README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:33174
- loss:TripletLoss
base_model: sentence-transformers/multi-qa-mpnet-base-cos-v1
widget:
- source_sentence: 'writeBlock blk_-2025444374149014902 received exception java.io.IOException:
    Could not read from stream'
  sentences:
  - PAM 5 more authentication failures; logname= uid=0 euid=0 tty=ssh ruser= rhost=218.65.30.30
    user=root
  - 'writeBlock blk_5718472814394212827 received exception java.io.IOException: Could
    not read from stream'
  - Adding an already existing block blk_5697572983288390847
- source_sentence: Accepted password for hxu from 137.189.206.152 port 13415 ssh2
  sentences:
  - Address 14.186.200.51 maps to static.vnpt.vn, but this does not map back to the
    address - POSSIBLE BREAK-IN ATTEMPT!
  - Accepted password for jmzhu from 112.96.33.40 port 48253 ssh2
  - Failed password for invalid user shengt from 115.233.91.242 port 49601 ssh2
- source_sentence: Unexpected error trying to delete block blk_9209337043266813528.
    BlockInfo not found in volumeMap.
  sentences:
  - Deleting block blk_6056040671227271408 file /mnt/hadoop/dfs/data/current/subdir63/blk_6056040671227271408
  - Unexpected error trying to delete block blk_8234858690572948833. BlockInfo not
    found in volumeMap.
  - '[instance: 40568281-5a34-464a-b17b-99a0a5591045] Deleting instance files /var/lib/nova/instances/40568281-5a34-464a-b17b-99a0a5591045_del'
- source_sentence: 'writeBlock blk_5827639102770185153 received exception java.io.IOException:
    Could not read from stream'
  sentences:
  - 'pam_unix(sshd:auth): check pass; user unknown'
  - Exception in receiveBlock for block blk_6495484866542253279 java.io.EOFException
  - 'writeBlock blk_-3265479347842446682 received exception java.io.IOException: Could
    not read from stream'
- source_sentence: '[instance: 71065aa4-40af-4e74-bd6a-ef77c7f4dd02] Total memory:
    64172 MB, used: 512.00 MB'
  sentences:
  - '[instance: c6289e85-a048-42bd-b32a-427cc1b12ef5] Total memory: 64172 MB, used:
    512.00 MB'
  - '[instance: 13b4689e-7f96-40a3-89a5-31d8e72a4113] VM Stopped (Lifecycle Event)'
  - '[instance: 09e74992-da6d-4111-861e-6d22bbf91fdc] Claim successful'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on sentence-transformers/multi-qa-mpnet-base-cos-v1

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-mpnet-base-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-cos-v1). It maps sentences & paragraphs to a 768-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/multi-qa-mpnet-base-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-cos-v1) <!-- at revision 822dbc9732879fe45b5d79fdb372f2ccec4c76b5 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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/UKPLab/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': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, '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:

```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 = [
    '[instance: 71065aa4-40af-4e74-bd6a-ef77c7f4dd02] Total memory: 64172 MB, used: 512.00 MB',
    '[instance: c6289e85-a048-42bd-b32a-427cc1b12ef5] Total memory: 64172 MB, used: 512.00 MB',
    '[instance: 09e74992-da6d-4111-861e-6d22bbf91fdc] Claim successful',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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You can finetune this model on your own dataset.

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 33,174 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: 12 tokens</li><li>mean: 41.23 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 41.22 tokens</li><li>max: 94 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 39.57 tokens</li><li>max: 94 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                         | sentence_1                                                                                                          | sentence_2                                                                    |
  |:-------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------|
  | <code>pam_unix(sshd:session): session opened for user hxu by (uid=0)</code>                                        | <code>pam_unix(sshd:session): session opened for user curi by (uid=0)</code>                                        | <code>Received disconnect from 58.218.213.45: 11: disconnect [preauth]</code> |
  | <code>[instance: 78644035-9af0-4e94-b1bc-6412cb13e474] VM Stopped (Lifecycle Event)</code>                         | <code>[instance: 18473413-894b-4ae9-85eb-566134c89cd4] VM Stopped (Lifecycle Event)</code>                          | <code>[instance: 643b82e0-49dd-4ff5-a967-9483ba081678] Creating image</code>  |
  | <code>PAM 5 more authentication failures; logname= uid=0 euid=0 tty=ssh ruser= rhost=59.63.188.30 user=root</code> | <code>PAM 5 more authentication failures; logname= uid=0 euid=0 tty=ssh ruser= rhost=218.65.30.126 user=root</code> | <code>pam_unix(sshd:session): session opened for user hxu by (uid=0)</code>   |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
  ```json
  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `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`: steps
- `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
- `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}
- `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
- `dispatch_batches`: None
- `split_batches`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.4.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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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