https://github.com/mya-mya/chatmpc
Natural Language-driven Controller Personalization Framework 📣
https://github.com/mya-mya/chatmpc
control-engineering natural-language-processing
Last synced: about 1 year ago
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Natural Language-driven Controller Personalization Framework 📣
- Host: GitHub
- URL: https://github.com/mya-mya/chatmpc
- Owner: Mya-Mya
- License: mit
- Created: 2023-06-26T02:29:30.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-02-28T05:35:57.000Z (over 2 years ago)
- Last Synced: 2025-03-31T02:11:40.439Z (about 1 year ago)
- Topics: control-engineering, natural-language-processing
- Language: MATLAB
- Homepage:
- Size: 21.5 KB
- Stars: 8
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## ChatMPC
## Reproduce the Numerical Experiment
### 1. Download Sentence BERT Pre-trained Weights
Sentence BERT model is used in the intent extractor $f_\mathrm{int}$ in the interpreter $\mathcal{A}$. In the numerical experiment, we used `deepset/sentence_bert` as the pre-trained weights.
Clone it from the [HuggingFace Model page](https://huggingface.co/deepset/sentence_bert).
### 2. Launch the IntentExtractor Python Server
Most of ChatMPC is implemented in MATLAB, but only the intent extractor $f_\mathrm{int}$ is implemented in Python. MATLAB code and the Python code communicate via HTTP.
Recall the path to the Sentence BERT folder, and execute the following:
```bash
>> cd IntentExtractor
>> python3 main.py --sbert_path
```
Check that the server is running on `http://127.0.0.1:3838`.
### 3. Work in MATLAB
The code for the numerical experiment is `main.m`.
Please run it **SECTION BY SECTION** because I'll need you to type/run some commands yourself in the middle of running.