sec-embedding-smoke

SentenceTransformer based on unsloth/bge-m3

This is a sentence-transformers model finetuned from unsloth/bge-m3. It maps inputs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: unsloth/bge-m3 <!-- at revision 57cb1c17d3cb917401c50b204393ee455359a565 -->
• Maximum Sequence Length: 1024 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

<!-- - **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({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'PeftModelForFeatureExtraction'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'cls', 'include_prompt': True})
  (2): Normalize({'module_input_name': 'sentence_embedding', 'module_output_name': 'sentence_embedding'})
)

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
queries = [
    'Các sản phẩm bị ảnh hưởng bởi CVE-2024-37242 có thể bị tấn công bằng cách nào?',
]
documents = [
    'CVE ID: CVE-2024-37242 | Cross-Site Request Forgery (CSRF) vulnerability in Automattic Newspack Newsletters newspack-newsletters allows Cross Site Request Forgery.This issue affects Newspack Newsletters: from n/a through <= 2.13.2. | Published: 2025-01-02',
    'CVE ID: CVE-2016-1000213 | Ruckus Wireless H500 web management interface CSRF | Published: 2016-10-25 | CVSS v3: 8.8 HIGH | Vector: CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H | AV:NETWORK AC:LOW PR:NONE UI:REQUIRED S:UNCHANGED | Impact: C:HIGH I:HIGH A:HIGH | CVSS v2: 6.8 | AV:N/AC:M/Au:N/C:P/I:P/A:P',
    'CVE ID: CVE-2003-1477 | MAILsweeper for SMTP 4.3.6 and 4.3.7 allows remote attackers to cause a denial of service (CPU consumption) via a PowerPoint attachment that either (1) is corrupt or (2) contains "embedded objects." | Published: 2003-12-31 | CVSS v2: 7.8 | AV:N/AC:L/Au:N/C:N/I:N/A:C',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.5969, 0.5004, 0.5534]])

<!-- ### 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: validation
• 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: 50 training samples
• Columns: <code>anchor</code> and <code>positive</code>
• Approximate statistics based on the first 50 samples:

  	anchor	positive
  type	string	string
  modality	text	text
  details	<ul><li>min: 15 tokens</li><li>mean: 25.76 tokens</li><li>max: 39 tokens</li></ul>	<ul><li>min: 38 tokens</li><li>mean: 168.26 tokens</li><li>max: 364 tokens</li></ul>

• Samples:

  anchor	positive
  <code>Các lỗ hổng tương tự CVE-2010-0629 trong các sản phẩm opensuse khác đã được công bố chưa?</code>	<code>CVE ID: CVE-2010-0629 | Use-after-free vulnerability in kadmin/server/server_stubs.c in kadmind in MIT Kerberos 5 (aka krb5) 1.5 through 1.6.3 allows remote authenticated users to cause a denial of service (daemon crash) via a request from a kadmin client that sends an invalid API version number. | Published: 2010-04-07 | CVSS v3: 6.5 MEDIUM | Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H | AV:NETWORK AC:LOW PR:LOW UI:NONE S:UNCHANGED | Impact: C:NONE I:NONE A:HIGH | CVSS v2: 4.0 | AV:N/AC:L/Au:S/C:N/I:N/A:P</code>
  <code>Cách kiểm tra xem hệ thống có bị ảnh hưởng bởi CVE-2003-1477 không, dựa trên sản phẩm all_windows?</code>	<code>CVE ID: CVE-2003-1477 | MAILsweeper for SMTP 4.3.6 and 4.3.7 allows remote attackers to cause a denial of service (CPU consumption) via a PowerPoint attachment that either (1) is corrupt or (2) contains "embedded objects." | Published: 2003-12-31 | CVSS v2: 7.8 | AV:N/AC:L/Au:N/C:N/I:N/A:C</code>
  <code>Có thông tin về thời gian phát hiện và công bố CVE-2005-3254 vào năm 2005 không?</code>	<code>CVE ID: CVE-2005-3254 | The CGIwrap program before 3.9 on Debian GNU/Linux uses an incorrect minimum value of 100 for a UID to determine whether it can perform a seteuid operation, which could allow attackers to execute code as other system UIDs that are greater than the minimum value, which should be 1000 on Debian systems. | Published: 2005-10-18 | CVSS v2: 10.0 | AV:N/AC:L/Au:N/C:C/I:C/A:C</code>

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

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "mini_batch_size": 8,
      "mini_batch_num_tokens": null,
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• num_train_epochs: 1.0
• learning_rate: 2e-05
• bf16: True

All Hyperparameters

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

• per_device_train_batch_size: 8
• num_train_epochs: 1.0
• max_steps: -1
• learning_rate: 2e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0
• 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
• per_device_eval_batch_size: 8
• 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: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: False
• 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: 0
• 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: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Training Time

• Training: 4.2 seconds
• Evaluation: 0.2 seconds
• Total: 4.3 seconds

Framework Versions

• Python: 3.12.3
• Sentence Transformers: 6.0.1
• Transformers: 5.5.0
• PyTorch: 2.12.1+cu130
• Accelerate: 1.15.0
• Datasets: 4.3.0
• Tokenizers: 0.22.2

Additional Resources

• Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
• Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
• Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
• Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
• Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

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}
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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