---
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 aging management process for concrete structures in
nuclear power plants?
sentences:
- Loss of material (spalling, scaling) and cracking due to freeze-thaw is a significant
consideration in the aging management of concrete used for structural support
and water retaining boundaries in nuclear power plants.
- The NRC requires thorough inspections of various reactor components to ensure
compliance with regulatory safety standards.
- The applicability of fuel fragmentation and relocation models considering the
full set of experimental test data presently available and higher fuel burnups
being proposed in WCAP-18850-P.
- source_sentence: control rod sequence during reactor startup
sentences:
- With the RWM inoperable during a reactor startup, the operator is still capable
of enforcing the prescribed control rod sequence. However, the overall reliability
is reduced because a single operator error can result in violating the control
rod sequence.
- With the RWM inoperable during a reactor shutdown, the operator is still capable
of enforcing the prescribed control rod sequence.
- Potential ecological impacts during aquifer restoration will be SMALL, since surface
disturbing activities will be limited. Potential impacts associated with aquifer
restoration activities will include vegetation and habitat alteration due to the
response and cleanup of potential spills, noxious weeds, wildlife mortality from
vehicle collisions and displacement due to noise, dust, and human/mechanical presence.
Potential impacts are expected to be less than during operation due to a smaller
workforce and decreased traffic.
- source_sentence: What measurement units are used for reactor coolant temperatures?
sentences:
- The coolant temperatures in the reactors are measured in degrees Celsius (°C).
- The reports frequently summarize operational data over time spans such as weeks
and months.
- 'MDA: Minimum Detectable Activity refers to the lowest quantity of radioactive
material that can be reliably detected by a particular measurement method.'
- source_sentence: The NRC mandates specific designs for the safety baskets in boiling
water reactors to ensure optimal performance.
sentences:
- COBRA-FLX is a computer code developed for the prediction of critical heat flux
(CHF) in light water reactor thermal-hydraulic systems, and it has been validated
against a wide range of experimental data to ensure its accuracy.
- Figure B2-5 presents the schematic of the BWR 81-Assembly Basket, which is essential
for the containment and organization of nuclear fuel assemblies.
- The arrangement of different materials in a reactor core affects the thermal efficiency
greatly, especially under high-pressure conditions.
- source_sentence: boric acid solution in nuclear reactors
sentences:
- A temperature sensor provides temperature measurement of each tank's contents.
Temperature indication is provided as well as high and low temperature alarms
which are indicated on the main control board.
- The calculated dose values at different locations are essential for ensuring compliance
with safety standards.
- The concentration of boric acid solution in storage is maintained between 4 and
4.4 percent by weight. Periodic manual sampling and corrective action, if necessary,
assures that tanks are maintained.
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: 0.9373840689659119
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)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### 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 = [
'boric acid solution in nuclear reactors',
'The concentration of boric acid solution in storage is maintained between 4 and 4.4 percent by weight. Periodic manual sampling and corrective action, if necessary, assures that tanks are maintained.',
"A temperature sensor provides temperature measurement of each tank's contents. Temperature indication is provided as well as high and low temperature alarms which are indicated on the main control board.",
]
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]
```
## Evaluation
### Metrics
#### Triplet
* Dataset: `validation`
* Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9374** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 8,622 training samples
* Columns: sentence_0, sentence_1, and sentence_2
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 4 tokens
- mean: 14.85 tokens
- max: 41 tokens
| - min: 6 tokens
- mean: 43.91 tokens
- max: 336 tokens
| - min: 4 tokens
- mean: 30.45 tokens
- max: 228 tokens
|
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:---------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| The NRC mandates monitoring and calibration of voltage outputs during reactor startup procedures. | Simulates pressing GEN FIELD FLASH pushbutton until Generator voltage stops rising. (GENERATOR LINE VOLTS should indicate ~260V). | Operator reads the Note and continues. |
| What are the environmental impacts of nuclear facilities during decommissioning? | The Environmental Impacts Comparison Summary provides a detailed analysis of various impact areas associated with the proposed action compared to the termination of operations and decommissioning of nuclear facilities. | The report outlines the thermal efficiency of natural gas combined cycle (NGCC) power plants, which can influence operational costs and the decision-making process for future energy projects. |
| ECCS signal path | The ECCS/NSSSS Signal Path is crucial for the timely activation of emergency core cooling systems to prevent overheating of the reactor core during an accident. | The integration of signal pathways within reactor control systems ensures effective monitoring and response capability, which is essential for maintaining safety standards in nuclear operations. |
* Loss: [MultipleNegativesRankingLoss](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
Click to expand
- `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