README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:70000
- loss:CoSENTLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: cabinet.cabinetPhaseBCurrentMeasurement
  sentences:
  - log.lowLoadPowerCutoff
  - log.loadPowerLowerLimit
  - log.minimumIlluminationSetpoint
- source_sentence: log.alarmFault.alarmFaultHighloadcurrent
  sentences:
  - log.upperLoadPowerConstraint
  - schedule.daysAssignedInSchedule
  - log.operatingRegimeChange
- source_sentence: log.alarmFault.lightStabilityViolation
  sentences:
  - log.alarmFault.loadVoltagelow
  - log.alarmFault.insufficientLoadWattage
  - log.alarmFault.alarmFaultLowcurrent
- source_sentence: log.relayComponentStatus
  sentences:
  - device.LightFixtureIpAddress
  - maintenance.maintenanceOperationStatus
  - log.wattsToVaRatio
- source_sentence: log.alarmFault.waveringLightEmission
  sentences:
  - log.maximumWattageBoundary
  - log.logLowloadpowerthreshold
  - log.presetBrightnessPoint
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
- **Maximum Sequence Length:** 384 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': 384, '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 = [
    'log.alarmFault.waveringLightEmission',
    'log.presetBrightnessPoint',
    'log.maximumWattageBoundary',
]
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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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 70,000 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                        | score                                                            |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | float                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 10.81 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.1 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: -0.0</li><li>mean: 0.11</li><li>max: 0.99</li></ul> |
* Samples:
  | sentence1                                | sentence2                                         | score                             |
  |:-----------------------------------------|:--------------------------------------------------|:----------------------------------|
  | <code>log.temperatureMaximumLimit</code> | <code>schedule.daysWhenScheduleIsEffective</code> | <code>0.006032609194517136</code> |
  | <code>device.DeviceTimeZone</code>       | <code>maintenance.maintenanceModifications</code> | <code>0.011996420472860337</code> |
  | <code>log.alarmFault.highAmps</code>     | <code>log.currentLowerBoundary</code>             | <code>0.20761280847788094</code>  |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 70,000 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                        | score                                                            |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | float                                                            |
  | details | <ul><li>min: 5 tokens</li><li>mean: 10.81 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.1 tokens</li><li>max: 17 tokens</li></ul> | <ul><li>min: -0.0</li><li>mean: 0.11</li><li>max: 0.99</li></ul> |
* Samples:
  | sentence1                                | sentence2                                         | score                             |
  |:-----------------------------------------|:--------------------------------------------------|:----------------------------------|
  | <code>log.temperatureMaximumLimit</code> | <code>schedule.daysWhenScheduleIsEffective</code> | <code>0.006032609194517136</code> |
  | <code>device.DeviceTimeZone</code>       | <code>maintenance.maintenanceModifications</code> | <code>0.011996420472860337</code> |
  | <code>log.alarmFault.highAmps</code>     | <code>log.currentLowerBoundary</code>             | <code>0.20761280847788094</code>  |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates

#### 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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 2e-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`: 10
- `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`: True
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step  | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.2286 | 1000  | 4.9688        | 4.1188          |
| 0.4571 | 2000  | 4.0956        | 3.9955          |
| 0.6857 | 3000  | 4.0295        | 3.8972          |
| 0.9143 | 4000  | 3.9616        | 3.8387          |
| 1.1429 | 5000  | 3.9073        | 3.7972          |
| 1.3714 | 6000  | 3.8188        | 3.7559          |
| 1.6    | 7000  | 3.7536        | 3.5798          |
| 1.8286 | 8000  | 3.6843        | 3.6076          |
| 2.0571 | 9000  | 3.6231        | 3.5363          |
| 2.2857 | 10000 | 3.5492        | 3.4779          |
| 2.5143 | 11000 | 3.5423        | 3.4188          |
| 2.7429 | 12000 | 3.4868        | 3.4221          |
| 2.9714 | 13000 | 3.4593        | 3.2962          |
| 3.2    | 14000 | 3.3957        | 3.3086          |
| 3.4286 | 15000 | 3.3801        | 3.2652          |
| 3.6571 | 16000 | 3.3501        | 3.2527          |
| 3.8857 | 17000 | 3.3117        | 3.2055          |
| 4.1143 | 18000 | 3.2396        | 3.1950          |
| 4.3429 | 19000 | 3.2424        | 3.1900          |
| 4.5714 | 20000 | 3.2185        | 3.1467          |
| 4.8    | 21000 | 3.2173        | 3.1315          |
| 5.0286 | 22000 | 3.2119        | 3.1175          |
| 5.2571 | 23000 | 3.1583        | 3.0700          |
| 5.4857 | 24000 | 3.1634        | 3.0862          |
| 5.7143 | 25000 | 3.1538        | 3.0367          |
| 5.9429 | 26000 | 3.1187        | 3.0292          |
| 6.1712 | 27000 | 3.0703        | 3.0349          |
| 6.3998 | 28000 | 3.0925        | 3.0017          |
| 6.6283 | 29000 | 3.0179        | 2.9847          |
| 6.8569 | 30000 | 3.0331        | 2.9622          |
| 7.0855 | 31000 | 3.0784        | 2.9761          |
| 7.3141 | 32000 | 3.0484        | 2.9501          |
| 7.5426 | 33000 | 3.0138        | 2.9397          |
| 7.7712 | 34000 | 2.9935        | 2.9322          |
| 7.9998 | 35000 | 2.9912        | 2.9247          |
| 8.2283 | 36000 | 2.9852        | 2.9069          |
| 8.4569 | 37000 | 2.946         | 2.9162          |
| 8.6855 | 38000 | 2.9503        | 2.9038          |
| 8.9141 | 39000 | 2.9759        | 2.8972          |
| 9.1426 | 40000 | 2.9413        | 2.8893          |
| 9.3712 | 41000 | 2.933         | 2.8878          |
| 9.5998 | 42000 | 2.918         | 2.8747          |
| 9.8283 | 43000 | 2.9427        | 2.8708          |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.4.1
- Transformers: 4.48.0
- PyTorch: 2.5.0+cu121
- Accelerate: 1.0.1
- Datasets: 3.0.2
- 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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