
# AusLaw Embedding Model v1.0

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.

This model is a fine-tune of [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) using the HCA case law in the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus) by Umar Butler. The PDF/OCR cases were not used. 

The cases were split into < 512 context chunks using the bge-small-en tokeniser and [semchunk](https://github.com/umarbutler/semchunk).

[mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) was used to generate a legal question for each context chunk.

129,137 context-question pairs were used for training. 

14,348 context-question pairs were used for evaluation (see the table below for results).

Using a 10% subset of the val dataset the following hit-rate performance was reached and is compared to the base model and OpenAI's default ada embedding model.

| **Model**                 | **Avg. hit-rate** |
|---------------------------|-------------------|
| BAAI/bge-small-en         | 89%               |
| OpenAI                    | 92%               |
| adlumal/auslaw-embed-v1.0 | **97%**           |

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('adlumal/auslaw-embed-v1.0')
embeddings = model.encode(sentences)
print(embeddings)
```

## Evaluation Results

The model was evauluated on 10% of the available data. The automated eval results for the final step are presented below.

| Eval                   | Score        |
|------------------------|--------------|
| cos_sim-Accuracy@1     | 0.730206301  |
| cos_sim-Accuracy@3     | 0.859562308  |
| cos_sim-Accuracy@5     | 0.892737664  |
| cos_sim-Accuracy@10    | 0.928352384  |
| cos_sim-Precision@1    | 0.730206301  |
| cos_sim-Recall@1       | 0.730206301  |
| cos_sim-Precision@3    | 0.286520769  |
| cos_sim-Recall@3       | 0.859562308  |
| cos_sim-Precision@5    | 0.178547533  |
| cos_sim-Recall@5       | 0.892737664  |
| cos_sim-Precision@10   | 0.092835238  |
| cos_sim-Recall@10      | 0.928352384  |
| cos_sim-MRR@10         | 0.801075782  |
| cos_sim-NDCG@10        | 0.832189447  |
| cos_sim-MAP@100        | 0.803593645  |
| dot_score-Accuracy@1   | 0.730136604  |
| dot_score-Accuracy@3   | 0.859562308  |
| dot_score-Accuracy@5   | 0.892737664  |
| dot_score-Accuracy@10  | 0.928352384  |
| dot_score-Precision@1  | 0.730136604  |
| dot_score-Recall@1     | 0.730136604  |
| dot_score-Precision@3  | 0.286520769  |
| dot_score-Recall@3     | 0.859562308  |
| dot_score-Precision@5  | 0.178547533  |
| dot_score-Recall@5     | 0.892737664  |
| dot_score-Precision@10 | 0.092835238  |
| dot_score-Recall@10    | 0.928352384  |
| dot_score-MRR@10       | 0.801040934  |
| dot_score-NDCG@10      | 0.832163724  |
| dot_score-MAP@100      | 0.803558796  |

## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 2583 with parameters:
```
{'batch_size': 50, 'sampler': 'torch.utils.data.sampler.SequentialSampler', '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": 2,
    "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": 516,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Normalize()
)
```

## Citing & Authors

```bibtex
@misc{malec-2024-auslaw-embed-v1,
    author = {Malec, Adrian Lucas},
    year = {2024},
    title = {AusLaw Embedding v1.0},
    publisher = {Hugging Face},
    version = {1.0},
    url = {https://huggingface.co/adlumal/auslaw-embed-v1.0}
}
```
{"language": ["en"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "law", "australia", "legal", "auslaw"], "datasets": ["umarbutler/open-australian-legal-corpus"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch bert feature-extraction sentence-similarity law australia legal auslaw en dataset:umarbutler/open-australian-legal-corpus license:apache-2.0 endpoints_compatible region:us