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LogPatternExtractor/Resources/model/README.md
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2026-05-02 18:33:38 +03:00

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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:2400
- loss:TripletLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: 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
sentences:
- '''<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: 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
sentences:
- 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: 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
sentences:
- 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: 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
sentences:
- 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: 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>
sentences:
- 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
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: triplet
name: Triplet
dataset:
name: structural val
type: structural-val
metrics:
- type: cosine_accuracy
value: 0.996666669845581
name: Cosine Accuracy
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/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](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```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("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]])
```
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### Direct Usage (Transformers)
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Triplet
* Dataset: `structural-val`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9967** |
<!--
## 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.*
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 2,400 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 31 tokens</li><li>mean: 81.66 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 33 tokens</li><li>mean: 81.55 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 79.74 tokens</li><li>max: 128 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>ERROR;[river_<NUM>];<IP>;bit;<NUM>.<NUM>;watches;Table;user:<NUM>.<NUM>;/var/log/art.zip;/var/log/neck.docx;id=<NUM><NUM><NUM>;<NUM>.<NUM>;schedules;watson_<NUM>;DELETE;user:<NUM>.<NUM>;Session</code> | <code>ERROR;[taxation_<NUM>];<IP>;bit;<NUM>.<NUM>;watches;Table;user:<NUM>.<NUM>;/var/log/hunt.pps;/var/log/radio.<NUM>z;id=<NUM><NUM><NUM>;<NUM>.<NUM>;schedules;tab_<NUM>;DELETE;user:<NUM>.<NUM>;Session</code> | <code>[experiments_<NUM>] id=<NUM><NUM><NUM> watches DELETE Table user:<NUM>.<NUM> <NUM>.<NUM> <NUM>.<NUM> need_<NUM> /var/log/list.mov <IP> user:<NUM>.<NUM> schedules Session /var/log/pull.pptx bit ERROR</code> |
| <code>divided;defence;binding;user:helmet<NUM>@outlook.com;hours;user:<IP>;parsing;rocky;API;Gateway;started;by;flexible;by;INFO;Interface;Memory;teens;JS;fetching;deleted</code> | <code>divided;defence;binding;user:night<NUM>@protonmail.com;hours;user:<IP>;parsing;rocky;API;Gateway;started;by;flexible;by;INFO;Interface;Memory;teens;JS;fetching;deleted</code> | <code>by;binding;Interface;user:<IP>;divided;INFO;parsing;API;Memory;teens;user:cells<NUM>@example.org;started;Gateway;by;deleted;JS;defence;hours;fetching;flexible;rocky</code> |
| <code>user:c<NUM>ed<NUM>\|queued\|<NUM>\|private\|Session\|blocked\|at\|user:<NUM>b<NUM>ba\|<NUM>.<NUM>\|Rollback\|Config\|<NUM><NUM><NUM>\|Config\|user:margin<NUM>@example.com\|spawning\|<NUM>\|inactive</code> | <code>user:<NUM>ae<NUM>\|queued\|<NUM>\|private\|Session\|blocked\|at\|user:<NUM>db<NUM>ce\|<NUM>.<NUM>\|Rollback\|Config\|<NUM><NUM><NUM>\|Config\|user:travelers<NUM>@yandex.com\|spawning\|<NUM>\|inactive</code> | <code><NUM>;spawning;inactive;<NUM><NUM><NUM>;user:d<NUM>ce<NUM>;queued;Config;<NUM>;user:promote<NUM>@protonmail.com;Config;private;user:f<NUM>ad<NUM>;at;Session;<NUM>.<NUM>;blocked;Rollback</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"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
<details><summary>Click to expand</summary>
- `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`: {}
</details>
### 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
```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",
}
```
#### TripletLoss
```bibtex
@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}
}
```
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