Upload README.md

README.md

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-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+# CleanTS Model Card
+
+## Introduction
+
+CleanTS is a competitive pre-trained baseline for time-series forecasting. It is designed to demonstrate the potential of **data-centric optimization** using a **standard encoder-only Transformer** architecture without any structural modifications.
+
+## Key Technical Principles
+
+### 1. Pure Architecture
+
+ Zero modifications to the attention mechanism or model structure. CleanTS proves that high-quality data governance can significantly elevate the performance of vanilla architectures.
+
+### 2. Systematic Data Governance
+
+CleanTS adheres to a strict data-centric pipeline:
+
+- **Zero Synthetic Data:** All training is performed exclusively on **authentic, real-world data**.
+- **Publicly Sourced:** The training corpus consists entirely of **publicly available datasets**, ensuring transparency and accessibility.
+- **Advanced Cleaning:** We achieve promising results solely through systematic data cleaning and preprocessing strategies.
+
+### 3. Contamination Prevention
+
+We strictly guarantee that **no data from the GiftEval test set，Fev-bench，Fev-leaderboard and LSTF** was involved in the training phase.
+
+> [!NOTE] A **detailed technical report**, including our specific data cleaning methodologies, training configurations, and comprehensive ablation studies, will be released concurrently with the upcoming publication of the full model.