benjamintli/modernbert-codesearchnet

SentenceTransformer based on benjamintli/modernbert-cosqa

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 <!-- at revision 8d7c40aabc62d4956cab19aec28165b206d86790 -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

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.

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]])

<!--

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. -->

Evaluation

Metrics

Information Retrieval

• —Dataset: eval
• —Evaluated with <code>InformationRetrievalEvaluator</code>

<!--

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

Unnamed Dataset

• —Size: 369,762 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: 3 tokens</li><li>mean: 71.9 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 37 tokens</li><li>mean: 236.1 tokens</li><li>max: 512 tokens</li></ul> |
• —Samples: | query | positive | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Returns group object for datacenter root group.<br><br> >>> clc.v2.Datacenter().RootGroup()<br> <clc.APIv2.group.Group object at 0x105feacd0><br> >>> print <br> WA1 Hardware</code> | <code>def RootGroup(self):<br> """Returns group object for datacenter root group.<br><br> >>> clc.v2.Datacenter().RootGroup()<br> <clc.APIv2.group.Group object at 0x105feacd0><br> >>> print <br> WA1 Hardware<br><br> """<br><br> return(clc.v2.Group(id=self.rootgroupid,alias=self.alias,session=self.session))</code> | | <code>Calculate the euclidean distance of all array positions in "matchArr".<br><br> :param matchArr: a dictionary of `numpy.arrays containing at least two<br> entries that are treated as cartesian coordinates.<br> :param tKey: #TODO: docstring<br> :param mKey: #TODO: docstring<br><br> :returns: #TODO: docstring<br><br> {'eucDist': numpy.array([eucDistance, eucDistance, ...]),<br> 'posPairs': numpy.array([[pos1, pos2], [pos1, pos2], ...])<br> }</code> | <code>def calcDistMatchArr(matchArr, tKey, mKey):<br> """Calculate the euclidean distance of all array positions in "matchArr".<br><br> :param matchArr: a dictionary of numpy.arrays` containing at least two<br> entries that are treated as cartesian coordinates.<br> :param tKey: #TODO: docstring<br> :param mKey: #TODO: docstring<br><br> :returns: #TODO: docstring<br><br> {'eucDist': numpy.array([eucDistance, eucDistance, ...]),<br> 'posPairs': numpy.array([[pos1, pos2], [pos1, pos2], ...])<br> }<br> """<br> #Calculate all sorted list of all eucledian feature distances<br> matchArrSize = listvalues(matchArr)[0].size<br><br> distInfo = {'posPairs': list(), 'eucDist': list()}<br> matrix = numpy.swapaxes(numpy.array([matchArr[tKey], matchArr[mKey]]), 0, 1)<br><br> for pos1 in range(matchArrSize-1):<br> for pos2 in range(pos1+1, matchArrSize):<br> distInfo['posPairs'].append((pos1, pos2))<br> distInfo['posPairs'] = numpy.array(distInfo['posPairs'])<br> distInfo['eucD...</code> | | <code>Format this verifier<br><br> Returns:<br> string: A formatted string</code> | <code>def format(self, indentlevel, indentsize=4):<br> """Format this verifier<br><br> Returns:<br> string: A formatted string<br> """<br><br> name = self.formatname('Literal', indentsize)<br><br> if self.longdesc is not None:<br> name += '\n'<br><br> name += self.wraplines('value: %s\n' % str(self.literal), 1, indentsize)<br><br> return self.wraplines(name, indentlevel, indentsize)</code> |
• —Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 64,
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }

Evaluation Dataset

Unnamed Dataset

• —Size: 19,462 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: 3 tokens</li><li>mean: 71.05 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 40 tokens</li><li>mean: 236.22 tokens</li><li>max: 512 tokens</li></ul> |
• —Samples: | query | positive | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Create a new ParticipantInstance<br><br> :param unicode attributes: An optional string metadata field you can use to store any data you wish.<br> :param unicode twilioaddress: The address of the Twilio phone number that the participant is in contact with.<br> :param datetime datecreated: The date that this resource was created.<br> :param datetime dateupdated: The date that this resource was last updated.<br> :param unicode identity: A unique string identifier for the session participant as Chat User.<br> :param unicode useraddress: The address of the participant's device.<br><br> :returns: Newly created ParticipantInstance<br> :rtype: twilio.rest.messaging.v1.session.participant.ParticipantInstance</code> | <code>def create(self, attributes=values.unset, twilioaddress=values.unset,<br> datecreated=values.unset, dateupdated=values.unset,<br> identity=values.unset, useraddress=values.unset):<br> """<br> Create a new ParticipantInstance<br><br> :param unicode attributes: An optional string metadata field you can use to store any data you wish.<br> :param unicode twilioaddress: The address of the Twilio phone number that the participant is in contact with.<br> :param datetime datecreated: The date that this resource was created.<br> :param datetime dateupdated: The date that this resource was last updated.<br> :param unicode identity: A unique string identifier for the session participant as Chat User.<br> :param unicode useraddress: The address of the participant's device.<br><br> :returns: Newly created ParticipantInstance<br> :rtype: twilio.rest.messaging.v1.session.participant.ParticipantInstance<br> """<br> data = values.o...</code> | | <code>It returns absolute url defined by node related to this page</code> | <code>def getabsoluteurl(self):<br> """<br> It returns absolute url defined by node related to this page<br> """<br> try:<br> node = Node.objects.selectrelated().filter(page=self)[0]<br> return node.getabsoluteurl()<br> except Exception, e:<br> raise ValueError(u"Error in {0}.{1}: {2}".format(self.module, self.class.name, e))<br> return u""</code> | | <code>Return the current scaled font.<br><br> :return:<br> A new :class:`ScaledFont` object,<br> wrapping an existing cairo object.</code> | <code>def getscaledfont(self):<br> """Return the current scaled font.<br><br> :return:<br> A new :class:`ScaledFont` object,<br> wrapping an existing cairo object.<br><br> """<br> return ScaledFont.frompointer(<br> cairo.cairogetscaledfont(self._pointer), incref=True)</code> |
• —Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 64,
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }

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

<details><summary>Click to expand</summary>

• —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: adamwtorchfused
• —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: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_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: {}

</details>

Training Logs

• —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

@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

@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}
}

<!--

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. -->