clarify

README.md

@@ -127,7 +127,7 @@ on the evaluation set of 10k linear equations from the DeepMind *algebra__linear
 
 Additionally, we construct a partial correctness dataset available at the following model card: [MarioBarbeque/CyberSolve-LinAlg-1.2-correctness-benchmark](https://huggingface.co/datasets/MarioBarbeque/CyberSolve-LinAlg-1.2-correctness-benchmark).
 This dataset was created with the goal of analyzing both the token-to-token and decoded-sequence-to-decoded-sequence partial correctness of CyberSolve's predicitions in detail beyond just its ability to get answers flat out right or wrong. Similar partial correctness benchmark datasets were created for the
-intial [FLAN-T5 model](https://huggingface.co/datasets/MarioBarbeque/FLAN-T5-DeepMind-LinAlg-1D-benchmark), the preliminary, [zeroth-generation downsampled training](https://huggingface.co/datasets/MarioBarbeque/CyberSolve-DeepMind-LinAlg-1D-downsample-benchmark-v2) of CyberSolve, and 
+intial [FLAN-T5 model](https://huggingface.co/datasets/MarioBarbeque/FLAN-T5-DeepMind-LinAlg-1D-benchmark), the [zeroth-generation downsampled training](https://huggingface.co/datasets/MarioBarbeque/CyberSolve-DeepMind-LinAlg-1D-downsample-benchmark-v2) of CyberSolve, and 
 the [1.1 version](https://huggingface.co/datasets/MarioBarbeque/CyberSolve-LinAlg-1.1-correctness-benchmark) of the model. *We have yet to complete partial correctness analysis of the various model versions and their predicitions, but we look forward to better understanding the mathematical
 reasoning capabilities of models and publishing our results when complete!*