README.md

---
base_model: BAAI/bge-base-en-v1.5
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:14271
- loss:BatchAllTripletLoss
widget:
- source_sentence: In a complex legal scenario involving multiple jurisdictions, how
    would you navigate the differences in laws related to online privacy violations
    and harassment?
  sentences:
  - How does voluntary admission under the Baker Act impact eligibility for a Concealed
    Weapon Permit?
  - How do the terms of the account and the circumstances impact the potential liability
    of the Bank of Hawaii in this situation?
  - Can someone run a background check on you without your consent?
- source_sentence: How long is the Kansas Lemon Law effective for?
  sentences:
  - What should I do to stop my neighbor from using my land and barn?
  - How does the expungement of an arrest impact the disclosure requirements in applications
    for permits or licenses?
  - If a policy is canceled due to a denied claim, does the canceled policy still
    cover injuries from the incident?
- source_sentence: What are the implications of a guilty plea without corroborating
    evidence in terms of justice and fairness?
  sentences:
  - How does having a Series 7 license impact the ability of a financial planner to
    sell securities products?
  - What are the specific state laws that govern the relationship between the Baker
    Act and Concealed Weapon Permits?
  - How does the duration of copyright protection impact the entry of works into the
    public domain?
- source_sentence: How can one prove the terms and existence of a verbal contract?
  sentences:
  - Is it common for search warrants to be obtained under a unique cause number?
  - In what ways can transparency in background check forms contribute to national
    security measures?
  - What are the potential legal responsibilities of the 14-year-old boy if he is
    determined to be the father of the baby?
- source_sentence: How can the person ensure they receive the necessary compensation
    for their work-related injury?
  sentences:
  - Is there a law in Oklahoma that restricts the distance of a dispensary to a baseball
    field?
  - Considering the complexities of property rights, due process, and public safety,
    what are the ethical and legal considerations surrounding citizens taking possession
    of unattended animals in public areas, and how do these actions intersect with
    constitutional rights and property laws?
  - What precedent cases or legal doctrines could be relevant in a lawsuit against
    the town council person and the township in this scenario?
---

# 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 tokens
- **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("Muhammad2003/router-embedding")
# Run inference
sentences = [
    'How can the person ensure they receive the necessary compensation for their work-related injury?',
    'Is there a law in Oklahoma that restricts the distance of a dispensary to a baseball field?',
    'Considering the complexities of property rights, due process, and public safety, what are the ethical and legal considerations surrounding citizens taking possession of unattended animals in public areas, and how do these actions intersect with constitutional rights and property laws?',
]
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.*
-->

<!--
## 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: 14,271 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence                                                                          | label                                                                                 |
  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                            | int                                                                                   |
  | details | <ul><li>min: 2 tokens</li><li>mean: 23.55 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>0: ~25.00%</li><li>1: ~25.00%</li><li>2: ~25.00%</li><li>3: ~25.00%</li></ul> |
* Samples:
  | sentence                                                                                                                     | label          |
  |:-----------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code>What rights do you have regarding accessing your medical records under HIPAA?</code>                                   | <code>1</code> |
  | <code>What should you do if you lose access to your patient portal after being discharged from a healthcare provider?</code> | <code>1</code> |
  | <code>How can you address the issue of losing access to your patient portal with the pain management office?</code>          | <code>3</code> |
* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: True
- `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}
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.1121 | 100  | 4.9663        |
| 0.2242 | 200  | 4.7529        |
| 0.3363 | 300  | 4.5009        |
| 0.4484 | 400  | 4.4893        |
| 0.5605 | 500  | 4.3914        |
| 0.6726 | 600  | 4.4306        |
| 0.7848 | 700  | 4.5464        |
| 0.8969 | 800  | 4.4952        |
| 1.0090 | 900  | 4.349         |
| 1.1211 | 1000 | 4.4221        |
| 1.2332 | 1100 | 4.4476        |
| 1.3453 | 1200 | 4.4516        |
| 1.4574 | 1300 | 4.3968        |
| 1.5695 | 1400 | 4.3283        |
| 1.6816 | 1500 | 4.3894        |
| 1.7937 | 1600 | 4.42          |
| 1.9058 | 1700 | 4.4457        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.1.0+cu118
- Accelerate: 0.33.0.dev0
- Datasets: 2.20.0
- Tokenizers: 0.19.1

## 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",
}
```

#### BatchAllTripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

<!--
## 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.*
-->