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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3157
- loss:TripletLoss
- dataset_size:2525
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: What is the role of the Public Information Officer during a radiological
    emergency?
  sentences:
  - The Public Information Officer (PIO), assigned duties at the Emergency Operations
    Center (EOC), and the Parish Spokesperson will be responsible for implementation
    of this procedure.
  - Be prepared to report to the Parish EOC if requested by the Director of the Office
    of Homeland Security and Emergency Preparedness.
  - HRI may proceed with its planned mining-related activities in these areas to the
    extent authorized by its NRC Materials License SUA-1508.
- source_sentence: The NRC also regulates the licensing and reporting obligations
    for materials that are byproducts in the medical field.
  sentences:
  - Parts 30, 31, 32 and 150
  - 'Exemptions From Licensing, General Licenses, and Distribution of Byproduct Material:
    Licensing and Reporting Requirements'
  - Containment vessel (CNV) pressure/temperature response analysis method, similar
    to method used in DCA technical report, responds to LOCA pipe break, secondary
    line breaks, IORV events, or inadvertent ECCS actuation.
- source_sentence: What is the aging management program for concrete in nuclear facilities?
  sentences:
  - Further evaluation is required to determine if a plant-specific aging management
    program is needed.
  - The DCPP Structures Monitoring AMP (B.2.3.33) is credited with managing cracking
    due to reaction with aggregates (such as ASR), for DCPP group 1, 3, 4, 5, and
    7 structures, including inaccessible areas.
  - The Survey Units listed in Figure 4 measure various acreage sizes, detailing the
    land area covered by each unit within the Non-Industrialized section of the site.
- source_sentence: What is the purpose of an emergency core cooling system in nuclear
    reactors?
  sentences:
  - The Commission issued Staff Requirements Memorandum (SRM) SECY-10-0113 directing
    the staff to consider alternative options for resolving GSI-191 (Reference 5).
  - preclude the formulation or implementation of reasonable and prudent alternatives
    to avoid jeopardizing the continued existence of endangered or threatened species
    or destroying or modifying critical habitat [Section 7(d)].
  - ECCS must be designed so that calculated cooling performance following postulated
    loss-of-coolant accidents conforms to the criteria set forth in paragraph (b)
    of this section.
- source_sentence: corrosion related to nuclear components
  sentences:
  - This alternative is requested for the duration of the Brunswick Steam Electric
    Plant, Units 1 & 2, Third Ten-Year Containment Inservice Inspection Interval,
    which is currently scheduled to end no later than May 10, 2028.
  - '*Note: Initiation of Reactor Enclosure isolation starts Reactor Enclosure Recirculation
    System (RERS) and SGTS. Ref: UFSAR 6.2.3.2.3*'
  - In the proposed alternative by the Owner (Duke Energy), corrosion or erosion that
    has reduced the component wall thickness to less than 145% of the minimum design
    wall thickness will be considered a relevant condition that will require evaluation
    or corrective measures to the extent necessary to meet the acceptance standards
    of IWE-3500 prior to continued service.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: validation
      type: validation
    metrics:
    - type: cosine_accuracy
      value: 0.8797468543052673
      name: Cosine Accuracy
    - type: cosine_accuracy
      value: 0.9556962251663208
      name: Cosine Accuracy
    - type: cosine_accuracy
      value: 0.9588607549667358
      name: Cosine Accuracy
---

# SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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 = [
    'corrosion related to nuclear components',
    'In the proposed alternative by the Owner (Duke Energy), corrosion or erosion that has reduced the component wall thickness to less than 145% of the minimum design wall thickness will be considered a relevant condition that will require evaluation or corrective measures to the extent necessary to meet the acceptance standards of IWE-3500 prior to continued service.',
    'This alternative is requested for the duration of the Brunswick Steam Electric Plant, Units 1 & 2, Third Ten-Year Containment Inservice Inspection Interval, which is currently scheduled to end no later than May 10, 2028.',
]
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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### 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

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## Evaluation

### Metrics

#### Triplet

* Dataset: `validation`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.8797** |

#### Triplet

* Dataset: `validation`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9557** |

#### Triplet

* Dataset: `validation`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9589** |

<!--
## 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.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 2,525 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: 4 tokens</li><li>mean: 15.01 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 45.67 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 33.2 tokens</li><li>max: 228 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                 | sentence_1                                                                                                                                                                                      | sentence_2                                                                                                                                                                                                                                                                                                                                                                 |
  |:---------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the significance of groundwater monitoring wells in nuclear safety assessments?</code>                       | <code>Locations of monitoring wells B-22 and B-36 that showed groundwater “mounding” supported by maps of the stormwater drainage system near the wells (Related to Need GW-5)</code>           | <code>To the extent that these components of the intake and discharge systems are accessible/viewable: - Submerged multi-port intake and intake tunnel, including depiction of location of intake - Traveling screens - Service water pumphouse - Emergency service water forebay - Cooling towers and cooling tower basin - Discharge tunnel and discharge outfall</code> |
  | <code>An analysis of the stresses experienced by reactor vessel studs is crucial for evaluating operational safety.</code> | <code>Table 2. Calculation of Primary Stresses in Reactor Vessel Studs, Two Studs Out of Service provides detailed calculations of primary stresses for each stud in the reactor vessel.</code> | <code>Regulatory filings often require extensive documentation to demonstrate compliance with safety protocols.</code>                                                                                                                                                                                                                                                     |
  | <code>An assessment was carried out to determine the potential for liquefaction at the EGC ESP Site.</code>                | <code>An evaluation of liquefaction potential was conducted at the EGC ESP Site.</code>                                                                                                         | <code>The static groundwater table within the Illinois till is approximately 30 ft below the ground surface, but that there are shallower perched groundwater layers closer to the surface.</code>                                                                                                                                                                         |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 24
- `per_device_eval_batch_size`: 24
- `num_train_epochs`: 5
- `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`: 24
- `per_device_eval_batch_size`: 24
- `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`: 5
- `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}
- `tp_size`: 0
- `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
- `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>

### Training Logs
| Epoch  | Step | Training Loss | validation_cosine_accuracy |
|:------:|:----:|:-------------:|:--------------------------:|
| 2.5253 | 500  | 4.2171        | -                          |
| 0.6329 | 100  | -             | 0.9177                     |
| 1.0    | 158  | -             | 0.8592                     |
| 1.2658 | 200  | -             | 0.8972                     |
| 1.8987 | 300  | -             | 0.875                      |
| 2.0    | 316  | -             | 0.8940                     |
| 2.5316 | 400  | -             | 0.8734                     |
| 3.0    | 474  | -             | 0.8956                     |
| 3.1646 | 500  | 3.985         | 0.8813                     |
| 3.7975 | 600  | -             | 0.8703                     |
| 4.0    | 632  | -             | 0.9003                     |
| 4.4304 | 700  | -             | 0.8797                     |
| 5.0    | 790  | -             | 0.8797                     |
| 0.6329 | 100  | -             | 0.8228                     |
| 1.0    | 158  | -             | 0.9383                     |
| 1.2658 | 200  | -             | 0.9541                     |
| 1.8987 | 300  | -             | 0.9573                     |
| 2.0    | 316  | -             | 0.9589                     |
| 2.5316 | 400  | -             | 0.9541                     |
| 3.0    | 474  | -             | 0.9525                     |
| 3.1646 | 500  | 2.0222        | 0.9525                     |
| 3.7975 | 600  | -             | 0.9541                     |
| 4.0    | 632  | -             | 0.9557                     |
| 4.4304 | 700  | -             | 0.9573                     |
| 5.0    | 790  | -             | 0.9557                     |
| 0.9434 | 100  | -             | 0.9509                     |
| 1.0    | 106  | -             | 0.9525                     |
| 1.8868 | 200  | -             | 0.9541                     |
| 2.0    | 212  | -             | 0.9573                     |
| 2.8302 | 300  | -             | 0.9557                     |
| 3.0    | 318  | -             | 0.9589                     |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 4.0.2
- Transformers: 4.51.3
- PyTorch: 2.2.2
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1

## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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