Improve language tag

README.md

@@ -1,154 +1,166 @@
----
-license: mit
-datasets:
-- SIA-IDE/MBHM
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-1.5B-Instruct
----
-<div align="center">
-<a href="https://github.com/SIA-IDE/BearLLM">
-<img src="https://raw.githubusercontent.com/SIA-IDE/BearLLM/refs/heads/main/docs/images/logo.svg" width="200" alt="logo"/>
-</a>
-<h1>BearLLM: A Prior Knowledge-Enhanced Bearing Health Management Framework with Unified Vibration Signal Representation</h1>
-
-<a href="https://www.python.org/"><img alt="Python" src="https://img.shields.io/badge/Python-3.12-blue"></a>
-<a href="https://pytorch.org/"><img alt="PyTorch" src="https://img.shields.io/badge/Pytorch-latest-orange"></a>
-<a href="https://arxiv.org/abs/2408.11281"><img alt="arXiv" src="https://img.shields.io/badge/Paper-arXiv-B31B1B"></a>
-<a href="https://huggingface.co/datasets/SIA-IDE/MBHM"><img alt="Dataset" src="https://img.shields.io/badge/Dataset-🤗-FFFDF5"></a>
-<a href="https://github.com/SIA-IDE/BearLLM"><img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/SIA-IDE/BearLLM"></a>
-</div>
-
-<h4 align="center">
-    <p>
-        <b>English</b> |
-        <a href="https://github.com/SIA-IDE/BearLLM/blob/main/docs/README_zh.md">简体中文</a>
-    </p>
-</h4>
-
-## 🔥 NEWS
-- **[2025-04-11]** 🎉 The [AAAI-25 Proceedings](https://aaai.org/proceeding/aaai-39-2025/) are now officially published! Our [conference paper](https://ojs.aaai.org/index.php/AAAI/article/view/34188) is included. We welcome you to read and cite it!
-- **[2025-03-06]** 🌟 The complete dataset and code are now officially open source!
-- **[2024-12-11]** ⏫ We are now working on making the code of BearLLM public. Stay tuned!
-- **[2024-12-10]** 🎉 The BearLLM paper is accepted by the Thirty-Ninth AAAI Conference on Artificial Intelligence ([AAAI-25](https://aaai.org/conference/aaai/aaai-25/)).
-- **[2024-08-21]** 📝 The preprint of the BearLLM paper is available on arXiv. Check the [paper page](https://arxiv.org/abs/2408.11281) for more details.
-
-## 📅 TODO
-- [ ] Improve related comments and documentation.
-- [x] Upload the complete BearLLM demo code.
-- [x] Upload the health management corpus of the MBHM dataset.
-- [x] Collect the codes for pre-training and fine-tuning BearLLM.
-- [x] Collect the codes of BearLLM's classification network and other comparison models.
-- [x] Upload the vibration signal portion of the MBHM dataset.
-
-## 📚 Introduction
-The [MBHM](https://huggingface.co/datasets/SIA-IDE/MBHM) dataset is the first multimodal dataset designed for the study of bearing health management. It is divided into two parts: vibration signals and health management corpus. The vibration signals and condition information are derived from 9 publicly available datasets, and are still under continuous updating and improvement. The thousands of working conditions pose more difficult challenges for the identification model and better represent real-world usage scenarios.
-
-[BearLLM](https://github.com/SIA-IDE/BearLLM) is a prior knowledge-enhanced bearing health management framework with a unified vibration signal representation. This framework transforms the signal to be tested into the frequency domain, enabling effective identification of spectral differences compared to the vibration signal under fault-free conditions. By aligning the vibration signal with the fault semantic embedding, we achieve a unified natural language response for various health management tasks through a fine-tuned language model with low computational overhead. Experiments demonstrate that this framework achieves leading performance under thousands of working conditions.
-
-## 💻 Requirements
-
-The code is implemented in Python 3.12. The required packages are listed in the `requirements.txt` file. You can install the required packages by running the following command:
-
-```bash
-conda create --name bearllm python=3.12
-conda activate bearllm
-pip install -r requirements.txt
-```
-
-
-## 🚀 Quick Start
-
-### 1. Download Demo Data / Use Your Own Data
-
-First, you need to download the `demo_data.json` from the [MBHM](https://huggingface.co/datasets/SIA-IDE/MBHM/tree/main) dataset.
-For users in mainland China, you can use the [mirror link](https://hf-mirror.com/datasets/SIA-IDE/MBHM/tree/main) to speed up the download:
-
-Or, you can also build your own test data in the same format:
-`instruction`: Text instruction for health management task.
-`vib_data`: Vibration signal data to be identified, with a required duration of 1 second.
-`ref_data`: Reference vibration signal data without faults, with a required duration of 1 second.
-
-```json
-{
-    "instruction": "xxx.",
-    "vib_data": [1.0, 0.0, 1.0, ...],
-    "ref_data": [1.0, 0.0, 1.0, ...],
-}
-```
-
-### 2. Download Weights
-
-You can download the pre-trained weights of [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/tree/main) from Hugging Face.
-
-Additionally, you need to download the weights of [BearLLM](https://huggingface.co/SIA-IDE/BearLLM/tree/main).
-
-### 3. Organize Files
-
-It is recommended to organize the weights and test data as follows:
-
-```
-BearLLM/
-├── qwen_weights/
-│   ├── model.safetensors
-│   ├── tokenizer.json
-│   ├── config.json
-│   └── other files...
-├── bearllm_weights/
-│   ├── vibration_adapter.pth
-│   ├── adapter_config.json
-│   └── adapter_model.safetensors
-└── mbhm_dataset/
-    └── demo_data.json 
-```
-
-### 4. Run Code
-First, copy the `.env.example` file to `.env` and modify the data paths inside.
-Then, you can run the code using the following command:
-
-```bash
-python run_demo.py
-```
-
-## ⚙️ Development
-
-### 1. Download Dataset
-
-First, you need to download the following files from the [MBHM](https://huggingface.co/datasets/SIA-IDE/MBHM/tree/main) dataset. For users in mainland China, you can use the [mirror link](https://hf-mirror.com/datasets/SIA-IDE/MBHM/tree/main) to speed up the download:
-
-- `data.hdf5`: Contains the vibration signal data.
-- `corpus.json`: Contains the health management corpus.
-- `metadata.sqlite`: Contains metadata information of the dataset.
-
-### 2. Download Weights
-
-You can download the pre-trained weights of [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/tree/main) from Hugging Face.
-
-### 3. Modify Environment Variables
-
-Copy the `.env.example` file to `.env` and modify the data paths inside.
-
-### 4. Pre-train and Fine-tune Model
-
-Pre-train according to `src/pre_training.py`.
-Fine-tune according to `src/fine_tuning.py`.
-
-## 📖 Citation
-Please cite the following paper if you use this study in your research:
-
-```
-@article{pengBearLLMPriorKnowledgeEnhanced2025,
-  title = {{{BearLLM}}: {{A Prior Knowledge-Enhanced Bearing Health Management Framework}} with {{Unified Vibration Signal Representation}}},
-  author = {Peng, Haotian and Liu, Jiawei and Du, Jinsong and Gao, Jie and Wang, Wei},
-  year = {2025},
-  month = apr,
-  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
-  volume = {39},
-  number = {19},
-  pages = {19866--19874},
-  issn = {2374-3468},
-  doi = {10.1609/aaai.v39i19.34188},
-  urldate = {2025-04-11},
-}
+---
+license: mit
+datasets:
+- SIA-IDE/MBHM
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+---
+<div align="center">
+<a href="https://github.com/SIA-IDE/BearLLM">
+<img src="https://raw.githubusercontent.com/SIA-IDE/BearLLM/refs/heads/main/docs/images/logo.svg" width="200" alt="logo"/>
+</a>
+<h1>BearLLM: A Prior Knowledge-Enhanced Bearing Health Management Framework with Unified Vibration Signal Representation</h1>
+
+<a href="https://www.python.org/"><img alt="Python" src="https://img.shields.io/badge/Python-3.12-blue"></a>
+<a href="https://pytorch.org/"><img alt="PyTorch" src="https://img.shields.io/badge/Pytorch-latest-orange"></a>
+<a href="https://arxiv.org/abs/2408.11281"><img alt="arXiv" src="https://img.shields.io/badge/Paper-arXiv-B31B1B"></a>
+<a href="https://huggingface.co/datasets/SIA-IDE/MBHM"><img alt="Dataset" src="https://img.shields.io/badge/Dataset-🤗-FFFDF5"></a>
+<a href="https://github.com/SIA-IDE/BearLLM"><img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/SIA-IDE/BearLLM"></a>
+</div>
+
+<h4 align="center">
+    <p>
+        <b>English</b> |
+        <a href="https://github.com/SIA-IDE/BearLLM/blob/main/docs/README_zh.md">简体中文</a>
+    </p>
+</h4>
+
+## 🔥 NEWS
+- **[2025-04-11]** 🎉 The [AAAI-25 Proceedings](https://aaai.org/proceeding/aaai-39-2025/) are now officially published! Our [conference paper](https://ojs.aaai.org/index.php/AAAI/article/view/34188) is included. We welcome you to read and cite it!
+- **[2025-03-06]** 🌟 The complete dataset and code are now officially open source!
+- **[2024-12-11]** ⏫ We are now working on making the code of BearLLM public. Stay tuned!
+- **[2024-12-10]** 🎉 The BearLLM paper is accepted by the Thirty-Ninth AAAI Conference on Artificial Intelligence ([AAAI-25](https://aaai.org/conference/aaai/aaai-25/)).
+- **[2024-08-21]** 📝 The preprint of the BearLLM paper is available on arXiv. Check the [paper page](https://arxiv.org/abs/2408.11281) for more details.
+
+## 📅 TODO
+- [ ] Improve related comments and documentation.
+- [x] Upload the complete BearLLM demo code.
+- [x] Upload the health management corpus of the MBHM dataset.
+- [x] Collect the codes for pre-training and fine-tuning BearLLM.
+- [x] Collect the codes of BearLLM's classification network and other comparison models.
+- [x] Upload the vibration signal portion of the MBHM dataset.
+
+## 📚 Introduction
+The [MBHM](https://huggingface.co/datasets/SIA-IDE/MBHM) dataset is the first multimodal dataset designed for the study of bearing health management. It is divided into two parts: vibration signals and health management corpus. The vibration signals and condition information are derived from 9 publicly available datasets, and are still under continuous updating and improvement. The thousands of working conditions pose more difficult challenges for the identification model and better represent real-world usage scenarios.
+
+[BearLLM](https://github.com/SIA-IDE/BearLLM) is a prior knowledge-enhanced bearing health management framework with a unified vibration signal representation. This framework transforms the signal to be tested into the frequency domain, enabling effective identification of spectral differences compared to the vibration signal under fault-free conditions. By aligning the vibration signal with the fault semantic embedding, we achieve a unified natural language response for various health management tasks through a fine-tuned language model with low computational overhead. Experiments demonstrate that this framework achieves leading performance under thousands of working conditions.
+
+## 💻 Requirements
+
+The code is implemented in Python 3.12. The required packages are listed in the `requirements.txt` file. You can install the required packages by running the following command:
+
+```bash
+conda create --name bearllm python=3.12
+conda activate bearllm
+pip install -r requirements.txt
+```
+
+
+## 🚀 Quick Start
+
+### 1. Download Demo Data / Use Your Own Data
+
+First, you need to download the `demo_data.json` from the [MBHM](https://huggingface.co/datasets/SIA-IDE/MBHM/tree/main) dataset.
+For users in mainland China, you can use the [mirror link](https://hf-mirror.com/datasets/SIA-IDE/MBHM/tree/main) to speed up the download:
+
+Or, you can also build your own test data in the same format:
+`instruction`: Text instruction for health management task.
+`vib_data`: Vibration signal data to be identified, with a required duration of 1 second.
+`ref_data`: Reference vibration signal data without faults, with a required duration of 1 second.
+
+```json
+{
+    "instruction": "xxx.",
+    "vib_data": [1.0, 0.0, 1.0, ...],
+    "ref_data": [1.0, 0.0, 1.0, ...],
+}
+```
+
+### 2. Download Weights
+
+You can download the pre-trained weights of [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/tree/main) from Hugging Face.
+
+Additionally, you need to download the weights of [BearLLM](https://huggingface.co/SIA-IDE/BearLLM/tree/main).
+
+### 3. Organize Files
+
+It is recommended to organize the weights and test data as follows:
+
+```
+BearLLM/
+├── qwen_weights/
+│   ├── model.safetensors
+│   ├── tokenizer.json
+│   ├── config.json
+│   └── other files...
+├── bearllm_weights/
+│   ├── vibration_adapter.pth
+│   ├── adapter_config.json
+│   └── adapter_model.safetensors
+└── mbhm_dataset/
+    └── demo_data.json 
+```
+
+### 4. Run Code
+First, copy the `.env.example` file to `.env` and modify the data paths inside.
+Then, you can run the code using the following command:
+
+```bash
+python run_demo.py
+```
+
+## ⚙️ Development
+
+### 1. Download Dataset
+
+First, you need to download the following files from the [MBHM](https://huggingface.co/datasets/SIA-IDE/MBHM/tree/main) dataset. For users in mainland China, you can use the [mirror link](https://hf-mirror.com/datasets/SIA-IDE/MBHM/tree/main) to speed up the download:
+
+- `data.hdf5`: Contains the vibration signal data.
+- `corpus.json`: Contains the health management corpus.
+- `metadata.sqlite`: Contains metadata information of the dataset.
+
+### 2. Download Weights
+
+You can download the pre-trained weights of [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/tree/main) from Hugging Face.
+
+### 3. Modify Environment Variables
+
+Copy the `.env.example` file to `.env` and modify the data paths inside.
+
+### 4. Pre-train and Fine-tune Model
+
+Pre-train according to `src/pre_training.py`.
+Fine-tune according to `src/fine_tuning.py`.
+
+## 📖 Citation
+Please cite the following paper if you use this study in your research:
+
+```
+@article{pengBearLLMPriorKnowledgeEnhanced2025,
+  title = {{{BearLLM}}: {{A Prior Knowledge-Enhanced Bearing Health Management Framework}} with {{Unified Vibration Signal Representation}}},
+  author = {Peng, Haotian and Liu, Jiawei and Du, Jinsong and Gao, Jie and Wang, Wei},
+  year = {2025},
+  month = apr,
+  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume = {39},
+  number = {19},
+  pages = {19866--19874},
+  issn = {2374-3468},
+  doi = {10.1609/aaai.v39i19.34188},
+  urldate = {2025-04-11},
+}
 ```