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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-insurance-v0.1 is part of a series of industry-fine-tuned sentence_transformer embedding models.  
+
+BERT-based 768-parameter drop-in substitute for non-industry-specific embeddings model.   This model was trained on a wide range of 
+publicly available materials related to the Insurance industry.   
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **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]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model is intended to be used as a sentence embedding model, specifically for the Asset Management and financial industries.
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### 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 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).
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+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
+}
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+