Update README.md

README.md

@@ -19,9 +19,9 @@ model-index:
       value: 0.9497212171554565
 ---
 
-# ZINC-t5 
+# CompoundT5 
 
-This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset.
+This model is a re-pretrained version of [google/t5-v1_1-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.1202
 - Accuracy: 0.9497
@@ -29,12 +29,12 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-We trained t5 on SMILES from ZINC using the task of masked-language modeling (MLM). Its tokenizer is also trained on ZINC.
+We trained t5 on SMILES from ZINC using masked-language modeling (MLM). Its tokenizer is also trained on ZINC.
 
 
 ## Intended uses & limitations
 
-This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning.
+This model can be used to predict molecules' properties, reactions, or interactions with proteins by changing the way of finetuning.
 As an example, We finetuned this model to predict products. The model is [here](https://huggingface.co/sagawa/ZINC-t5-productpredicition), and you can use the demo [here](https://huggingface.co/spaces/sagawa/predictproduct-t5).
 Using its encoder, we trained a regression model to predict a reaction yield. You can use this demo [here](https://huggingface.co/spaces/sagawa/predictyield-t5).