Update README.md

README.md

@@ -205,9 +205,20 @@ below is the weight each config gets, and those weights are the recipe: they dec
 read, so some configs were read several times and some only in part. Tokenize the `text` column with the Carbon-X
 tokenizer, train with these weights, and you get the same data diet as Carbon-X.
 
+### Columns
+
+Every mid-training config has the same columns:
+
+| column | |
+|---|---|
+| `text` | the document, as it was tokenized |
+| `id` | the source's document id, where it has one |
+| `source` | the config name without `mid_`, or for biology text the original source (`pubmed`, `biorxiv`, …) |
+| `metadata` | the source's other fields, as a JSON string |
+
 ### The mix
 
-| | unique tokens | share of training |
+| | unique tokens | share of training in Carbon-X |
 |---|---:|---:|
 | DNA | 181.96B | 55% |
 | Biology text | 15.75B | 15% |
@@ -302,17 +313,6 @@ follow instructions.
 
 Biology configs also have a `validation` split: the documents held out of training to track loss.
 
-### Columns
-
-Every mid-training config has the same columns:
-
-| column | |
-|---|---|
-| `text` | the document, as it was tokenized |
-| `id` | the source's document id, where it has one |
-| `source` | the config name without `mid_`, or for biology text the original source (`pubmed`, `biorxiv`, …) |
-| `metadata` | the source's other fields, as a JSON string |
-
 ## SFT
 
 Supervised fine-tuning conversations for a DNA language model: 204,023 rows (203,923 `train`, 100 `test`).