Update README.md

README.md

@@ -19,7 +19,7 @@ March 2023
 
 ## Documentation
 
-* Blog post
+* [Blog post](https://www.mosaicml.com/blog/mosaicbert)
 * [Github (mosaicml/examples/bert repo)](https://github.com/mosaicml/examples/tree/main/examples/bert)
 
 ## How to use
@@ -150,13 +150,13 @@ Full configuration details for pretraining MosaicBERT-Base can be found in the c
 
 ## Evaluation results
 
-When fine-tuned on downstream tasks, this model achieves the following results:
-
-GLUE test results:
+When fine-tuned on downstream tasks (following the [finetuning details here](https://github.com/mosaicml/examples/blob/main/examples/bert/yamls/finetuning/glue/mosaic-bert-base-uncased.yaml)), the MosaicBERT model achieves the following GLUE results:
 
 | Task | MNLI-(m/mm) | QQP  | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE  | Average |
 |:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
-|      |    |  |  |   |  |   |  |  |     |
+|      | 0.8495 | 0.9029 | 0.9074|  0.9246 | 0.5511 | 0.8927  | 0.9003 | 0.8136 |  0.8428   |
+
+Note that this is averaged over n=5 pretraining seeds.
 
 ## Intended uses & limitations