Update README.md

README.md

@@ -1,7 +1,71 @@
 ---
-license: apache-2.0
+license: mit
+datasets:
+- sagawa/ZINC-canonicalized
+metrics:
+- accuracy
+model-index:
+- name: ZINC-deberta
+  results:
+  - task:
+      name: Masked Language Modeling
+      type: fill-mask
+    dataset:
+      name: sagawa/ZINC-canonicalized
+      type: sagawa/ZINC-canonicalized
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9497212171554565
 ---
+
 # ZINC-t5 
 
-We trained T5 on SMILES from ZINC using the task of masked-language modeling (MLM), and its tokenizer is also trained on ZINC data. This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning.
-As an example, We finetuned this model to predict products. Model is [here](https://huggingface.co/sagawa/ZINC-t5-productpredicition), and you can use the demo [here](https://huggingface.co/spaces/sagawa/predictproduct-t5).
+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.
+It achieves the following results on the evaluation set:
+- Loss: 0.1202
+- Accuracy: 0.9497
+
+
+## Model description
+
+We trained t5 on SMILES from ZINC using the task of 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.
+As an example, We finetuned this model to predict products. 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).
+
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-03
+- train_batch_size: 30
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 30.0
+
+### Training results
+
+| Training Loss | Step   | Accuracy | Validation Loss |
+|:-------------:|:------:|:--------:|:---------------:|
+| 0.2226        | 25000  | 0.9843   | 0.2226          |
+| 0.1783        | 50000  | 0.9314   | 0.1783          |
+| 0.1619        | 75000  | 0.9371   | 0.1619          |
+| 0.1520        | 100000 | 0.9401   | 0.1520          |
+| 0.1449        | 125000 | 0.9422   | 0.1449          |
+| 0.1404        | 150000 | 0.9436   | 0.1404          |
+| 0.1368        | 175000 | 0.9447   | 0.1368          |
+| 0.1322        | 200000 | 0.9459   | 0.1322          |
+| 0.1299        | 225000 | 0.9466   | 0.1299          |
+| 0.1268        | 250000 | 0.9473   | 0.1268          |
+| 0.1244        | 275000 | 0.9483   | 0.1244          |
+| 0.1216        | 300000 | 0.9491   | 0.1216          |
+| 0.1204        | 325000 | 0.9497   | 0.1204          |