--- 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) - **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 = [ '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] ``` ## 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** | **0.8797** | #### Triplet * Dataset: `validation` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.9557** | #### Triplet * Dataset: `validation` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.9589** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 2,525 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 | | | | * Samples: | sentence_0 | sentence_1 | sentence_2 | |:---------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What is the significance of groundwater monitoring wells in nuclear safety assessments? | 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) | 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 | | An analysis of the stresses experienced by reactor vessel studs is crucial for evaluating operational safety. | 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. | Regulatory filings often require extensive documentation to demonstrate compliance with safety protocols. | | An assessment was carried out to determine the potential for liquefaction at the EGC ESP Site. | An evaluation of liquefaction potential was conducted at the EGC ESP Site. | 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. | * 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`: 24 - `per_device_eval_batch_size`: 24 - `num_train_epochs`: 5 - `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`: 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
### 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} } ```