
# 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: 0.2121
- Accuracy: 0.9259


## Model description

We trained t5 on SMILES from PubChem using the task of masked-language modeling (MLM). Its tokenizer is 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
- 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 |
|:-------------:|:------:|:--------:|:---------------:|
| 0.3866        | 25000  | 0.8830   | 0.3631          |
| 0.3352        | 50000  | 0.8996   | 0.3049          |
| 0.2834        | 75000  | 0.9057   | 0.2825          |
| 0.2685        | 100000 | 0.9099   | 0.2675          |
| 0.2591        | 125000 | 0.9124   | 0.2587          |
| 0.2620        | 150000 | 0.9144   | 0.2512          |
| 0.2806        | 175000 | 0.9161   | 0.2454          |
| 0.2468        | 200000 | 0.9179   | 0.2396          |
| 0.2669        | 225000 | 0.9194   | 0.2343          |
| 0.2611        | 250000 | 0.9210   | 0.2283          |
| 0.2346        | 275000 | 0.9226   | 0.2230          |
| 0.1972        | 300000 | 0.9238   | 0.2191          |
| 0.2344        | 325000 | 0.9250   | 0.2152          |
| 0.2164        | 350000 | 0.9259   | 0.2121          |
{"license": "mit", "datasets": ["sagawa/pubchem-10m-canonicalized"], "metrics": ["accuracy"], "model-index": [{"name": "PubChem-10m-t5", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "sagawa/pubchem-10m-canonicalized", "type": "sagawa/pubchem-10m-canonicalized"}, "metrics": [{"type": "accuracy", "value": 0.9259435534477234, "name": "Accuracy"}]}]}]}
transformers pytorch jax t5 text2text-generation dataset:sagawa/pubchem-10m-canonicalized license:mit model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #dataset-sagawa/pubchem-10m-canonicalized #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

PubChem-10m-t5
==============


This model is a fine-tuned version of google/t5-v1\_1-base on the sagawa/pubchem-10m-canonicalized dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2121
* Accuracy: 0.9259


Model description
-----------------


We trained t5 on SMILES from PubChem using the task of masked-language modeling (MLM). Its tokenizer is 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 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
* 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 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
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #dataset-sagawa/pubchem-10m-canonicalized #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### 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
passage: TAGS
#transformers #pytorch #jax #t5 #text2text-generation #dataset-sagawa/pubchem-10m-canonicalized #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### 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