Update README.md

README.md

@@ -1,6 +1,64 @@
 ---
 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: 0.9475839734077454
 ---
-# ZINC-t5-v2 
 
-We trained T5 on SMILES from ZINC using the task of masked-language modeling (MLM). Compared to ZINC-t5, ZINC-t5-v2 uses a character-level tokenizer. This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning. 
+# ZINC-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: 0.1228
+- Accuracy: 0.9476
+
+
+## Model description
+
+We trained t5 on SMILES from ZINC using the task of masked-language modeling (MLM). Compared to PubChem-t5, PubChemC-t5-v2 uses a character-level tokenizer, and it was 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. 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).
+
+## Training and evaluation data
+
+We downloaded [ZINC data](https://drive.google.com/drive/folders/1lSPCqh31zxTVEhuiPde7W3rZG8kPgp-z) and canonicalized them using RDKit. Then, we dropped duplicates. The total number of data is 22992522, and they were randomly split into train:validation=10:1.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-03
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10.0
+
+### Training results
+
+| Training Loss | Step   | Accuracy | Validation Loss |
+|:-------------:|:------:|:--------:|:---------------:|
+| 0.2090        | 100000 | 0.9264   | 0.1860          |
+| 0.1628        | 200000 | 0.9349   | 0.1613          |
+| 0.1632        | 300000 | 0.9395   | 0.1467          |
+| 0.1451        | 400000 | 0.9435   | 0.1345          |
+| 0.1311        | 500000 | 0.9465   | 0.1261          |