Upload fine-tuned BGE embeddings model for nuclear licensing search

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,439 @@
+---
+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
+
+![Evaluation against BAAI/bge-base-en-v1.5](eval.png)
+
+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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## 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** |
+
+<!--
+## 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: 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,439 @@
+---
+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
+
+![Evaluation against BAAI/bge-base-en-v1.5](eval.png)
+
+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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## 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** |
+
+<!--
+## 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: 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,31 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.51.3",
+    "pytorch": "2.2.2"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

eval/.ipynb_checkpoints/triplet_evaluation_validation_results-checkpoint.csv

@@ -0,0 +1,6 @@
+epoch,steps,accuracy_cosine
+1.0,270,0.9369202256202698
+2.0,540,0.9383116960525513
+3.0,810,0.9364563822746277
+4.0,1080,0.9373840689659119
+5.0,1350,0.9364563822746277

eval/triplet_evaluation_validation_results.csv

@@ -0,0 +1,6 @@
+epoch,steps,accuracy_cosine
+1.0,270,0.9369202256202698
+2.0,540,0.9383116960525513
+3.0,810,0.9364563822746277
+4.0,1080,0.9373840689659119
+5.0,1350,0.9364563822746277

model.safetensors

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+oid sha256:d21e11fe473fec2293864218b534331c2639d140957aa65146975eb4c7d2d99e
+size 437951328

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}