
# Sentence Transformer for Assurance & Risk Question-Answering (STAR-QA)

Sentence Transformer for Assurance & Risk Question-Answering (STAR-QA) is a fine-tuned [sentence-transformers](https://www.SBERT.net) model based on ALL-MPNET-BASE-V2. It has been developed to produce **high-performance embeddings for audit, risk-management, compliance and associated regulatory documents**. The model maps sentence pairs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search as part of retrieval-augmented generation pipelines.

## Evaluation Results

The model was evaluated on a held-out sample from the STAR-QA dataset (see below) using `sentence-transformers.InformationRetrievalEvaluator`. Reported metrics include cosine similarity of retrieved documents w/r/t ground truth P/R @ 3 candidates, as well as MRR @ 10, MAP @ 10 and NDCG @ 100. This fine-tuned model was also benchmarked against its base model using the same methodology.

| Metric       | STAR-QA Score | ALL-MPNET-BASE-V2 Score |
|--------------|---------------|-------------------------|
|Precision @ 3 |          0.315|                    0.215|
|Recall @ 3    |          0.324|                    0.223|
|MRR @ 10      |          0.887|                    0.578|
|NDCG @ 10     |           0.44|                    0.303|
|MAP @ 100     |          0.316|                    0.209|

## Training Data

The model was fine-tuned on a corpus of audit, risk-management, compliance and associated regulatory documents sourced from the public internet. Documents were cleaned and chunked into 2-sentence blocks. Each block was then sent to a state-of-the-art LLM with the following prompt: "Write a question about {document_topic} for which this is the answer: {block}"

The resulting question and its associated ground-truth answer (collectively a "pair") constitute a single training example for the fine-tuning step. The final model was fine-tuned on ~18K such pairs.

## Training
The model was fine-tuned with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 634 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 1,
    "evaluation_steps": 50,
    "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 10000,
    "weight_decay": 0.01
}
```

## 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})
  (2): Normalize()
)
```

## Citing & Authors

@misc{Theron_2024, 
  title={Sentence Transformer for Assurance &#38; Risk Question-Answering (STAR-QA)},
  url={https://huggingface.co/dptrsa/STAR-QA},
  author={Theron, Daniel},
  year={2024},
  month={Feb}
}
{"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"}
sentence-transformers safetensors mpnet feature-extraction sentence-similarity license:apache-2.0 endpoints_compatible region:us