'<br>			)<br>		<br>		return CommandOutput(proc.returncode, stdout, stderr)</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### code_search_net
+
+* Dataset: code_search_net
+* Size: 23,107 evaluation samples
+* Columns: <code>query</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | query                                                                                | positive                                                                             |
+  |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                               | string                                                                               |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 168.27 tokens</li><li>max: 2118 tokens</li></ul> | <ul><li>min: 48 tokens</li><li>mean: 467.9 tokens</li><li>max: 4096 tokens</li></ul> |
+* Samples:
+  | query                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Train a deepq model.<br><br>    Parameters<br>    -------<br>    env: gym.Env<br>        environment to train on<br>    network: string or a function<br>        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<br>        (mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which<br>        will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)<br>    seed: int or None<br>        prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.<br>    lr: float<br>        learning rate for adam optimizer<br>    total_timesteps: int<br>        number of env steps to optimizer for<br>    buffer_size: int<br>        size of the replay buffer<br>    exploration_fraction: float<br>        fraction of entire training period over which the exploration rate is annealed<br>    exploration_final_eps: float<br>        final value of ra...</code> | <code>def learn(env,<br>          network,<br>          seed=None,<br>          lr=5e-4,<br>          total_timesteps=100000,<br>          buffer_size=50000,<br>          exploration_fraction=0.1,<br>          exploration_final_eps=0.02,<br>          train_freq=1,<br>          batch_size=32,<br>          print_freq=100,<br>          checkpoint_freq=10000,<br>          checkpoint_path=None,<br>          learning_starts=1000,<br>          gamma=1.0,<br>          target_network_update_freq=500,<br>          prioritized_replay=False,<br>          prioritized_replay_alpha=0.6,<br>          prioritized_replay_beta0=0.4,<br>          prioritized_replay_beta_iters=None,<br>          prioritized_replay_eps=1e-6,<br>          param_noise=False,<br>          callback=None,<br>          load_path=None,<br>          **network_kwargs<br>            ):<br>    """Train a deepq model.<br><br>    Parameters<br>    -------<br>    env: gym.Env<br>        environment to train on<br>    network: string or a function<br>        neural network to use as a q function approximator. If string, has to be one of the ...</code> |
+  | <code>Save model to a pickle located at `path`</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | <code>def save_act(self, path=None):<br>        """Save model to a pickle located at `path`"""<br>        if path is None:<br>            path = os.path.join(logger.get_dir(), "model.pkl")<br><br>        with tempfile.TemporaryDirectory() as td:<br>            save_variables(os.path.join(td, "model"))<br>            arc_name = os.path.join(td, "packed.zip")<br>            with zipfile.ZipFile(arc_name, 'w') as zipf:<br>                for root, dirs, files in os.walk(td):<br>                    for fname in files:<br>                        file_path = os.path.join(root, fname)<br>                        if file_path != arc_name:<br>                            zipf.write(file_path, os.path.relpath(file_path, td))<br>            with open(arc_name, "rb") as f:<br>                model_data = f.read()<br>        with open(path, "wb") as f:<br>            cloudpickle.dump((model_data, self._act_params), f)</code>                                                                                                                                                                                                 |
+  | <code>CNN from Nature paper.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>def nature_cnn(unscaled_images, **conv_kwargs):<br>    """<br>    CNN from Nature paper.<br>    """<br>    scaled_images = tf.cast(unscaled_images, tf.float32) / 255.<br>    activ = tf.nn.relu<br>    h = activ(conv(scaled_images, 'c1', nf=32, rf=8, stride=4, init_scale=np.sqrt(2),<br>                   **conv_kwargs))<br>    h2 = activ(conv(h, 'c2', nf=64, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs))<br>    h3 = activ(conv(h2, 'c3', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), **conv_kwargs))<br>    h3 = conv_to_fc(h3)<br>    return activ(fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2)))</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `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
+<details><summary>Click to expand</summary>
+
+- `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
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| 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          |
+
+</details>
+
+### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "./modernBERT-python-code-retrieval",
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
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+  "classifier_pooling": "mean",
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+  "decoder_bias": true,
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+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "reference_compile": true,
+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.1",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.48.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+oid sha256:8763110e75db040058b130b75e405d154741ee7768e42f001b0126d13695c802
+size 596070136

modules.json

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+[
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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
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+  "do_lower_case": false
+}

special_tokens_map.json

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