LogPatternExtractor/Resources/model/README.md

tags, base_model, widget, pipeline_tag, library_name, metrics, model-index

tags	base_model	widget	pipeline_tag	library_name	metrics	model-index
sentence-transformers sentence-similarity feature-extraction dense generated_from_trainer dataset_size:2400 loss:TripletLoss	sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2	source_sentence sentences id=certification<NUM>@yahoo.com <NUM> Volume [<IP>] '<NUM>' id=c<NUM>a<NUM>ac<NUM> Latency Error to rendering connecting user:chorus_<NUM> [<NUM>a<NUM>bc] '<NUM>ecd<NUM>f' 'estimated<NUM>@example.org' started together [<NUM><NUM><NUM>] user:trying<NUM>@yandex.com present <NUM> id=<NUM>c<NUM>b<NUM>ad '<NUM>';<NUM><NUM><NUM>;goals;failed;Client;'<IP>';Directory;killing;licence<NUM>@gmail.com;id=<NUM><NUM><NUM>;<NUM><NUM><NUM>;pound;Route;failed;authenticating;<NUM>;picture;through;Header;martin<NUM>@yahoo.com;<IP>;/var/log/unit.jpg;Route;deleted id=positioning<NUM>@example.com;confidential;'/var/log/offer.awk';'/var/log/contain.dat';id=<NUM>;id=cute<NUM>@protonmail.com;'<NUM>';Packet;'<NUM>';locked;either;with;Transaction;updated;'<NUM>.<NUM>' id=collaboration<NUM>@example.com <NUM> Volume [<IP>] '<NUM>' id=<NUM>ec<NUM>cbb Latency Error to rendering connecting user:depot_<NUM> [<NUM>eca] '<NUM>e<NUM>a<NUM>' 'prior<NUM>@yahoo.com' started together [<NUM><NUM><NUM>] user:solaris<NUM>@outlook.com present <NUM> id=<NUM>b<NUM>d<NUM> source_sentence sentences remote user:robbie_<NUM> <NUM> fundamental id=<NUM> User aborted user:/var/log/with.jpeg through '/var/log/love.md' cycling '<NUM>.<NUM>' private '<NUM>.<NUM>' 'indigenous_<NUM>' Database authenticating <NUM> 'universe<NUM>@protonmail.com' Query <NUM> id=chris_<NUM> names user:/var/log/silver.doc <NUM> User remote <NUM> names aborted 'smoke<NUM>@duck.com' <NUM> authenticating '<NUM>.<NUM>' private cycling user:alto_<NUM> '<NUM>.<NUM>' id=<NUM> Query fundamental Database '/var/log/wall.mov' through id=jonathan_<NUM> 'identification_<NUM>' fetching;[<NUM>ff<NUM>e<NUM>];available;HTTP/<NUM>;[<NUM>.<NUM>];POST;user:<NUM>.<NUM>;<NUM><NUM><NUM>;user:<NUM>;<NUM>.<NUM>;Session;System;user:san<NUM>@outlook.com;had;'<NUM>';user:/var/log/rich.tar.gz;Stack remote user:dvds_<NUM> <NUM> fundamental id=<NUM> User aborted user:/var/log/from.csv through '/var/log/foot.dat' cycling '<NUM>.<NUM>' private '<NUM>.<NUM>' 'proposed_<NUM>' Database authenticating <NUM> 'exceptional<NUM>@protonmail.com' Query <NUM> id=website_<NUM> names source_sentence sentences projection;local;insecure;Thread;'<IP>';<IP>;[<NUM>];with;Interface;Buffer;updated;'/var/log/write.bmp';user:clearly_<NUM>;active;afford;id=<NUM>ab<NUM>;Latency;[strain<NUM>@live.com];stupid<NUM>@gmail.com;Key;created projection;local;insecure;Thread;'<IP>';<IP>;[<NUM>];with;Interface;Buffer;updated;'/var/log/shoe.jar';user:mirrors_<NUM>;active;afford;id=bac<NUM>cfa;Latency;[associations<NUM>@yandex.com];laos<NUM>@example.org;Key;created 'commercial_<NUM>'|'/var/log/piece.tar.gz'|Table|user:catering_<NUM>|user:<NUM>|authorizing|'<IP>'|oxygen|URI|started|Component|Packet|<NUM><NUM><NUM>|Interface|'/var/log/made.exe'|GET|user:resist<NUM>@yahoo.com|Payload|[<NUM>] Port|user:pdf_<NUM>|<NUM>|user:<NUM>.<NUM>|[<NUM>f<NUM>c<NUM>dc]|'adb<NUM>e<NUM>'|implementing|user:<NUM>cfb<NUM>e<NUM>a|<NUM>.<NUM>|discussed|<NUM>|Memory|id=/var/log/dance.m<NUM>u|<NUM>.<NUM>|ceo|remote|'<NUM>.<NUM>'|user:<NUM>a<NUM>|JS source_sentence sentences updated|national|rendering|comply|user:<NUM>|binding|Gateway|<IP>|resolving|responsible|[<NUM>]|'opportunities<NUM>@duck.com'|opens_<NUM>|JSON|retrying|Server|Error|'<NUM>ec<NUM>ca'|berkeley|id=<NUM>.<NUM>|System|torture|Job|id=f<NUM>d connecting disconnected comes<NUM>@gmail.com unavailable Directory [/var/log/early.m<NUM>v] with memorabilia active Payload to Index 'watershed_<NUM>' validated created <NUM>ad<NUM> origin<NUM>@yandex.com;'peaceful_<NUM>';user:<NUM>;URL;its;Gateway;Component;[<NUM>];[<NUM><NUM><NUM>];insecure;tune;'zero_<NUM>';Heap;HTTP/<NUM>;id=queue_<NUM> updated|national|rendering|comply|user:<NUM>|binding|Gateway|<IP>|resolving|responsible|[<NUM>]|'tools<NUM>@duck.com'|jury_<NUM>|JSON|retrying|Server|Error|'e<NUM>a<NUM>b<NUM>ce'|berkeley|id=<NUM>.<NUM>|System|torture|Job|id=bb<NUM>bc source_sentence sentences authenticating YAML PATCH authorizing id=/var/log/seem.tar.xz [<NUM>] rendering 'pursue_<NUM>' [<NUM><NUM><NUM>] fresh online authenticating GET Heap CRITICAL Module id=bother_<NUM> authenticating YAML PATCH authorizing id=/var/log/born.log [<NUM>] rendering 'school_<NUM>' [<NUM><NUM><NUM>] fresh online authenticating GET Heap CRITICAL Module id=brochure_<NUM> user:<IP>;completed;<NUM>;id=/var/log/whose.jpg;user:<NUM>.<NUM>;resolving;allowed;Commit;Index;Daemon;building;length;hall;[/var/log/segment.doc];with Heap;id=dim_<NUM>;[except<NUM>@gmail.com];dropped;determination;via;File;created;id=<NUM>;unavailable;id=/var/log/page.tar.xz;rendering;<NUM>b<NUM>ad<NUM>;id=/var/log/want.tar.gz;Kernel;JS;secure;HTTP/<NUM>;user:a<NUM>dd<NUM>d;user:<NUM><NUM><NUM>;resolving;Header	sentence-similarity	sentence-transformers	cosine_accuracy	name results SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 task dataset metrics type name triplet Triplet name type structural val structural-val type value name cosine_accuracy 0.996666669845581 Cosine Accuracy

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 semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

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

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': 128, 'do_lower_case': False, 'architecture': '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})
)

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 = [
    "authenticating YAML PATCH authorizing id=/var/log/seem.tar.xz [<NUM>] rendering 'pursue_<NUM>' [<NUM><NUM><NUM>] fresh online authenticating GET Heap CRITICAL Module id=bother_<NUM>",
    "authenticating YAML PATCH authorizing id=/var/log/born.log [<NUM>] rendering 'school_<NUM>' [<NUM><NUM><NUM>] fresh online authenticating GET Heap CRITICAL Module id=brochure_<NUM>",
    'Heap;id=dim_<NUM>;[except<NUM>@gmail.com];dropped;determination;via;File;created;id=<NUM>;unavailable;id=/var/log/page.tar.xz;rendering;<NUM>b<NUM>ad<NUM>;id=/var/log/want.tar.gz;Kernel;JS;secure;HTTP/<NUM>;user:a<NUM>dd<NUM>d;user:<NUM><NUM><NUM>;resolving;Header',
]
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,  0.9960, -0.1292],
#         [ 0.9960,  1.0000, -0.1269],
#         [-0.1292, -0.1269,  1.0000]])

Evaluation

Metrics

Triplet

• Dataset: structural-val
• Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9967

Training Details

Training Dataset

Unnamed Dataset

• Size: 2,400 training samples
• Columns: sentence_0, sentence_1, and sentence_2
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1	sentence_2
  type	string	string	string
  details	min: 31 tokens mean: 81.66 tokens max: 128 tokens	min: 33 tokens mean: 81.55 tokens max: 128 tokens	min: 28 tokens mean: 79.74 tokens max: 128 tokens

• Samples:

  sentence_0	sentence_1	sentence_2
  ERROR;[river_];;bit;.;watches;Table;user:.;/var/log/art.zip;/var/log/neck.docx;id=;.;schedules;watson_;DELETE;user:.;Session	ERROR;[taxation_];;bit;.;watches;Table;user:.;/var/log/hunt.pps;/var/log/radio.z;id=;.;schedules;tab_;DELETE;user:.;Session	[experiments_] id= watches DELETE Table user:. . . need_ /var/log/list.mov user:. schedules Session /var/log/pull.pptx bit ERROR
  divided;defence;binding;user:helmet@outlook.com;hours;user:;parsing;rocky;API;Gateway;started;by;flexible;by;INFO;Interface;Memory;teens;JS;fetching;deleted	divided;defence;binding;user:night@protonmail.com;hours;user:;parsing;rocky;API;Gateway;started;by;flexible;by;INFO;Interface;Memory;teens;JS;fetching;deleted	by;binding;Interface;user:;divided;INFO;parsing;API;Memory;teens;user:cells@example.org;started;Gateway;by;deleted;JS;defence;hours;fetching;flexible;rocky
  user:ced|queued||private|Session|blocked|at|user:bba|.|Rollback|Config||Config|user:margin@example.com|spawning||inactive	user:ae|queued||private|Session|blocked|at|user:dbce|.|Rollback|Config||Config|user:travelers@yandex.com|spawning||inactive	;spawning;inactive;;user:dce;queued;Config;;user:promote@protonmail.com;Config;private;user:fad;at;Session;.;blocked;Rollback

• Loss: TripletLoss with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.COSINE",
      "triplet_margin": 0.5
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• 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: 3
• 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: 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}
• 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}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• 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: {}

Training Logs

Epoch	Step	structural-val_cosine_accuracy
1.0	38	0.9950
2.0	76	0.9967

Framework Versions

• Python: 3.12.2
• Sentence Transformers: 5.1.2
• Transformers: 4.57.1
• PyTorch: 2.6.0+cu124
• Accelerate: 1.12.0
• Datasets: 4.4.1
• 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",
}

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}
}