Update README.md

README.md

@@ -24,16 +24,46 @@ pipeline_tag: time-series-forecasting
 ---
 # Toto-Open-Base-1.0
 
-Toto (Time Series Optimized Transformer for [Observability](https://www.datadoghq.com/knowledge-center/observability/) is a time-series foundation model designed for multi-variate time series forecasting, emphasizing observability metrics. Toto efficiently handles high-dimensional, sparse, and non-stationary data commonly encountered in observability scenarios.
+Toto (Time Series Optimized Transformer for [Observability](https://www.datadoghq.com/knowledge-center/observability/) is a state-of-the-art time-series foundation model designed for multi-variate time series forecasting, emphasizing observability metrics. Toto efficiently handles high-dimensional, sparse, and non-stationary data commonly encountered in observability scenarios.
+
+<div style="width: 80%; margin: auto; padding: 1rem;">
+  <img src="figures/rankings.png" alt="model ranking" style="width: 100%; height: auto;" />
+  <em style="display: block; margin-top: 0.5rem; text-align: center;">
+    The average rank of Toto compared to the runner-up models on both the <a href="https://huggingface.co/spaces/Salesforce/GIFT-Eval">GIFT-Eval</a> and <a href="https://huggingface.co/datasets/Datadog/BOOM">BOOM</a> benchmarks (as of May 19, 2025).
+  </em>
+</div>
+
+---
+
+## ✨ Key Features
+
+- **Zero-Shot Forecasting**: Perform forecasting without fine-tuning on your specific time series.
+- **High-Dimension Multi-Variate Support**: Efficiently process multiple variables using Proportional Factorized Space-Time Attention.
+- **Decoder-Only Transformer Architecture**: Support for variable prediction horizons and context lengths.
+- **Probabilistic Predictions**: Generate both point forecasts and uncertainty estimates using a Student-T mixture model.
+- **Extensive Pretraining on Large-Scale Data**: Trained on over 2 trillion time series data points, the largest pretraining dataset for any open-weights time series foundation model to date.
+- **Tailored for Observability Metrics with State-of-the-Art Performance** on [GIFT-Eval](https://huggingface.co/spaces/Salesforce/GIFT-Eval) and [BOOM](https://huggingface.co/datasets/Datadog/BOOM)
 
 
 <div style="width: 100%; margin: auto; padding: 1rem;">
   <img src="figures/architecture.png" alt="model architecture" style="width: 100%; height: auto;" />
   <em style="display: block; margin-top: 0.5rem; text-align: center;">
-    Overview of Toto-Open-Base-1.0 architecture.
+    Oerview of Toto-Open-Base-1.0 architecture.
   </em>
 </div>
 
+---
+
+## 📚 Training Data Summary
+
+- **Observability Metrics:** ~1 trillion points from Datadog internal systems (no customer data)
+- **Public Datasets:**
+  - [GIFT-Eval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain)
+  - [Chronos datasets](https://huggingface.co/datasets/autogluon/chronos_datasets)
+- **Synthetic Data:** ~1/3 of training data
+
+
+
 ---
 
 ## ⚡ Quick Start: Model Inference
@@ -113,29 +143,7 @@ For detailed inference instructions, refer to the [inference tutorial notebook](
 | [Toto-Open-Base-1.0](https://huggingface.co/Datadog/Toto-Open-Base-1.0/blob/main/model.safetensors) | 151M | [Config](https://huggingface.co/Datadog/Toto-Open-Base-1.0/blob/main/config.json) | 605 MB | Initial release with SOTA performance |
 
 
-## ✨ Key Features
-
-- **Zero-Shot Forecasting**
-- **Multi-Variate Support**
-- **Decoder-Only Transformer Architecture**
-- **Probabilistic Predictions (Student-T mixture model)**
-- **Causal Patch-Wise Instance Normalization**
-- **Extensive Pretraining on Large-Scale Data**
-- **High-Dimensional Time Series Support**
-- **Tailored for Observability Metrics**
-- **State-of-the-Art Performance** on [GiftEval](https://huggingface.co/spaces/Salesforce/GIFT-Eval) and [BOOM](https://huggingface.co/datasets/Datadog/BOOM)
-
----
-
-## 📚 Training Data Summary
-
-- **Observability Metrics:** ~1 trillion points from Datadog internal systems (no customer data)
-- **Public Datasets:**
-  - [GiftEval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain)
-  - [Chronos datasets](https://huggingface.co/datasets/autogluon/chronos_datasets)
-- **Synthetic Data:** ~1/3 of training data
 
----
 
 ## 🔗 Additional Resources