---
license: apache-2.0
---
# Model Card for Model ID

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industry-bert-contracts-v0.1 is part of a series of industry-fine-tuned sentence_transformer embedding models.   

## Model Details

### Model Description

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BERT-based 768-parameter drop-in substitute for non-industry-specific embeddings model.   This model was trained on a wide range of 
publicly available commercial contracts, including open source contract datasets. 

- **Developed by:** llmware
- **Shared by [optional]:** Darren Oberst
- **Model type:** BERT-based Industry domain fine-tuned Sentence Transformer architecture
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model [optional]:** BERT-based model, fine-tuning methodology described below.

### Model Sources [optional]

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- **Repository:** [More Information Needed]
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## Uses

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### Direct Use

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This model is intended to be used as a sentence embedding model, specifically for contracts use cases.

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Training Procedure 

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This model was fine-tuned using a custom self-supervised procedure that combined contrastive techniques with stochastic injections of 
distortions in the samples.  The methodology was derived, adapted and inspired primarily from three research papers cited below:
TSDAE (Reimers), DeClutr (Giorgi), and Contrastive Tension (Carlsson).

#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Metrics

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### Results

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#### Summary


### Model Architecture and Objective

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## Citation [optional]

Custom training protocol used to train the model, which was derived and inspired by the following papers:

@article{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and  Gurevych, Iryna", 
    journal= "arXiv preprint arXiv:2104.06979",
    month = "4",
    year = "2021",
    url = "https://arxiv.org/abs/2104.06979",
}

@inproceedings{giorgi-etal-2021-declutr,
    title        = {{D}e{CLUTR}: Deep Contrastive Learning for Unsupervised Textual Representations},
    author       = {Giorgi, John  and Nitski, Osvald  and Wang, Bo  and Bader, Gary},
    year         = 2021,
    month        = aug,
    booktitle    = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
    publisher    = {Association for Computational Linguistics},
    address      = {Online},
    pages        = {879--895},
    doi          = {10.18653/v1/2021.acl-long.72},
    url          = {https://aclanthology.org/2021.acl-long.72}
}

@article{Carlsson-2021-CT,
      title =  {Semantic Re-tuning with Contrastive Tension},
      author=  {Fredrik Carlsson, Amaru Cuba Gyllensten, Evangelia Gogoulou, Erik Ylipää Hellqvist, Magnus Sahlgren},
        year=  {2021},
        month= {"January"}
    Published: 12 Jan 2021, Last Modified: 05 May 2023
}

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## Model Card Authors [optional]

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## Model Card Contact

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