CodeSearch-ModernBERT-Crow-v3-large-len1024-Plus

SentenceTransformer based on Shuu12121/CodeModernBERT-Crow-v3-large-len1024

This is a sentence-transformers model finetuned from Shuu12121/CodeModernBERT-Crow-v3-large-len1024. It maps sentences & paragraphs to a 1024-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: Shuu12121/CodeModernBERT-Crow-v3-large-len1024 <!-- at revision ed7d0b61cdf30bdd4aab2c9469635f83e9a3051d -->
• Maximum Sequence Length: 1024 tokens
• Output Dimensionality: 1024 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': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
  (1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'OnEachBatchTx registers a handler for a specific event type that is contingent on a transaction.',
    'func OnEachBatchTx[T, TTx comparable](b *Bus, fn func(ctx context.Context, tx TTx, data []T) error) {\n\tsub := findBus[T, TTx](b)\n\tsub.onBatchTx = append(sub.onBatchTx, fn)\n}',
    'func (s *GetDomainDetailOutput) SetAbuseContactEmail(v string) *GetDomainDetailOutput {\n\ts.AbuseContactEmail = &v\n\treturn s\n}',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7953, 0.0445],
#         [0.7953, 1.0000, 0.0267],
#         [0.0445, 0.0267, 1.0000]])

<!-- ### 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

Unnamed Dataset

• Size: 8,003,584 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	<ul><li>min: 8 tokens</li><li>mean: 36.78 tokens</li><li>max: 1024 tokens</li></ul>	<ul><li>min: 15 tokens</li><li>mean: 165.61 tokens</li><li>max: 1024 tokens</li></ul>	<ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul>

• Samples:

  sentence_0	sentence_1	label
  <code>Generated from example definition: https://github.com/Azure/azure-rest-api-specs/blob/ee1eec42dcc710ff88db2d1bf574b2f9afe3d654/specification/eventgrid/resource-manager/Microsoft.EventGrid/stable/2025-02-15/examples/DomainTopicEventSubscriptions_Delete.json</code>	<code>func ExampleDomainTopicEventSubscriptionsClient_BeginDelete() {<br> cred, err := azidentity.NewDefaultAzureCredential(nil)<br> if err != nil {<br> log.Fatalf("failed to obtain a credential: %v", err)<br> }<br> ctx := context.Background()<br> clientFactory, err := armeventgrid.NewClientFactory("<subscription-id>", cred, nil)<br> if err != nil {<br> log.Fatalf("failed to create client: %v", err)<br> }<br> poller, err := clientFactory.NewDomainTopicEventSubscriptionsClient().BeginDelete(ctx, "examplerg", "exampleDomain1", "exampleDomainTopic1", "examplesubscription1", nil)<br> if err != nil {<br> log.Fatalf("failed to finish the request: %v", err)<br> }<br> _, err = poller.PollUntilDone(ctx, nil)<br> if err != nil {<br> log.Fatalf("failed to pull the result: %v", err)<br> }<br>}</code>	<code>1.0</code>
  <code>newUpgradeContext creates a Context, avoid nil Context member.<br>only used for testing now.</code>	<code>func newUpgradeContext() Context {<br> return Context{<br> Context: context.Background(),<br> SubTaskConfigs: make(map[string]map[string]config.SubTaskConfig),<br> }<br>}</code>	<code>1.0</code>
  <code>ToPostRequestInformation create new navigation property to rules for policies</code>	<code>func (m *RoleManagementPoliciesItemRulesRequestBuilder) ToPostRequestInformation(ctx context.Context, body iadcd81124412c61e647227ecfc4449d8bba17de0380ddda76f641a29edf2b242.UnifiedRoleManagementPolicyRuleable, requestConfiguration *RoleManagementPoliciesItemRulesRequestBuilderPostRequestConfiguration)(*i2ae4187f7daee263371cb1c977df639813ab50ffa529013b7437480d1ec0158f.RequestInformation, error) {<br> requestInfo := i2ae4187f7daee263371cb1c977df639813ab50ffa529013b7437480d1ec0158f.NewRequestInformation()<br> requestInfo.UrlTemplate = m.urlTemplate<br> requestInfo.PathParameters = m.pathParameters<br> requestInfo.Method = i2ae4187f7daee263371cb1c977df639813ab50ffa529013b7437480d1ec0158f.POST<br> requestInfo.Headers.Add("Accept", "application/json")<br> err := requestInfo.SetContentFromParsable(ctx, m.requestAdapter, "application/json", body)<br> if err != nil {<br> return nil, err<br> }<br> if requestConfiguration != nil {<br> requestInfo.Headers.AddAll(requestConfiguration.Header...</code>	<code>1.0</code>

• Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 32,
      "gather_across_devices": false
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 1024
• per_device_eval_batch_size: 1024
• num_train_epochs: 1
• fp16: True
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 1024
• per_device_eval_batch_size: 1024
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• 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
• 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}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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
• hub_revision: None
• 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
• include_tokens_per_second: False
• include_num_input_tokens_seen: no
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: True
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 5.2.0
• Transformers: 4.57.3
• PyTorch: 2.8.0+cu128
• Accelerate: 1.12.0
• Datasets: 3.6.0
• Tokenizers: 0.22.1

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}
}

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