Datasets:
Tasks:
Text2Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
License:
license: apache-2.0 | |
task_categories: | |
- text2text-generation | |
language: | |
- en | |
size_categories: | |
- 1M<n<10M | |
# ModelicaDat_v1.0 | |
Total entries: 3935 | |
## Dataset Collection Pipeline | |
#### 1. Source Identification (Manual) | |
Identify and select open-source repositories containing models and libraries with clean code and well-written descriptions. | |
#### 2. Data Collection (Automated) | |
For each model in the selected repositories, extract the following information: | |
* Name | |
* Location | |
* Type | |
* Code | |
* Description | |
Store this information in a JSONL file. | |
#### 3. Data Cleaning (Manual & Automated) | |
Remove irrelevant descriptions and overly long code snippets through a combination of automated scripts and manual review. Specifically: | |
* Exclusions: | |
* Omit visualization resources, such as icons and visualization-specific components. | |
* Exclude human-oriented text descriptions (e.g., "UsersGuide"). | |
* Skip test components like "ModelicaTest." | |
* Annotations Handling: | |
* Use documentation found within annotations as descriptions, if available. | |
* Remove annotations containing only visualization details. | |
#### 4. Pairing and Storage (Automated) | |
Convert the cleaned data into text (description) and code (model) pairs. | |
Save these pairs in a JSONL file format. | |
#### 5. Prompt Generation and Enhancement (Automated) | |
Utilize an LLM to optimize and transform each text description into a more structured prompt, such as: "Generate a model/package using the xxx library for [specific purpose]." | |
Update the text entries with these refined prompts in the JSONL file. | |
#### 6. Final Cleanup (Manual) | |
Conduct a final manual review to ensure all entries are accurate, relevant, and ready for fine-tuning. | |
## Error Handling | |
To improve the dataset's utility, common Modelica modeling errors and their solutions have been included. These entries help users identify and resolve typical issues, benefiting both beginners and experienced users. | |
The pipeline utilizes an LLM and an experienced Modelica user to generate and verify error-handling entries, ensuring that solutions are both practical and actionable. | |
## List of Considered Repos | |
The current focus is on energy systems modeling. Therefore, only a representative repositories in these fields have been selected. | |
| Repo | Version & Release Date | Description | Number of Entries | | |
| :----------------------------------------------------------: | :--------------------: | :----------------------------------------------------------: | :---------------------: | | |
| [Standard Library](https://github.com/modelica/ModelicaStandardLibrary) | v4.1.0 (2024-02-06) | Modelica Standard library | 1806 | | |
| [AixLib](https://github.com/modelica-3rdparty/AixLib) | v2.0.0 (2024-08-19) | Building performance simulations | 2029 | | |
| [PhotoVoltaics](https://github.com/modelica-3rdparty/PhotoVoltaics) | v2.0.0 (2021-07-19) | Simulation of photo voltaic cells and modules | 47 | | |
| [OMCompiler](https://github.com/OpenModelica/OMCompiler/tree/master/Examples) | - | Collection of basic examples | 6 | | |
| [Modelica University](https://mbe.modelica.university) | - | Classic examples | 27 | | |
| Error Handling | - | Prompt pairs for error handling | 20 | |