Hugging Face's logo

PawK
/
GIS4MODEL

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:769
loss:TripletLoss
text-embeddings-inference
Model card Files
xet
Community

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 on the json dataset. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2
  • Maximum Sequence Length: 256 tokens
  • Output Dimensionality: 384 dimensions
  • Similarity Function: Cosine Similarity
  • Training Dataset:
    • json

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (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("PawK/GIS4MODEL")
# Run inference
sentences = [
    'Threat 4 from Ball Aerospace James Webb Telescope security vulnerability Threat involving Threat 4 from Ball Aerospace James Webb Telescope security vulnerability',
    'CVE-2023-29360: SIMULATED-1 This is a simulated vulnerability for testing with keyword: Ball Aerospace James Webb Telescope security vulnerability security Yes 2025-07-01 Ball Aerospace James Webb Telescope security vulnerability Risk Score: 6.194262717622377 Severity: Critical Exploitability: Moderate Impact: Significant Affected: Tech Industries Components Manufacturers: Tech Industries',
    'CVE-2023-32315: SIMULATED-2 This is a simulated vulnerability for testing with keyword: James Webb Space Telescope compromise security No 2025-08-02 James Webb Space Telescope compromise Risk Score: 5.359379620388373 Severity: Critical Exploitability: Easy Impact: Minimal Affected: Cybersecurity Inc Components Manufacturers: Cybersecurity Inc',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

json

  • Dataset: json
  • Size: 769 training samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 769 samples:
    anchor positive negative
    type string string string
    details
    • min: 11 tokens
    • mean: 15.14 tokens
    • max: 30 tokens
    • min: 70 tokens
    • mean: 78.26 tokens
    • max: 89 tokens
    • min: 70 tokens
    • mean: 78.31 tokens
    • max: 89 tokens
  • Samples:
    anchor positive negative
    Asset related to Asset 2 from Ball Aerospace James Webb Telescope security vulnerability CVE-2023-36036: SIMULATED-3 This is a simulated vulnerability for testing with keyword: Ball Aerospace James Webb Telescope security vulnerability security No 2025-09-05 Ball Aerospace James Webb Telescope security vulnerability Risk Score: 2.1667727327059936 Severity: High Exploitability: Moderate Impact: Minimal Affected: Generic Corp Components Manufacturers: Generic Corp CVE-2023-29360: SIMULATED-3 This is a simulated vulnerability for testing with keyword: Webb Telescope attack security Planned 2025-07-15 Webb Telescope attack Risk Score: 7.06990194191305 Severity: Medium Exploitability: Easy Impact: Minimal Affected: Cybersecurity Inc Components Manufacturers: Cybersecurity Inc
    Asset related to Asset 3 from James Webb malware CVE-2022-0847: SIMULATED-5 This is a simulated vulnerability for testing with keyword: James Webb malware malware Planned 2025-06-18 James Webb malware Risk Score: 3.346935150769055 Severity: Medium Exploitability: Easy Impact: Minimal Affected: Cybersecurity Inc Components Manufacturers: Cybersecurity Inc CVE-2023-32315: SIMULATED-3 This is a simulated vulnerability for testing with keyword: James Webb CVE security No 2025-07-09 James Webb CVE Risk Score: 5.640697982524985 Severity: Low Exploitability: Moderate Impact: Minimal Affected: Tech Industries Components Manufacturers: Tech Industries
    Asset related to Asset 3 from Webb Telescope backdoor CVE-2023-32315: SIMULATED-4 This is a simulated vulnerability for testing with keyword: Webb Telescope backdoor security Yes 2025-09-13 Webb Telescope backdoor Risk Score: 4.6647547554970785 Severity: Medium Exploitability: Difficult Impact: Significant Affected: Tech Industries Components Manufacturers: Tech Industries CVE-2023-29360: SIMULATED-4 This is a simulated vulnerability for testing with keyword: Ball Aerospace James Webb Telescope security vulnerability security Yes 2025-09-13 Ball Aerospace James Webb Telescope security vulnerability Risk Score: 0.5088432907602923 Severity: Medium Exploitability: Difficult Impact: Critical Affected: Generic Corp Components Manufacturers: Generic Corp
  • Loss: TripletLoss with these parameters:
    {
    "distance_metric": "TripletDistanceMetric.COSINE",
    "triplet_margin": 5
}

Evaluation Dataset

json

  • Dataset: json
  • Size: 193 evaluation samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 193 samples:
    anchor positive negative
    type string string string
    details
    • min: 11 tokens
    • mean: 15.33 tokens
    • max: 30 tokens
    • min: 70 tokens
    • mean: 78.34 tokens
    • max: 89 tokens
    • min: 70 tokens
    • mean: 77.8 tokens
    • max: 88 tokens
  • Samples:
    anchor positive negative
    Asset related to Asset 1 from James Webb backdoor CVE-2023-3456: SIMULATED-3 This is a simulated vulnerability for testing with keyword: James Webb backdoor security Yes 2025-07-13 James Webb backdoor Risk Score: 0.8440758738483046 Severity: Medium Exploitability: Difficult Impact: Critical Affected: Cybersecurity Inc Components Manufacturers: Cybersecurity Inc CVE-2023-28771: SIMULATED-2 This is a simulated vulnerability for testing with keyword: Webb Telescope compromise security No 2025-06-15 Webb Telescope compromise Risk Score: 1.310545683795466 Severity: Medium Exploitability: Moderate Impact: Significant Affected: Generic Corp Components Manufacturers: Generic Corp
    Asset related to Asset 4 from James Webb breach CVE-2023-3456: SIMULATED-3 This is a simulated vulnerability for testing with keyword: James Webb breach security No 2025-08-12 James Webb breach Risk Score: 3.4028923165168137 Severity: Medium Exploitability: Easy Impact: Critical Affected: Generic Corp Components Manufacturers: Generic Corp CVE-2023-21036: SIMULATED-2 This is a simulated vulnerability for testing with keyword: James Webb Space Telescope security security Yes 2025-10-06 James Webb Space Telescope security Risk Score: 3.0882717202625423 Severity: Critical Exploitability: Easy Impact: Moderate Affected: Tech Industries Components Manufacturers: Tech Industries
    Asset related to Asset 3 from James Webb Space Telescope security CVE-2023-32315: SIMULATED-1 This is a simulated vulnerability for testing with keyword: James Webb Space Telescope security security Yes 2025-09-03 James Webb Space Telescope security Risk Score: 6.962623430566551 Severity: High Exploitability: Moderate Impact: Significant Affected: Generic Corp Components Manufacturers: Generic Corp CVE-2023-28771: SIMULATED-1 This is a simulated vulnerability for testing with keyword: James Webb Space Telescope threat security No 2025-09-23 James Webb Space Telescope threat Risk Score: 7.572013580016532 Severity: Low Exploitability: Moderate Impact: Moderate Affected: Tech Industries Components Manufacturers: Tech Industries
  • Loss: TripletLoss with these parameters:
    {
    "distance_metric": "TripletDistanceMetric.COSINE",
    "triplet_margin": 5
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • num_train_epochs: 4
  • warmup_ratio: 0.1
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • 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: 1.0
  • num_train_epochs: 4
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • 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
  • use_ipex: False
  • bf16: False
  • fp16: False
  • 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}
  • tp_size: 0
  • 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}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • 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
  • 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: False
  • neftune_noise_alpha: None
  • optim_target_modules: None
  • batch_eval_metrics: False
  • eval_on_start: False
  • use_liger_kernel: False
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss Validation Loss
2.0 50 4.0999 -
2.32 60 4.0104 -
2.7200 70 4.0787 -
3.12 80 3.9972 -
3.52 90 3.9994 -
3.92 100 4.0216 3.8883

Framework Versions

  • Python: 3.13.3
  • Sentence Transformers: 4.1.0
  • Transformers: 4.51.3
  • PyTorch: 2.6.0+cpu
  • Accelerate: 1.6.0
  • Datasets: 3.5.0
  • Tokenizers: 0.21.1

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",
}

TripletLoss 

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
Downloads last month
-
Safetensors
Model size
22.7M params
Tensor type
F32
·
Inference Providers NEW
Sentence Similarity
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for PawK/GIS4MODEL

Base model

sentence-transformers/all-MiniLM-L6-v2
Finetuned
(564)
this model