Shuu12121/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]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 8,003,584 training samples
• —Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
• —Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | 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: | sentence0 | sentence1 | 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/DomainTopicEventSubscriptionsDelete.json</code> | <code>func ExampleDomainTopicEventSubscriptionsClientBeginDelete() {<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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —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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