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--- |
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language: en |
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license: mit |
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model_name: tbert-siamese-encoder |
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--- |
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# Model Card for Model ID |
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This repository contains the embedding model used to embed artifact for traceability link prediction. |
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## Model Details |
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used in the siamese models |
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### Model Description |
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This embedding model is the encoder portion of the siamese model used in the paper cited. This model utilized a relational classifier |
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to create similarity scores between text pairs resembling a cross-encoder and consistently ranked almost as high as the top performer. |
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- **Developed by:** Jinfeng Lin (translated by Alberto Rodriguez) |
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- **Model type:** Roberta encoder trained on automatic traceability link prediction. |
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- **Language(s) (NLP):** en |
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- **License:** mit |
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- **Finetuned from model [optional]:** See Cited Ppaer. |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/jinfenglin/TraceBERT |
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- **Paper:** https://arxiv.org/abs/2102.04411 |
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## Uses |
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Used to embed software artifacts intended to be compared via cosine similarity. |
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### Direct Use |
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Software traceability link prediction, Retrieval Augmented Generation, Artifact Clustering. |
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### Downstream Use [optional] |
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The intended vision for this model within a traceability link prediction pipeline, used to retrieve software artifacts for an LLM prompt, and for clustering. |
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### Out-of-Scope Use |
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This model could be used for a good set of starting weights for requirements classification. |
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## Bias, Risks, and Limitations |
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This data uses open source git data which can be inaccurate and lead to unexpected results. |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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``` |
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parent_artifacts = [ |
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"Display Artifacts", |
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] |
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texts = [ |
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"Display Artifacts", // parent artifact |
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"A table view should be provided to display all project artifacts.", // child 1 |
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"The system should be able to generate documentation for a set of artifacts." // child 2 |
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] |
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embeddings = model.encode(texts, convert_to_tensor=False) |
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parent_embedding = embeddings[0:1] |
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children_embeddings = embeddings[1:] |
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# Compute cosine similarity |
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sim_matrix = cosine_similarity(parent_embedding, children_embeddings) |
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``` |
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## Training, Evaluation, and Results Details |
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Please see cited paper for more information on training method, evaluation, and resuts. |
