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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- law |
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- australia |
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- legal |
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- auslaw |
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license: apache-2.0 |
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datasets: |
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- umarbutler/open-australian-legal-corpus |
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language: |
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- en |
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--- |
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# AusLaw Embedding Model v1.0 |
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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. |
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<!--- Describe your model here --> |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('adlumal/auslaw-embed-v1.0') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Evaluation Results |
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<!--- Describe how your model was evaluated --> |
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| Test | Score | |
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|------------------------|--------------| |
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| cos_sim-Accuracy@1 | 0.730206301 | |
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| cos_sim-Accuracy@3 | 0.859562308 | |
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| cos_sim-Accuracy@5 | 0.892737664 | |
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| cos_sim-Accuracy@10 | 0.928352384 | |
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| cos_sim-Precision@1 | 0.730206301 | |
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| cos_sim-Recall@1 | 0.730206301 | |
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| cos_sim-Precision@3 | 0.286520769 | |
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| cos_sim-Recall@3 | 0.859562308 | |
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| cos_sim-Precision@5 | 0.178547533 | |
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| cos_sim-Recall@5 | 0.892737664 | |
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| cos_sim-Precision@10 | 0.092835238 | |
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| cos_sim-Recall@10 | 0.928352384 | |
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| cos_sim-MRR@10 | 0.801075782 | |
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| cos_sim-NDCG@10 | 0.832189447 | |
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| cos_sim-MAP@100 | 0.803593645 | |
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| dot_score-Accuracy@1 | 0.730136604 | |
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| dot_score-Accuracy@3 | 0.859562308 | |
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| dot_score-Accuracy@5 | 0.892737664 | |
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| dot_score-Accuracy@10 | 0.928352384 | |
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| dot_score-Precision@1 | 0.730136604 | |
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| dot_score-Recall@1 | 0.730136604 | |
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| dot_score-Precision@3 | 0.286520769 | |
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| dot_score-Recall@3 | 0.859562308 | |
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| dot_score-Precision@5 | 0.178547533 | |
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| dot_score-Recall@5 | 0.892737664 | |
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| dot_score-Precision@10 | 0.092835238 | |
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| dot_score-Recall@10 | 0.928352384 | |
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| dot_score-MRR@10 | 0.801040934 | |
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| dot_score-NDCG@10 | 0.832163724 | |
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| dot_score-MAP@100 | 0.803558796 | |
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## Training |
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The model was trained with the parameters: |
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**DataLoader**: |
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`torch.utils.data.dataloader.DataLoader` of length 2583 with parameters: |
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``` |
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{'batch_size': 50, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} |
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``` |
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**Loss**: |
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`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: |
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``` |
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{'scale': 20.0, 'similarity_fct': 'cos_sim'} |
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``` |
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Parameters of the fit()-Method: |
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``` |
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{ |
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"epochs": 2, |
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"evaluation_steps": 50, |
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"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", |
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"max_grad_norm": 1, |
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"optimizer_class": "<class 'torch.optim.adamw.AdamW'>", |
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"optimizer_params": { |
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"lr": 2e-05 |
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}, |
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"scheduler": "WarmupLinear", |
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"steps_per_epoch": null, |
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"warmup_steps": 516, |
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"weight_decay": 0.01 |
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} |
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``` |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(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}) |
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(2): Normalize() |
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) |
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``` |
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## Citing & Authors |
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```bibtex |
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@misc{malec-2024-auslaw-embed-v1, |
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author = {Malec, Adrian Lucas}, |
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year = {2024}, |
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title = {AusLaw Embedding v1.0}, |
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publisher = {Hugging Face}, |
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version = {1.0}, |
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url = {https://huggingface.co/adlumal/auslaw-embed-v1.0} |
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} |
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``` |