SentenceTransformer based on answerdotai/ModernBERT-base

This is a sentence-transformers model finetuned from answerdotai/ModernBERT-base on the code-retrieval-combined 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
• Maximum Sequence Length: 1024 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • code-retrieval-combined

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': 1024, '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-code")
# Run inference
queries = [
    "function onActiveEditorChanged(event, current, previous) {\n        if (current \u0026\u0026 !current._codeMirror._lineFolds) {\n            enableFoldingInEditor(current);\n   ",
]
documents = [
    '     }\n        if (previous) {\n            saveLineFolds(previous);\n        }\n    }',
    'Save config data.\n\n@param string $path\n@param string $value\n@param string $scope\n@param int $scopeId\n\n@return null',
    'Get playback settings such as shuffle and repeat.',
]
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([[0.6443, 0.0381, 0.0291]])

Evaluation

Metrics

Information Retrieval

• Dataset: eval
• Evaluated with InformationRetrievalEvaluator

Training Details

Training Dataset

code-retrieval-combined

• Dataset: code-retrieval-combined at 4403b52
• Size: 193,623 training samples
• Columns: query and positive
• Approximate statistics based on the first 1000 samples:

  	query	positive
  type	string	string
  details	min: 6 tokensmean: 143.24 tokensmax: 1024 tokens	min: 5 tokensmean: 64.75 tokensmax: 937 tokens

• Samples:

  query	positive
  protected function sendMusicMsgToJsonString(WxSendMusicMsg $msg) { $formatStr = '{ "touser":"%s", "msgtype":"%s", "music": { "title":"%s", "description":"%s", "musicurl":"%s", "hqmusicurl":"%s", "thumb_media_id":"%s" } }'; $result = sprintf($formatStr, $msg->getToUserName(), $msg->getMsgType(), $msg->getTitle(), $msg->getDescription(), $msg->getMusicUrl(), $msg->getHQMusicUrl(), $msg->getThumbMediaId() ); return $result; }	formatter WxSendMusicMsg to Json string@param WxSendMusicMsg $msg@return string
  def getBlocks(self): """ Get the blocks that need to be migrated """ try: conn = self.dbi.connection() result =	self.buflistblks.execute(conn) return result finally: if conn: conn.close()
  function obj(/key,value, key,value .../) { var result = {} for(var n=0; n<arguments.length; n+=2) { result[arguments[n]] = arguments[n+1] } return result}	builds an object immediate where keys can be expressions

• Loss: CachedMultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 128,
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Evaluation Dataset

code-retrieval-combined

• Dataset: code-retrieval-combined at 4403b52
• Size: 21,514 evaluation samples
• Columns: query and positive
• Approximate statistics based on the first 1000 samples:

  	query	positive
  type	string	string
  details	min: 7 tokensmean: 140.91 tokensmax: 1024 tokens	min: 5 tokensmean: 71.36 tokensmax: 1024 tokens

• Samples:

  query	positive
  def save self.attributes.stringify_keys! self.attributes.delete('customer') self.attributes.delete('product') self.attributes.delete('credit_card') self.attributes.delete('bank_account') self.attributes.delete('paypal_account')	self.attributes, options = extract_uniqueness_token(attributes) self.prefix_options.merge!(options) super end
  def _update_summary(self, summary=None): """Update all parts of the summary or clear when no summary.""" board_image_label = self._parts['board image label'] # get content for update or use blanks when no summary if summary: # make a board image with the swap drawn on it # board, action, text = summary.board, summary.action, summary.text board_image_cv = self._create_board_image_cv(summary.board) self._draw_swap_cv(board_image_cv, summary.action) board_image_tk = self._convert_cv_to_tk(board_image_cv) text = '' if not summary.score is None: text += 'Score: {:3.1f}'.format(summary.score) if (not summary.mana_drain_leaves is None) and<br> (not summary.total_leaves is None): text += ' Mana Drains: {}/{}' <br> ''.format(summary.mana_drain_leaves,	summary.total_leaves) else: #clear any stored state image and use the blank board_image_tk = board_image_label._blank_image text = '' # update the UI parts with the content board_image_label._board_image = board_image_tk board_image_label.config(image=board_image_tk) # update the summary text summary_label = self._parts['summary label'] summary_label.config(text=text) # refresh the UI self._base.update()
  def chi_p(mass1, mass2, spin1x, spin1y, spin2x, spin2y): """Returns the effective precession spin from mass1, mass2, spin1x, spin1y, spin2x, and spin2y. """ xi1 = secondary_xi(mass1, mass2, spin1x, spin1y, spin2x, spin2y) xi2 = primary_xi(mass1, mass2, spin1x, spin1y, spin2x, spin2y) return chi_p_from_xi1_xi2(xi1, xi2)	Returns the effective precession spin from mass1, mass2, spin1x, spin1y, spin2x, and spin2y.

• Loss: CachedMultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 128,
      "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: 1024
• num_train_epochs: 1
• learning_rate: 8e-05
• warmup_steps: 0.05
• bf16: True
• eval_strategy: steps
• per_device_eval_batch_size: 1024
• push_to_hub: True
• hub_model_id: modernbert-code
• load_best_model_at_end: True
• dataloader_num_workers: 4
• batch_sampler: no_duplicates

All Hyperparameters

• per_device_train_batch_size: 1024
• num_train_epochs: 1
• max_steps: -1
• learning_rate: 8e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0.05
• 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: steps
• per_device_eval_batch_size: 1024
• 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-code
• 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

• 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: 4.8.3
• 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}
}