fix claim

model2vec/train/README.md

@@ -122,7 +122,7 @@ We use 14 classification datasets, using 1000 examples from the train set, and t
 
 As you can see, full fine-tuning brings modest performance improvements in some cases, but very large ones in other cases, leading to a pretty large increase in average score. Our advice is to test both if you can use `potion-base-32m`, and to use full fine-tuning if you are starting from another base model.
 
-The speed difference between model2vec and setfit is immense, with the full finetune being 50x faster than a setfit based on `all-minilm-l6-v2`.
+The speed difference between model2vec and setfit is immense, with the full finetune being 35x faster than a setfit based on `all-minilm-l6-v2` on CPU.
 
 |                |   logreg   |  full finetune | setfit
 |:---------------------------|-----------:|---------------:|-------:|