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Observer2399/erdanei-security-encoder

sourceHugging Faceupdated 5mo agoView on Hugging Face

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Model Card

SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a sentence-transformers model finetuned from sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

—Model Type: Sentence Transformer
—Base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
—Maximum Sequence Length: 128 tokens
—Output Dimensionality: 384 dimensions
—Similarity Function: Cosine Similarity
—Supported Modality: Text

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': 384, 'pooling_mode': 'mean', 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

bash

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Observer2399/erdanei-security-encoder")
# Run inference
sentences = [
    '10.8 Un ejemplo completo con C# y SQLite __________________________________________________ 216',
    'public:',
    'void funcion_D();',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9302, 0.9360],
#         [0.9302, 1.0000, 0.8984],
#         [0.9360, 0.8984, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

—Size: 2,500 training samples
—Columns: <code>sentence1</code> and <code>sentence2</code>
—Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 19.16 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 18.05 tokens</li><li>max: 90 tokens</li></ul> |
—Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| | <code>más funcionalidad. De hecho, respetar un estándar de codificación puede ser bastante aburrido si la</code> | <code>10. La empresa Objectic, para la cual trabajas, te ha pedido que</code> | | <code>mano izquierda del s´ımbolo igual (=) se indica la variable a la que se va a asignar el valor</code> | <code>El archivo de proyecto esta basado en XML y define algunos metadatos</code> | | <code>Primero revisa tu código antes de buscar culpar a otros Escoge tus herramientas con cuidado</code> | <code>10.6.3. Alternativa simple en bucle para ................................</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "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: 16
—warmup_steps: 100

All Hyperparameters

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

—per_device_train_batch_size: 16
—num_train_epochs: 3
—max_steps: -1
—learning_rate: 5e-05
—lr_scheduler_type: linear
—lr_scheduler_kwargs: None
—warmup_steps: 100
—optim: adamwtorchfused
—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: False
—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
—eval_strategy: no
—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: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_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

Epoch	Step	Training Loss
0.3185	50	4.8044
0.6369	100	2.9301
0.9554	150	2.7735
1.2739	200	2.7447
1.5924	250	2.7647
1.9108	300	2.7669
2.2293	350	2.7047
2.5478	400	2.6765
2.8662	450	2.6872

Training Time

—Training: 4.0 minutes

Framework Versions

—Python: 3.12.13
—Sentence Transformers: 5.4.0
—Transformers: 5.5.0
—PyTorch: 2.10.0+cu128
—Accelerate: 1.13.0
—Datasets: 4.3.0
—Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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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