Update README.md

README.md

@@ -53,29 +53,22 @@ TTMs that can cater to many common forecasting settings in practice.
 Each pre-trained model will be released in a different branch name in this model card. Kindly access the required model using our 
 getting started [notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb) mentioning the branch name.
 
- 
-## Model Releases (along with the branch name where the models are stored):
+## Model Releases:
 
+Given the variety of models included, please use the [[get_model]](https://github.com/ibm-granite/granite-tsfm/blob/main/tsfm_public/toolkit/get_model.py) utility to automatically select the required model based on your input context length and forecast length requirement.
 
+There are several models available in different branches of this model card. The naming scheme follows the following format:
+`<context length>-<prediction length>-<frequency prefix tuning indicator>-<pretraining metric>-<release number>`
 
-- **512-96-r2**: Given the last 512 time-points (i.e. context length), this model can forecast up to the next 96 time-points (i.e. forecast length)
-    in future. (branch name: main)
-  
-- **1024-96-r2**: Given the last 1024 time-points (i.e. context length), this model can forecast up to the next 96 time-points (i.e. forecast length)
-    in future.  (branch name: 1024-96-r2) [[Benchmarks]]
-
-- **1536-96-r2**: Given the last 1536 time-points (i.e. context length), this model can forecast up to the next 96 time-points (i.e. forecast length)
-    in future. (branch name: 1536-96-r2)
+ - context length: The historical data used as input to the TTM model.
 
-- Likewise, we have models released for forecast lengths up to 720 timepoints. The branch names for these are as follows: `512-192-r2`, `1024-192-r2`, `1536-192-r2`, `512-336-r2`,
-  `512-336-r2`, `1024-336-r2`, `1536-336-r2`, `512-720-r2`, `1024-720-r2`, `1536-720-r2`
+ - prediction length: The number of time points predicted by model (i.e., the forecast length)
 
-- Please use the [[get_model]](https://github.com/ibm-granite/granite-tsfm/blob/main/tsfm_public/toolkit/get_model.py) utility to automatically select the required model based on your input context length and forecast length requirement.
+ - frequency tuning indicator ("ft" or missing): "ft" is used to indicate use of frequency prefix tuning. When enabled an extra embedding vector indicating the frequency of the data is added to the input of the model. If missing, only the context window is used by the model.
 
-- We currently allow 3 context lengths (512, 1024 and 1536) and 4 forecast lengths (96, 192, 336, 720). Users need to provide one of the 3 allowed context lengths as input.
-but can provide any forecast lengths up to 720 in get_model() to get the required model.
+ - pretraining metric ("mae" or missing): MAE indicates pertaining with mean absolute error loss, while missing indicates using mean squared error.
 
-  
+ - release number ("r2" or "r2.1"): Indicates the model release; the release indicates which data was used to train the model. See "training data" below for more details on the data included in the particular training datasets.