Upload README.md

README.md

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 license: apache-2.0
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+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+industry-bert-contracts-v0.1 is part of a series of industry-fine-tuned sentence_transformer embedding models.   
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+industry-bert-contracts-v0.1 is a domain fine-tuned BERT-based 768-parameter Sentence Transformer model, intended to as a "drop-in" 
+substitute for contractual and legal domains.   This model was trained on a wide range of publicly available commercial contracts, 
+including open source contract datasets. 
+
+- **Developed by:** llmware
+- **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 Use
+
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("llmware/industry-bert-contracts-v0.1")
+model = AutoModel.from_pretrained("llmware/industry-bert-contracts-v0.1")
+
+
+## Bias, Risks, and Limitations
+
+This is a semantic embedding model, fine-tuned on public domain contracts and related documents.   Results may vary if used outside of this
+domain, and like any embedding model, there is always the potential for anomalies in the vector embedding space.   No specific safeguards have
+put in place for safety or mitigate potential bias in the dataset.
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+This model was fine-tuned using a custom self-supervised procedure and custom dataset 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).
+
+
+## Citation [optional]
+
+Custom self-supervised 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
+}
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+
+## Model Card Contact
+
+Darren Oberst @ llmware
+
+