Update README.md

README.md

@@ -132,9 +132,9 @@ The below model scripts can be used for any of the above TTM models. Please upda
 TTM outperforms popular benchmarks such as TimesFM, Moirai, Chronos, Lag-Llama, Moment, GPT4TS, TimeLLM, LLMTime in zero/fewshot forecasting while reducing computational requirements significantly. 
 Moreover, TTMs are lightweight and can be executed even on CPU-only machines, enhancing usability and fostering wider
 adoption in resource-constrained environments. For more details, refer to our [paper](https://arxiv.org/pdf/2401.03955.pdf).
-- TTM-B referred in the paper maps to the `512-96-r2` model.
-- TTM-E referred in the paper maps to the `1024-96-r2` model.
-- TTM-A referred in the paper maps to the `1536-96-r2` model
+- TTM-B referred in the paper maps to the 512 context models.
+- TTM-E referred in the paper maps to the 1024 context models.
+- TTM-A referred in the paper maps to the 1536 context models.
 
 Please note that the Granite TTM models are pre-trained exclusively on datasets
 with clear commercial-use licenses that are approved by our legal team. As a result, the pre-training dataset used in this release differs slightly from the one used in the research