Update README.md

README.md

@@ -1,5 +1,60 @@
 ---
 license: mit
+datasets:
+- sagawa/pubchem-10m-canonicalized
+metrics:
+- accuracy
+model-index:
+- name: PubChem-10m-t5
+  results:
+  - task:
+      name: Masked Language Modeling
+      type: fill-mask
+    dataset:
+      name: sagawa/pubchem-10m-canonicalized
+      type: sagawa/pubchem-10m-canonicalized
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9189779162406921
 ---
-# PubChem-10m-t5-v2
-We trained T5 on SMILES from PubChem using the task of masked-language modeling (MLM). Compared to PubChem-t5, PubChemC-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. 
+
+# PubChem-10m-t5
+
+This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/pubchem-10m-canonicalized dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2165
+- Accuracy: 0.9190
+
+
+## Model description
+
+We trained t5 on SMILES from PubChem using the task of masked-language modeling (MLM). Compared to PubChem-10m-t5, PubChem-10m-t5-v2 uses a character-level tokenizer, and it was also trained on PubChem.
+
+
+## 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.
+
+## Training and evaluation data
+
+We downloaded [PubChem data](https://drive.google.com/file/d/1ygYs8dy1-vxD1Vx6Ux7ftrXwZctFjpV3/view) and canonicalized them using RDKit. Then, we dropped duplicates. The total number of data is 9999960, 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 |
+|:-------------:|:------:|:--------:|:---------------:|
+| 0.2592        | 100000 | 0.8997   | 0.2784          |
+| 0.2790        | 200000 | 0.9095   | 0.2468          |
+| 0.2278        | 300000 | 0.9162   | 0.2256          |