whenxuan: update the readme

README.md

@@ -1,104 +1,104 @@
----
-license: apache-2.0
-metrics:
-- mse
-- mae
-tags:
-- time series
-- forecasting
-- foundation models
-- pretrained models
-- generative models
-- time series foundation models
-library_name: transformers
----
-
-# SymTime NeurIPS 2025
-
-This code is the official PyTorch implementation of our NeurIPS'25 paper: **Synthetic Series-Symbol Data Generation for Time Series Foundation Models**.
-
-<div align="center">
-
-[![NeurIPS](https://img.shields.io/badge/NeurIPS'25-SymTime-orange)](https://neurips.cc/virtual/2025/poster/115260) [![PyPI version](https://badge.fury.io/py/s2generator.svg)](https://pypi.org/project/s2generator/) [![Python](https://img.shields.io/badge/python-3.10+-blue?logo=python)](https://www.python.org/) [![PyTorch](https://img.shields.io/badge/PyTorch-2.0.1-blue)](https://pytorch.org/)
-
-[Paper](https://arxiv.org/abs/2510.08445) | [Poster](https://github.com/wwhenxuan/wwhenxuan.github.io/blob/main/assets/img/poster_neurips_2025_115260_synthetic_series-symbol_data_generation.jpg) | [Blog](https://mp.weixin.qq.com/s/D6O5SBl2RYHdkiinV6UM8w) | [Video](https://www.bilibili.com/video/BV1RT4QzXECt/?spm_id_from=333.337.search-card.all.click) | [PPT](https://github.com/wwhenxuan/wwhenxuan.github.io/blob/main/assets/files/NeurIPS_2025_SymTime_video_en.pptx) | [Citation](#Citation) | [HF 🤗](https://huggingface.co/FlowVortex/SymTime)
-
-</div>
-
-This repository contains the official Hugging Face / PyTorch implementation of **SymTime** from our NeurIPS 2025 paper, *Synthetic Series-Symbol Data Generation for Time Series Foundation Models*.
-
-## Overview
-
-SymTime is a lightweight time series foundation model designed to learn strong temporal representations from patch-based inputs. It is built for practical downstream use and supports easy loading through the Hugging Face `AutoModel` interface.
-
-<div style="text-align: center;">
-    <img src="https://raw.githubusercontent.com/wwhenxuan/SymTime/main/configs/images/S2Generator_SymTime.png" alt="SymTime" style="zoom:80%;" />
-</div>
-
-The model takes a univariate time series, splits it into patches, and encodes the patch sequence with a transformer backbone. The repository includes the configuration, model definition, and a runnable example for inference.
-
-## Quick start
-
-### Install dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-### Load the model
-
-```python
-from transformers import AutoModel
-
-model = AutoModel.from_pretrained("FlowVortex/SymTime", trust_remote_code=True)
-```
-
-### Run inference
-
-```python
-import torch
-
-x = torch.randn(16, 256)
-out = model(x)
-out_no_cls = model(x, return_cls_token=False)
-```
-
-## Model summary
-
-- Input: `Tensor` with shape `[batch_size, seq_length]`
-- Output: patch embeddings, optionally with a CLS token output
-- Backend: patch-based transformer encoder
-
-## Citation <a id="Citation"></a>
-
-If you find this code useful, please cite our paper.
-
-```
-@misc{wang2025syntheticseriessymboldatageneration,
-      title={Synthetic Series-Symbol Data Generation for Time Series Foundation Models}, 
-      author={Wenxuan Wang and Kai Wu and Yujian Betterest Li and Dan Wang and Xiaoyu Zhang},
-      year={2025},
-      eprint={2510.08445},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2510.08445}, 
-}
-```
-
-## Contact
-
-If you have any questions or are interested in our view on the complex dynamics of time series, feel free to contact:
-
-- [Whenxuan Wang](https://wwhenxuan.github.io/) (whenxuanwang@stu.xidian.edu.cn)
-- [Kai Wu](https://sparsel.github.io/index.html) (kwu@xidian.edu.cn)
-- [Dan Wang](https://web.xidian.edu.cn/danwang/) (danwang@xidian.edu.cn)
-
-## Acknowledgement
-
-We appreciate the following GitHub repos a lot for their valuable code and efforts.
-
-- Time-Series-Library (https://github.com/thuml/Time-Series-Library)
-- PySDKit (https://github.com/wwhenxuan/PySDKit)
-- ALBEF (https://github.com/salesforce/ALBEF)
-- PatchTST (https://github.com/yuqinie98/PatchTST)
-- Short-term Forecasting (https://github.com/ServiceNow/N-BEATS)
+---
+license: apache-2.0
+metrics:
+- mse
+- mae
+tags:
+- time series
+- forecasting
+- foundation models
+- pretrained models
+- generative models
+- time series foundation models
+library_name: transformers
+language:
+- en
+---
+
+# SymTime NeurIPS 2025
+
+This code is the official PyTorch implementation of our NeurIPS'25 paper: **Synthetic Series-Symbol Data Generation for Time Series Foundation Models**.
+
+<div align="center">
+  
+[Paper](https://arxiv.org/abs/2510.08445) | [Poster](https://github.com/wwhenxuan/wwhenxuan.github.io/blob/main/assets/img/poster_neurips_2025_115260_synthetic_series-symbol_data_generation.jpg) | [Blog](https://mp.weixin.qq.com/s/D6O5SBl2RYHdkiinV6UM8w) | [Video](https://www.bilibili.com/video/BV1RT4QzXECt/?spm_id_from=333.337.search-card.all.click) | [PPT](https://github.com/wwhenxuan/wwhenxuan.github.io/blob/main/assets/files/NeurIPS_2025_SymTime_video_en.pptx) | [Citation](#Citation) | [HF 🤗](https://huggingface.co/FlowVortex/SymTime)
+
+</div>
+
+This repository contains the official Hugging Face / PyTorch implementation of **SymTime** from our NeurIPS 2025 paper, *Synthetic Series-Symbol Data Generation for Time Series Foundation Models*.
+
+## Overview
+
+SymTime is a lightweight time series foundation model designed to learn strong temporal representations from patch-based inputs. It is built for practical downstream use and supports easy loading through the Hugging Face `AutoModel` interface.
+
+<div style="text-align: center;">
+    <img src="https://raw.githubusercontent.com/wwhenxuan/SymTime/main/configs/images/S2Generator_SymTime.png" alt="SymTime" style="zoom:80%;" />
+</div>
+
+The model takes a univariate time series, splits it into patches, and encodes the patch sequence with a transformer backbone. The repository includes the configuration, model definition, and a runnable example for inference.
+
+## Quick start
+
+### Install dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### Load the model
+
+```python
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained("FlowVortex/SymTime", trust_remote_code=True)
+```
+
+### Run inference
+
+```python
+import torch
+
+x = torch.randn(16, 256)
+out = model(x)
+out_no_cls = model(x, return_cls_token=False)
+```
+
+## Model summary
+
+- Input: `Tensor` with shape `[batch_size, seq_length]`
+- Output: patch embeddings, optionally with a CLS token output
+- Backend: patch-based transformer encoder
+
+## Citation <a id="Citation"></a>
+
+If you find this code useful, please cite our paper.
+
+```
+@misc{wang2025syntheticseriessymboldatageneration,
+      title={Synthetic Series-Symbol Data Generation for Time Series Foundation Models}, 
+      author={Wenxuan Wang and Kai Wu and Yujian Betterest Li and Dan Wang and Xiaoyu Zhang},
+      year={2025},
+      eprint={2510.08445},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2510.08445}, 
+}
+```
+
+## Contact
+
+If you have any questions or are interested in our view on the complex dynamics of time series, feel free to contact:
+
+- [Whenxuan Wang](https://wwhenxuan.github.io/) (whenxuanwang@stu.xidian.edu.cn)
+- [Kai Wu](https://sparsel.github.io/index.html) (kwu@xidian.edu.cn)
+- [Dan Wang](https://web.xidian.edu.cn/danwang/) (danwang@xidian.edu.cn)
+
+## Acknowledgement
+
+We appreciate the following GitHub repos a lot for their valuable code and efforts.
+
+- Time-Series-Library (https://github.com/thuml/Time-Series-Library)
+- PySDKit (https://github.com/wwhenxuan/PySDKit)
+- ALBEF (https://github.com/salesforce/ALBEF)
+- PatchTST (https://github.com/yuqinie98/PatchTST)
+- Short-term Forecasting (https://github.com/ServiceNow/N-BEATS)