diff --git a/TRAINING_LOG.md b/TRAINING_LOG.md
index f551b090..c59dccfd 100644
--- a/TRAINING_LOG.md
+++ b/TRAINING_LOG.md
@@ -244,7 +244,7 @@ We tried training a full model using the parameters above, but found that during
 We trained multiple [GPT-J models](https://huggingface.co/EleutherAI/gpt-j-6b) with varying success. We found that training the full model lead to diverged post epoch 1. ![](figs/overfit-gpt-j.png). We release the checkpoint after epoch 1.
 
 
-Using Atlas, we extracted the embeddings and calculated the per sequence level loss. We then uploaded [this to Atlas](https://atlas.nomic.ai/map/gpt4all-j-post-epoch-1-embeddings) and noticed that the higher loss items seem to cluster. On further inspection, the highest density clusters seemded to be of prompt/response pairs that asked for creative-like generations such as `Generate a story about ...` ![](figs/clustering_overfit.png)
+Using Atlas, we extracted the embeddings of each point in the dataset and calculated the loss per sequence. We then uploaded [this to Atlas](https://atlas.nomic.ai/map/gpt4all-j-post-epoch-1-embeddings) and noticed that the higher loss items seem to cluster. On further inspection, the highest density clusters seemded to be of prompt/response pairs that asked for creative-like generations such as `Generate a story about ...` ![](figs/clustering_overfit.png)