Update model card.

README.md

@@ -5,62 +5,45 @@ model_name: tbert-siamese-encoder
 ---
 
 # Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+This repository contains the embedding model used to embed artifact for traceability link prediction.
 
 
 ## Model Details
 
+used in the siamese models
 ### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
+This embedding model is the encoder portion of the siamese model used in the paper cited.  This model utilized a relational classifier 
+to create similarity scores between text pairs resembling a cross-encoder and consistently ranked almost as high as the top performer.
 
 
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
+- **Developed by:** Jinfeng Lin (translated by Alberto Rodriguez)
+- **Model type:** Roberta encoder trained on automatic traceability link prediction.
 - **Language(s) (NLP):** en
 - **License:** mit
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** See Cited Ppaer.
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/jinfenglin/TraceBERT
+- **Paper:** https://arxiv.org/abs/2102.04411
 
 ## 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. -->
+Used to embed software artifacts intended to be compared via cosine similarity.
 
 ### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+Software traceability link prediction, Retrieval Augmented Generation, Artifact Clustering.
 
 ### 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]
+The intended vision for this model within a traceability link prediction pipeline, used to retrieve software artifacts for an LLM prompt, and for clustering.
 
 ### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model could be used for a good set of starting weights for requirements classification.
 
 ## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+This data uses open source git data which can be inaccurate and lead to unexpected results.
 
 ### Recommendations
 
@@ -70,131 +53,23 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 ## 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 Dataset 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. -->
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-[More Information Needed]
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-### Training Procedure
-
-<!-- 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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-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
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-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
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-<!-- 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]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## 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. -->
-
-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
+```
+parent_artifacts = [
+"Display Artifacts",
+]
+texts = [
+    "Display Artifacts", // parent artifact
+    "A table view should be provided to display all project artifacts.", // child 1
+    "The system should be able to generate documentation for a set of artifacts." // child 2
+]
+embeddings = model.encode(texts, convert_to_tensor=False)
+
+parent_embedding = embeddings[0:1]
+children_embeddings = embeddings[1:]
+
+# Compute cosine similarity
+sim_matrix = cosine_similarity(parent_embedding, children_embeddings)
+
+```
+## Training, Evaluation, and Results Details
+Please see cited paper for more information on training method, evaluation, and resuts.