checkpoint-30/README.md

---
base_model: sentence-transformers/all-mpnet-base-v2
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:80
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What is the short title of this Act?
  sentences:
  - The Banking (Amendment) Act, No. 24 of 2024
  - The Central Bank can assess whether granting a license is in the interest of the
    banking system and national economy
  - They must conduct offshore banking business only with non-residents and certain
    residents as determined by the Central Bank
- source_sentence: What new power does the Central Bank have regarding excess accommodations
    to related parties?
  sentences:
  - Accommodation and all financial investments
  - The Central Bank can assess a bank's track record for operating with good governance
    and integrity
  - The Central Bank may require the bank to deduct such excess from regulatory capital
    calculations
- source_sentence: What new requirement is there for licensed commercial banks regarding
    the disclosure of audit information?
  sentences:
  - Auditors must immediately report findings that could materially affect the bank's
    safety and soundness
  - They must prepare financial statements that represent a true and fair view of
    the bank's financial position
  - To the Governor, Deputy Governor, Assistant Governor, Director of Bank Supervision,
    or other high-ranking officers
- source_sentence: Who approves the appointment, election, or nomination of directors
    for licensed commercial banks?
  sentences:
  - The Central Bank
  - The Central Bank can designate foreign currencies for offshore banking business
  - The Central Bank can request information or documents from any person other than
    a licensed bank
- source_sentence: What new power does the Central Bank have regarding the number
    of directors on a bank's board?
  sentences:
  - Within fifteen days of becoming aware of such facts
  - Transparency in ownership structure and beneficial ownership
  - The Central Bank can determine the number of board members, which shall not be
    less than seven
---

# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2) <!-- at revision 84f2bcc00d77236f9e89c8a360a00fb1139bf47d -->
- **Maximum Sequence Length:** 384 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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 new power does the Central Bank have regarding the number of directors on a bank's board?",
    'The Central Bank can determine the number of board members, which shall not be less than seven',
    'Within fifteen days of becoming aware of such facts',
]
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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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 80 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                             | answer                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 8 tokens</li><li>mean: 16.82 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.85 tokens</li><li>max: 28 tokens</li></ul> |
* Samples:
  | query                                                                                                          | answer                                                                                                                      |
  |:---------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
  | <code>What new power does the Central Bank have regarding corrective action for misleading disclosures?</code> | <code>The Central Bank can require banks to publish corrected disclosure statements</code>                                  |
  | <code>What new requirement is there for licensed commercial banks regarding corporate governance?</code>       | <code>The Board of Directors is ultimately responsible for ensuring compliance with laws and sound banking practices</code> |
  | <code>Who is responsible for overseeing the management of a licensed commercial bank?</code>                   | <code>The Board of Directors</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"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 20 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                             | answer                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           |
  | details | <ul><li>min: 11 tokens</li><li>mean: 17.4 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.2 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
  | query                                                                                                         | answer                                                                                                                                   |
  |:--------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What new power does the Central Bank have regarding the assessment of bank license applications?</code> | <code>The Central Bank can assess whether granting a license is in the interest of the banking system and national economy</code>        |
  | <code>What new definition is provided for 'unlawful activity' in the Act?</code>                              | <code>It has the same meaning as in the Prevention of Money Laundering Act, No. 5 of 2006</code>                                         |
  | <code>What new requirement is there for licensed commercial banks regarding offshore banking?</code>          | <code>They must conduct offshore banking business only with non-residents and certain residents as determined by the Central Bank</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`: epoch
- `per_device_eval_batch_size`: 16
- `learning_rate`: 3e-05
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
- `ddp_find_unused_parameters`: False

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `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
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-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`: 3
- `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`: 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`: True
- `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`: False
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch | Step | Training Loss | loss   |
|:-----:|:----:|:-------------:|:------:|
| 0.2   | 2    | 1.1686        | -      |
| 0.4   | 4    | 1.0061        | -      |
| 0.6   | 6    | 0.8377        | -      |
| 0.8   | 8    | 0.8121        | -      |
| 1.0   | 10   | 0.267         | 0.8161 |
| 1.2   | 12   | 0.3282        | -      |
| 1.4   | 14   | 0.1546        | -      |
| 1.6   | 16   | 0.3546        | -      |
| 1.8   | 18   | 0.0544        | -      |
| 2.0   | 20   | 0.0271        | 0.6723 |
| 2.2   | 22   | 0.1496        | -      |
| 2.4   | 24   | 0.0569        | -      |
| 2.6   | 26   | 0.1682        | -      |
| 2.8   | 28   | 0.0452        | -      |
| 3.0   | 30   | 0.0261        | 0.6644 |


### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.43.1
- PyTorch: 2.3.0
- Accelerate: 0.32.0
- Datasets: 2.19.1
- 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",
}
```

#### 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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