sagawa commited on
Commit
129b06b
1 Parent(s): 6aff882

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +4 -4
README.md CHANGED
@@ -19,9 +19,9 @@ model-index:
19
  value: 0.9497212171554565
20
  ---
21
 
22
- # ZINC-t5
23
 
24
- 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.
25
  It achieves the following results on the evaluation set:
26
  - Loss: 0.1202
27
  - Accuracy: 0.9497
@@ -29,12 +29,12 @@ It achieves the following results on the evaluation set:
29
 
30
  ## Model description
31
 
32
- We trained t5 on SMILES from ZINC using the task of masked-language modeling (MLM). Its tokenizer is also trained on ZINC.
33
 
34
 
35
  ## Intended uses & limitations
36
 
37
- This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning.
38
  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).
39
  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).
40
 
 
19
  value: 0.9497212171554565
20
  ---
21
 
22
+ # CompoundT5
23
 
24
+ 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.
25
  It achieves the following results on the evaluation set:
26
  - Loss: 0.1202
27
  - Accuracy: 0.9497
 
29
 
30
  ## Model description
31
 
32
+ We trained t5 on SMILES from ZINC using masked-language modeling (MLM). Its tokenizer is also trained on ZINC.
33
 
34
 
35
  ## Intended uses & limitations
36
 
37
+ This model can be used to predict molecules' properties, reactions, or interactions with proteins by changing the way of finetuning.
38
  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).
39
  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).
40