AusLaw Embedding Model v1.0
This is a sentence-transformers 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 using the HCA case law in the 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.
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 installed:
pip install -U sentence-transformers
Then you can use the model like this:
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
@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}
}