bge-attackqa-retriever

SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-base-en-v1.5 <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 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': 'BertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'cls', 'include_prompt': True})
  (2): Normalize({})
)

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("qnguyenle/bge-attackqa-retriever")
# Run inference
sentences = [
    "Represent this sentence for searching relevant passages: What attack techniques are used by software 'S0144: ChChes'?",
    "The attack techniques used by software 'S0144: ChChes' are: 'T1036.005: Match Legitimate Name or Location', 'T1057: Process Discovery', 'T1071.001: Web Protocols', 'T1082: System Information Discovery', 'T1083: File and Directory Discovery', 'T1105: Ingress Tool Transfer', 'T1132.001: Standard Encoding', 'T1547.001: Registry Run Keys / Startup Folder', 'T1553.002: Code Signing', 'T1555.003: Credentials from Web Browsers', 'T1562.001: Disable or Modify Tools', 'T1573.001: Symmetric Cryptography'",
    "How attack software 'S1119: LIGHTWIRE' uses attack technique 'T1573.001: Symmetric Cryptography':\nLIGHTWIRE can RC4 encrypt C2 commands.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9300, 0.0201],
#         [0.9300, 1.0000, 0.0798],
#         [0.0201, 0.0798, 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: 22,801 training samples
• Columns: <code>anchor</code> and <code>positive</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	<ul><li>min: 14 tokens</li><li>mean: 32.93 tokens</li><li>max: 54 tokens</li></ul>	<ul><li>min: 18 tokens</li><li>mean: 80.87 tokens</li><li>max: 512 tokens</li></ul>

• Samples:

  anchor	positive
  <code>Represent this sentence for searching relevant passages: How does attack software 'S0074: Sakula' use attack technique 'T1543.003: Windows Service'?</code>	<code>How attack software 'S0074: Sakula' uses attack technique 'T1543.003: Windows Service':<br>Some Sakula samples install themselves as services for persistence by calling WinExec with the net start argument.</code>
  <code>Represent this sentence for searching relevant passages: What attack techniques are used by software 'S0386: Ursnif'?</code>	<code>The attack techniques used by software 'S0386: Ursnif' are: 'T1005: Data from Local System', 'T1007: System Service Discovery', 'T1012: Query Registry', 'T1027.010: Command Obfuscation', 'T1027.013: Encrypted/Encoded File', 'T1036.005: Match Legitimate Name or Location', 'T1041: Exfiltration Over C2 Channel', 'T1047: Windows Management Instrumentation', 'T1055.005: Thread Local Storage', 'T1055.012: Process Hollowing', 'T1056.004: Credential API Hooking', 'T1057: Process Discovery', 'T1059.001: PowerShell', 'T1059.005: Visual Basic', 'T1070.004: File Deletion', 'T1071.001: Web Protocols', 'T1074.001: Local Data Staging', 'T1080: Taint Shared Content', 'T1082: System Information Discovery', 'T1090.003: Multi-hop Proxy', 'T1090: Proxy', 'T1091: Replication Through Removable Media', 'T1105: Ingress Tool Transfer', 'T1106: Native API', 'T1112: Modify Registry', 'T1113: Screen Capture', 'T1132: Data Encoding', 'T1140: Deobfuscate/Decode Files or Information', 'T1185: Browser Session Hijacki...</code>
  <code>Represent this sentence for searching relevant passages: How does attack software 'S0435: PLEAD' use attack technique 'T1555: Credentials from Password Stores'?</code>	<code>How attack software 'S0435: PLEAD' uses attack technique 'T1555: Credentials from Password Stores':<br>PLEAD has the ability to steal saved passwords from Microsoft Outlook.</code>

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

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "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: 32
• learning_rate: 2e-05
• num_train_epochs: 1
• warmup_steps: 0.1
• fp16: True

All Hyperparameters

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

• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 8
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_ratio: None
• warmup_steps: 0.1
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• enable_jit_checkpoint: False
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• use_cpu: False
• seed: 42
• data_seed: None
• bf16: False
• fp16: True
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: -1
• ddp_backend: None
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• 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
• 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_for_metrics: []
• eval_do_concat_batches: True
• auto_find_batch_size: False
• full_determinism: False
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• 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
• use_cache: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Training Time

• Training: 11.3 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.4.0
• Transformers: 5.0.0
• PyTorch: 2.10.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.0.0
• 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",
}

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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