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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8622
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: What is the purpose of geotechnical exploration at the PSEG Site?
sentences:
- 'The purposes of the PSEG Site geotechnical exploration and testing were to: -
Obtain new data to meet current NRC and vendor design control document Tier 1
site characteristics requirements as appropriate for an ESPA - Confirm and demonstrate
the applicability of the existing field data from the previous site exploration
work for the existing nuclear plants'
- Geotechnical evaluations at the PSEG Site included assessing soil stratigraphy
and groundwater conditions to identify potential risks and the suitability of
the site for construction, focusing on the mechanical properties of subsurface
materials.
- Table 3.8-3 Illinois Inventory of Archaeological Sites Entries within 6-miles
of DNPS (Sheet 2 of 28) lists various archaeological sites and their statuses
relevant to the regulatory considerations for the plant.
- source_sentence: The analysis of the identified nuclides can greatly aid in determining
the safety measures necessary for nuclear facilities.
sentences:
- IDENTIFIED NUCLIDES
- 'Peak Analysis Performed on: 5/29/2019 6:14:38 AM'
- 10 CFR Part 50, Appendix H, “Reactor Vessel Material Surveillance Program Requirements,”
requires that peak neutron fluence at the end of the design life of the vessel
will not exceed 1.0 x 10¹⁷ n/cm² (E > 1.0 MeV), or that reactor vessel beltline
materials be monitored by a surveillance program.
- source_sentence: The NRC assessment includes evaluations to determine the impact
of specific events on safety measures.
sentences:
- The staff noted that the licensee performed a root cause evaluation with an extent
of condition and extent of cause evaluation following the May 25 scram.
- In assessing operational events, it is crucial to differentiate between various
types of occurrences to ensure comprehensive safety evaluations encompass all
relevant aspects, including human factors and procedural adherence.
- The reactor trip breaker indicating lights provide crucial information on the
status of the reactor trip system during an Anticipated Transient Without Scram
(ATWS).
- source_sentence: Each reactor building isolation valve must remain effective during
various operational modes.
sentences:
- The RHRSW System functions to remove heat from the RHR System and Emergency Equipment
Cooling Water (EECW) System components by pumping water from Wheeler Reservoir
through the Residual Heat Removal (RHR) heat exchangers and Emergency Equipment
Cooling Water (EECW) System components and discharges back to Wheeler Reservoir.
- Each reactor building isolation valve shall be OPERABLE.
- Separate Condition entry is allowed for each penetration flow path.
- source_sentence: What is the purpose of the Rapid Borate Stop Valve in Reactor Control?
sentences:
- CLOSE the Air Supply Isolation Valve, 12CV160 A/S, AIR SUPPLY FOR 12CV160.
- The NRC staff is reviewing Westinghouse’s license renewal application and preparing
an environmental impact statement (EIS) in accordance with the National Environmental
Policy Act of 1969.
- Locates and discusses opening 1CV175, Rapid Borate Stop Valve by disengaging clutch
and rotating handwheel (counterclockwise).
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: 0.9397031664848328
name: Cosine Accuracy
- type: cosine_accuracy
value: 0.9387755393981934
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 = [
'What is the purpose of the Rapid Borate Stop Valve in Reactor Control?',
'Locates and discusses opening 1CV175, Rapid Borate Stop Valve by disengaging clutch and rotating handwheel (counterclockwise).',
'CLOSE the Air Supply Isolation Valve, 12CV160 A/S, AIR SUPPLY FOR 12CV160.',
]
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** | **0.9397** |
#### 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** | **0.9388** |
<!--
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### Recommendations
*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: 8,622 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: 5 tokens</li><li>mean: 14.64 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 43.24 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 31.29 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:----------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is the concentration of H-3 in µCi/ml?</code> | <code>H-3 has a concentration of 8.5E-10 µCi/ml.</code> | <code>The isotope Rb-89 has a release rate of 4.7E-05 Ci/yr.</code> |
| <code>gamma calibration procedures</code> | <code>Gamma Calibration: GM detectors positioned perpendicular to source for M-44-9 in which the front of probe faces source.</code> | <code>Effective calibration of GM detectors is crucial for accurate measurement. Procedures often involve using a consistent radiation source and monitoring the response of various detector models across multiple energy levels.</code> |
| <code>What is the function of the TAP-A program in thermal analysis?</code> | <code>The TAP-A program is applicable to both “transient and steady-state heat transfer in multidimensional systems having arbitrary geometric configurations, boundary conditions, initial conditions, and physical properties.</code> | <code>The wall panel model for the crane wall is 48 ft long with 8 axial stations each 6 ft in length.</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`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 5
- `fp16`: True
- `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`: 32
- `per_device_eval_batch_size`: 32
- `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`: 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}
- `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 |
|:------:|:----:|:-------------:|:--------------------------:|
| 0.5556 | 200 | - | 0.9272 |
| 1.0 | 360 | - | 0.9318 |
| 1.1111 | 400 | - | 0.9309 |
| 1.3889 | 500 | 0.5286 | - |
| 1.6667 | 600 | - | 0.9355 |
| 2.0 | 720 | - | 0.9378 |
| 2.2222 | 800 | - | 0.9374 |
| 2.7778 | 1000 | 0.2751 | 0.9397 |
| 3.0 | 1080 | - | 0.9397 |
| 0.7407 | 200 | - | 0.9374 |
| 1.0 | 270 | - | 0.9369 |
| 1.4815 | 400 | - | 0.9374 |
| 1.8519 | 500 | 0.2128 | - |
| 2.0 | 540 | - | 0.9383 |
| 2.2222 | 600 | - | 0.9388 |
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 4.1.0
- 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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