This is a sentence-transformers model finetuned from benjamintli/modernbert-cosqa. 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: benjamintli/modernbert-cosqa
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'OptimizedModule'})
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("modernbert-codesearchnet")
# Run inference
queries = [
    "Split the data object along a given expression, in units.\n\n        Parameters\n        ----------\n        expression : int or str\n            The expression to split along. If given as an integer, the axis at that index\n            is used.\n        positions : number-type or 1D array-type\n            The position(s) to split at, in units.\n        units : str (optional)\n            The units of the given positions. Default is same, which assumes\n            input units are identical to first variable units.\n        parent : WrightTools.Collection (optional)\n            The parent collection in which to place the \u0027split\u0027 collection.\n            Default is a new Collection.\n        verbose : bool (optional)\n            Toggle talkback. Default is True.\n\n        Returns\n        -------\n        WrightTools.collection.Collection\n            A Collection of data objects.\n            The order of the objects is such that the axis points retain their original order.\n\n        See Also\n        --------\n        chop\n            Divide the dataset into its lower-dimensionality components.\n        collapse\n            Collapse the dataset along one axis.",
]
documents = [
    'def split(\n        self, expression, positions, *, units=None, parent=None, verbose=True\n    ) -> wt_collection.Collection:\n        """\n        Split the data object along a given expression, in units.\n\n        Parameters\n        ----------\n        expression : int or str\n            The expression to split along. If given as an integer, the axis at that index\n            is used.\n        positions : number-type or 1D array-type\n            The position(s) to split at, in units.\n        units : str (optional)\n            The units of the given positions. Default is same, which assumes\n            input units are identical to first variable units.\n        parent : WrightTools.Collection (optional)\n            The parent collection in which to place the \'split\' collection.\n            Default is a new Collection.\n        verbose : bool (optional)\n            Toggle talkback. Default is True.\n\n        Returns\n        -------\n        WrightTools.collection.Collection\n            A Collection of data objects.\n            The order of the objects is such that the axis points retain their original order.\n\n        See Also\n        --------\n        chop\n            Divide the dataset into its lower-dimensionality components.\n        collapse\n            Collapse the dataset along one axis.\n        """\n        # axis ------------------------------------------------------------------------------------\n        old_expr = self.axis_expressions\n        old_units = self.units\n        out = wt_collection.Collection(name="split", parent=parent)\n        if isinstance(expression, int):\n            if units is None:\n                units = self._axes[expression].units\n            expression = self._axes[expression].expression\n        elif isinstance(expression, str):\n            pass\n        else:\n            raise TypeError("expression: expected {int, str}, got %s" % type(expression))\n\n        self.transform(expression)\n        if units:\n            self.convert(units)\n\n        try:\n            positions = [-np.inf] + sorted(list(positions)) + [np.inf]\n        except TypeError:\n            positions = [-np.inf, positions, np.inf]\n\n        values = self._axes[0].full\n        masks = [(values >= lo) & (values < hi) for lo, hi in wt_kit.pairwise(positions)]\n        omasks = []\n        cuts = []\n        for mask in masks:\n            try:\n                omasks.append(wt_kit.mask_reduce(mask))\n                cuts.append([i == 1 for i in omasks[-1].shape])\n                # Ensure at least one axis is kept\n                if np.all(cuts[-1]):\n                    cuts[-1][0] = False\n            except ValueError:\n                omasks.append(None)\n                cuts.append(None)\n        for i in range(len(positions) - 1):\n            out.create_data("split%03i" % i)\n\n        for var in self.variables:\n            for i, (imask, omask, cut) in enumerate(zip(masks, omasks, cuts)):\n                if omask is None:\n                    # Zero length split\n                    continue\n                omask = wt_kit.enforce_mask_shape(omask, var.shape)\n                omask.shape = tuple([s for s, c in zip(omask.shape, cut) if not c])\n                out_arr = np.full(omask.shape, np.nan)\n                imask = wt_kit.enforce_mask_shape(imask, var.shape)\n                out_arr[omask] = var[:][imask]\n                out[i].create_variable(values=out_arr, **var.attrs)\n\n        for ch in self.channels:\n            for i, (imask, omask, cut) in enumerate(zip(masks, omasks, cuts)):\n                if omask is None:\n                    # Zero length split\n                    continue\n                omask = wt_kit.enforce_mask_shape(omask, ch.shape)\n                omask.shape = tuple([s for s, c in zip(omask.shape, cut) if not c])\n                out_arr = np.full(omask.shape, np.nan)\n                imask = wt_kit.enforce_mask_shape(imask, ch.shape)\n                out_arr[omask] = ch[:][imask]\n                out[i].create_channel(values=out_arr, **ch.attrs)\n\n        if verbose:\n            for d in out.values():\n                try:\n                    d.transform(expression)\n                except IndexError:\n                    continue\n\n            print("split data into {0} pieces along <{1}>:".format(len(positions) - 1, expression))\n            for i, (lo, hi) in enumerate(wt_kit.pairwise(positions)):\n                new_data = out[i]\n                if new_data.shape == ():\n                    print("  {0} : None".format(i))\n                else:\n                    new_axis = new_data.axes[0]\n                    print(\n                        "  {0} : {1:0.2f} to {2:0.2f} {3} {4}".format(\n                            i, lo, hi, new_axis.units, new_axis.shape\n                        )\n                    )\n\n        for d in out.values():\n            try:\n                d.transform(*old_expr)\n                keep = []\n                keep_units = []\n                for ax in d.axes:\n                    if ax.size > 1:\n                        keep.append(ax.expression)\n                        keep_units.append(ax.units)\n                    else:\n                        d.create_constant(ax.expression, verbose=False)\n                d.transform(*keep)\n                for ax, u in zip(d.axes, keep_units):\n                    ax.convert(u)\n            except IndexError:\n                continue\n            tempax = Axis(d, expression)\n            if all(\n                np.all(\n                    np.sum(~np.isnan(tempax.masked), axis=tuple(set(range(tempax.ndim)) - {j}))\n                    <= 1\n                )\n                for j in range(tempax.ndim)\n            ):\n                d.create_constant(expression, verbose=False)\n        self.transform(*old_expr)\n        for ax, u in zip(self.axes, old_units):\n            ax.convert(u)\n\n        return out',
    'def add_item(self, title, key, synonyms=None, description=None, img_url=None):\n        """Adds item to a list or carousel card.\n\n        A list must contain at least 2 items, each requiring a title and object key.\n\n        Arguments:\n            title {str} -- Name of the item object\n            key {str} -- Key refering to the item.\n                        This string will be used to send a query to your app if selected\n\n        Keyword Arguments:\n            synonyms {list} -- Words and phrases the user may send to select the item\n                              (default: {None})\n            description {str} -- A description of the item (default: {None})\n            img_url {str} -- URL of the image to represent the item (default: {None})\n        """\n        item = build_item(title, key, synonyms, description, img_url)\n        self._items.append(item)\n        return self',
    'def compare(a, b):\n    """Compares two timestamps.\n\n    ``a`` and ``b`` must be the same type, in addition to normal\n    representations of timestamps that order naturally, they can be rfc3339\n    formatted strings.\n\n    Args:\n      a (string|object): a timestamp\n      b (string|object): another timestamp\n\n    Returns:\n      int: -1 if a < b, 0 if a == b or 1 if a > b\n\n    Raises:\n      ValueError: if a or b are not the same type\n      ValueError: if a or b strings but not in valid rfc3339 format\n\n    """\n    a_is_text = isinstance(a, basestring)\n    b_is_text = isinstance(b, basestring)\n    if type(a) != type(b) and not (a_is_text and b_is_text):\n        _logger.error(u\'Cannot compare %s to %s, types differ %s!=%s\',\n                      a, b, type(a), type(b))\n        raise ValueError(u\'cannot compare inputs of differing types\')\n\n    if a_is_text:\n        a = from_rfc3339(a, with_nanos=True)\n        b = from_rfc3339(b, with_nanos=True)\n\n    if a < b:\n        return -1\n    elif a > b:\n        return 1\n    else:\n        return 0',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.9188, 0.1817, 0.1583]])

Evaluation

Metrics

Information Retrieval

Dataset: eval
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9481
cosine_accuracy@3	0.9703
cosine_accuracy@5	0.9752
cosine_accuracy@10	0.9807
cosine_precision@1	0.9481
cosine_precision@3	0.3234
cosine_precision@5	0.195
cosine_precision@10	0.0981
cosine_recall@1	0.9481
cosine_recall@3	0.9703
cosine_recall@5	0.9752
cosine_recall@10	0.9807
cosine_ndcg@10	0.9652
cosine_mrr@10	0.9602
cosine_map@100	0.9606

Training Details

Training Dataset

Unnamed Dataset

Size: 369,762 training samples
Columns: query and positive
Approximate statistics based on the first 1000 samples:
query positive
type string string
details
min: 3 tokens
mean: 71.9 tokens
max: 512 tokens
min: 37 tokens
mean: 236.1 tokens
max: 512 tokens

Samples:

query	positive
`Returns group object for datacenter root group.[object Object][object Object] >>> clc.v2.Datacenter().RootGroup()[object Object] <clc.APIv2.group.Group object at 0x105feacd0>[object Object] >>> print _[object Object] WA1 Hardware`	`def RootGroup(self):[object Object] """Returns group object for datacenter root group.[object Object][object Object] >>> clc.v2.Datacenter().RootGroup()[object Object] <clc.APIv2.group.Group object at 0x105feacd0>[object Object] >>> print _[object Object] WA1 Hardware[object Object][object Object] """[object Object][object Object] return(clc.v2.Group(id=self.root_group_id,alias=self.alias,session=self.session))`
Calculate the euclidean distance of all array positions in "matchArr".[object Object][object Object] :param matchArr: a dictionary of [object Object] containing at least two[object Object] entries that are treated as cartesian coordinates.[object Object] :param tKey: #TODO: docstring[object Object] :param mKey: #TODO: docstring[object Object][object Object] :returns: #TODO: docstring[object Object][object Object] {'eucDist': numpy.array([eucDistance, eucDistance, ...]),[object Object] 'posPairs': numpy.array([[pos1, pos2], [pos1, pos2], ...])[object Object] }	def calcDistMatchArr(matchArr, tKey, mKey):[object Object] """Calculate the euclidean distance of all array positions in "matchArr".[object Object][object Object] :param matchArr: a dictionary of [object Object] containing at least two[object Object] entries that are treated as cartesian coordinates.[object Object] :param tKey: #TODO: docstring[object Object] :param mKey: #TODO: docstring[object Object][object Object] :returns: #TODO: docstring[object Object][object Object] {'eucDist': numpy.array([eucDistance, eucDistance, ...]),[object Object] 'posPairs': numpy.array([[pos1, pos2], [pos1, pos2], ...])[object Object] }[object Object] """[object Object] #Calculate all sorted list of all eucledian feature distances[object Object] matchArrSize = listvalues(matchArr)[0].size[object Object][object Object] distInfo = {'posPairs': list(), 'eucDist': list()}[object Object] _matrix = numpy.swapaxes(numpy.array([matchArr[tKey], matchArr[mKey]]), 0, 1)[object Object][object Object] for pos1 in range(matchArrSize-1):[object Object] for pos2 in range(pos1+1, matchArrSize):[object Object] distInfo['posPairs'].append((pos1, pos2))[object Object] distInfo['posPairs'] = numpy.array(distInfo['posPairs'])[object Object] distInfo['eucD...
`Format this verifier[object Object][object Object] Returns:[object Object] string: A formatted string`	def format(self, indent_level, indent_size=4):[object Object] """Format this verifier[object Object][object Object] Returns:[object Object] string: A formatted string[object Object] """[object Object][object Object] name = self.format_name('Literal', indent_size)[object Object][object Object] if self.long_desc is not None:[object Object] name += '\n'[object Object][object Object] name += self.wrap_lines('value: %s\n' % str(self._literal), 1, indent_size)[object Object][object Object] return self.wrap_lines(name, indent_level, indent_size)

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "mini_batch_size": 64, 5 "gather_across_devices": false, 6 "directions": [ 7 "query_to_doc" 8 ], 9 "partition_mode": "joint", 10 "hardness_mode": null, 11 "hardness_strength": 0.0 12}

Evaluation Dataset

Unnamed Dataset

Size: 19,462 evaluation samples
Columns: query and positive
Approximate statistics based on the first 1000 samples:
query positive
type string string
details
min: 3 tokens
mean: 71.05 tokens
max: 512 tokens
min: 40 tokens
mean: 236.22 tokens
max: 512 tokens

Samples:

query	positive
Create a new ParticipantInstance[object Object][object Object] :param unicode attributes: An optional string metadata field you can use to store any data you wish.[object Object] :param unicode twilio_address: The address of the Twilio phone number that the participant is in contact with.[object Object] :param datetime date_created: The date that this resource was created.[object Object] :param datetime date_updated: The date that this resource was last updated.[object Object] :param unicode identity: A unique string identifier for the session participant as Chat User.[object Object] :param unicode user_address: The address of the participant's device.[object Object][object Object] :returns: Newly created ParticipantInstance[object Object] :rtype: twilio.rest.messaging.v1.session.participant.ParticipantInstance	def create(self, attributes=values.unset, twilio_address=values.unset,[object Object] date_created=values.unset, date_updated=values.unset,[object Object] identity=values.unset, user_address=values.unset):[object Object] """[object Object] Create a new ParticipantInstance[object Object][object Object] :param unicode attributes: An optional string metadata field you can use to store any data you wish.[object Object] :param unicode twilio_address: The address of the Twilio phone number that the participant is in contact with.[object Object] :param datetime date_created: The date that this resource was created.[object Object] :param datetime date_updated: The date that this resource was last updated.[object Object] :param unicode identity: A unique string identifier for the session participant as Chat User.[object Object] :param unicode user_address: The address of the participant's device.[object Object][object Object] :returns: Newly created ParticipantInstance[object Object] :rtype: twilio.rest.messaging.v1.session.participant.ParticipantInstance[object Object] """[object Object] data = values.o...
`It returns absolute url defined by node related to this page`	def get_absolute_url(self):[object Object] """[object Object] It returns absolute url defined by node related to this page[object Object] """[object Object] try:[object Object] node = Node.objects.select_related().filter(page=self)[0][object Object] return node.get_absolute_url()[object Object] except Exception, e:[object Object] raise ValueError(u"Error in {0}.{1}: {2}".format(self.[object Object], self.[object Object].[object Object], e))[object Object] return u""
`Return the current scaled font.[object Object][object Object] :return:[object Object] A new :class:[object Object] object,[object Object] wrapping an existing cairo object.`	`def get_scaled_font(self):[object Object] """Return the current scaled font.[object Object][object Object] :return:[object Object] A new :class:[object Object] object,[object Object] wrapping an existing cairo object.[object Object][object Object] """[object Object] return ScaledFont._from_pointer([object Object] cairo.cairo_get_scaled_font(self._pointer), incref=True)`

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "mini_batch_size": 64, 5 "gather_across_devices": false, 6 "directions": [ 7 "query_to_doc" 8 ], 9 "partition_mode": "joint", 10 "hardness_mode": null, 11 "hardness_strength": 0.0 12}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 8192
num_train_epochs: 1
learning_rate: 2e-06
warmup_steps: 0.1
bf16: True
eval_strategy: epoch
per_device_eval_batch_size: 8192
push_to_hub: True
hub_model_id: modernbert-codesearchnet
load_best_model_at_end: True
dataloader_num_workers: 4
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

per_device_train_batch_size: 8192
num_train_epochs: 1
max_steps: -1
learning_rate: 2e-06
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: trackio
eval_strategy: epoch
per_device_eval_batch_size: 8192
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: True
hub_private_repo: None
hub_model_id: modernbert-codesearchnet
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: True
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 4
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	eval_cosine_ndcg@10
0.2174	10	0.9210	-	-
0.4348	20	0.6679	-	-
0.6522	30	0.5007	-	-
0.8696	40	0.4181	-	-
1.0	46	-	0.0328	0.9652

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.3.0
Transformers: 5.3.0
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0
Datasets: 4.8.2
Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

bibtex

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

CachedMultipleNegativesRankingLoss

bibtex

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

	query	positive
type	string	string
details	min: 3 tokens mean: 71.9 tokens max: 512 tokens	min: 37 tokens mean: 236.1 tokens max: 512 tokens

benjamintli/modernbert-codesearchnet

SentenceTransformer based on benjamintli/modernbert-cosqa

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

CachedMultipleNegativesRankingLoss

benjamintli/
modernbert-codesearchnet