Update README.md

README.md

@@ -18,7 +18,7 @@ As mentioned, a few updates are planned:
 * Fine-tuning the resulting model for instruct, code and storywriting. These will then be combined using MergeKit to create a MoE model.
 * Release a GGUF version and an extended context version of the base model
 
-## Model Performance Tracking
+# Model Performance Tracking
 
 This table tracks the performance of our model on various tasks over time.
 
@@ -26,25 +26,16 @@ This table tracks the performance of our model on various tasks over time.
 |-------------------|----------|---------------|---------------|---------------|---------------| ---- |
 | 2024-07-27        | acc      | 27.40% ± 0.92% | 25.52% ± 0.44% | 52.71% ± 3.01% | 39.52% ± 1.11% | 36.29% |
 
-### Legend
+## Legend
 - Date: The date of each evaluation run
-- Metric: The evaluation metric used (acc = accuracy, acc_norm = normalized accuracy)
+- Metric: The evaluation metric used (acc = accuracy)
 - Task columns: Results for each task in the format "Percentage ± Standard Error"
 
-### Notes
+## Notes
 - All accuracy values are presented as percentages
 - Empty cells indicate that the task was not evaluated on that date or for that metric
 - Standard errors are also converted to percentages for consistency
 
-### Legend
-- Task: The name of the evaluation task
-- Metric: The evaluation metric used (acc = accuracy, acc_norm = normalized accuracy)
-- Date columns: The date of each evaluation run, with results in the format "Value ± Standard Error"
-
-### Notes
-- All accuracy values are on a scale from 0 to 1
-- Empty cells indicate that the task was not evaluated on that date
-
 # Tokenizer
 Our tokenizer was trained from scratch on 500,000 samples from the Openwebtext dataset. Like Mistral, we use the LlamaTokenizerFast as our tokenizer class; in legacy mode.