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license: apache-2.0 |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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industry-bert-contracts-v0.1 is part of a series of industry-fine-tuned sentence_transformer embedding models. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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industry-bert-contracts-v0.1 is a domain fine-tuned BERT-based 768-parameter Sentence Transformer model, intended to as a "drop-in" |
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substitute for contractual and legal domains. This model was trained on a wide range of publicly available commercial contracts, |
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including open source contract datasets. |
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- **Developed by:** llmware |
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- **Model type:** BERT-based Industry domain fine-tuned Sentence Transformer architecture |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Finetuned from model [optional]:** BERT-based model, fine-tuning methodology described below. |
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## Model Use |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("llmware/industry-bert-contracts-v0.1") |
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model = AutoModel.from_pretrained("llmware/industry-bert-contracts-v0.1") |
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## Bias, Risks, and Limitations |
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This is a semantic embedding model, fine-tuned on public domain contracts and related documents. Results may vary if used outside of this |
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domain, and like any embedding model, there is always the potential for anomalies in the vector embedding space. No specific safeguards have |
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put in place for safety or mitigate potential bias in the dataset. |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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This model was fine-tuned using a custom self-supervised procedure and custom dataset that combined contrastive techniques |
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with stochastic injections of distortions in the samples. The methodology was derived, adapted and inspired primarily from |
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three research papers cited below: TSDAE (Reimers), DeClutr (Giorgi), and Contrastive Tension (Carlsson). |
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## Citation [optional] |
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Custom self-supervised training protocol used to train the model, which was derived and inspired by the following papers: |
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@article{wang-2021-TSDAE, |
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title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning", |
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author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna", |
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journal= "arXiv preprint arXiv:2104.06979", |
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month = "4", |
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year = "2021", |
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url = "https://arxiv.org/abs/2104.06979", |
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} |
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@inproceedings{giorgi-etal-2021-declutr, |
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title = {{D}e{CLUTR}: Deep Contrastive Learning for Unsupervised Textual Representations}, |
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author = {Giorgi, John and Nitski, Osvald and Wang, Bo and Bader, Gary}, |
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year = 2021, |
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month = aug, |
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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)}, |
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publisher = {Association for Computational Linguistics}, |
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address = {Online}, |
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pages = {879--895}, |
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doi = {10.18653/v1/2021.acl-long.72}, |
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url = {https://aclanthology.org/2021.acl-long.72} |
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} |
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@article{Carlsson-2021-CT, |
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title = {Semantic Re-tuning with Contrastive Tension}, |
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author= {Fredrik Carlsson, Amaru Cuba Gyllensten, Evangelia Gogoulou, Erik Ylipää Hellqvist, Magnus Sahlgren}, |
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year= {2021}, |
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month= {"January"} |
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Published: 12 Jan 2021, Last Modified: 05 May 2023 |
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} |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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## Model Card Contact |
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Darren Oberst @ llmware |
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