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-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.   
+industry-bert-insurance-v0.1 is part of a series of industry-fine-tuned sentence_transformer embedding models.   
 
 ## Model Details
 
@@ -16,121 +13,41 @@ publicly available materials related to the Insurance industry.
 
 <!-- Provide a longer summary of what this model is. -->
 
+industry-bert-insurance-v0.1 is a domain fine-tuned BERT-based 768-parameter Sentence Transformer model, intended to as a "drop-in" 
+substitute for embeddings in the insurance industry domain.   This model was trained on a wide range of publicly available documents on the insurance industry.
+
 - **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]
+## Model Use
 
-### Out-of-Scope Use
+from transformers import AutoTokenizer, AutoModel
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+tokenizer = AutoTokenizer.from_pretrained("llmware/industry-bert-insurance-v0.1")
+model = AutoModel.from_pretrained("llmware/industry-bert-insurance-v0.1")
 
-[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]
+This is a semantic embedding model, fine-tuned on public domain SEC filings and regulatory 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 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).
+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).
 
-#### 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:
+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",
@@ -162,12 +79,11 @@ Custom training protocol used to train the model, which was derived and inspired
     Published: 12 Jan 2021, Last Modified: 05 May 2023
 }
 
-## Model Card Authors [optional]
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-[More Information Needed]
 
 ## Model Card Contact
 
-[More Information Needed]
+Darren Oberst @ llmware