Upload fine-tuned BGE embeddings model for nuclear licensing search

58c1d9a verified 6 months ago

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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>

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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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