Update README.md

README.md

@@ -145,30 +145,31 @@ For a step-by-step guide on running inferences with Toto, please refer to our [G
 
 - For optimal inference speed install [xformers](https://github.com/facebookresearch/xformers?tab=readme-ov-file#installing-xformers) and [flash-attention](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features)
 
-## Training Details - TODO keep or remove?
+## Training Details
 
 ### Pre-Training Data
-| Dataset                                                                          |
-|----------------------------------------------------------------------------------|
-| [GiftEval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain) |
-| [Chronos](https://huggingface.co/datasets/autogluon/chronos_datasets) (Note: we use a subset of the Chronos dataset to avoid contamination with the GiftEval benchmark.)           |
-| Synthetic          |
-| Observability (**Note: No customer data was used in the training of this model**)      |
 
+Toto was trained on a massive and diverse mixture of time series datasets:
 
-For more details about the pretraining data and preprocessing steps, please refer to the [paper](#TODO-Link-to-Paper) or the [GitHub repository](https://github.com/DataDog/toto.git).
+#### Observability Data
 
+The largest portion of pretraining data comes from a dataset of approximately 1 trillion time series points collected from Datadog metrics. These metrics are generated from Datadog's monitoring of internal systems, and **do not** include any customer data. They cover a diverse array of software stacks and types of services, and span wide variety of domains within observability, including application performance, infrastructure, networking, security, databases, and more.
 
-## Citation - TODO
+#### Public Datasets
 
+To improve the performance of Toto on general-purpose time series forecasting across many domains, we include publcly availa
+- [GiftEval Pretrain](https://huggingface.co/datasets/Salesforce/GiftEvalPretrain)
+- [Chronos pretraining data](https://huggingface.co/datasets/autogluon/chronos_datasets) (Note: only a subset of this dataset was used to avoid leakage with the GiftEval benchmark)
+
+#### Synthetic Data
+To improve robustness, approximately 1/3 of the pretraining data mix consists of synthetically-generated time series.
+
+For more details about the pretraining data and preprocessing steps, please refer to the [paper](#TODO-Link-to-Paper).
+
+
+## Citation
 If you use Toto in your research or applications, please cite us using the following:
 
-```
-@article{Toto-Open-Base-1.0,
-  title={TOTO: Time Series Optimized Transformer for Observability},
-  author={Your Author Names Here},
-  journal={arXiv preprint arXiv:XXXX.XXXXX},
-  year={2025},
-  url={https://arxiv.org/abs/XXXX.XXXXX}
-}
+```bibtex
+TODO
 ```