pytorch tensorboard roberta text-classification transformers generated_from_trainer model-index
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ChemBERTa_drug_state_classification
  results: []
---

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# ChemBERTa_drug_state_classification

This model is a fine-tuned version of [nepp1d0/ChemBERTa_drug_state_classification](https://huggingface.co/nepp1d0/ChemBERTa_drug_state_classification) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0463
- Accuracy: 0.9870

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5063        | 1.0   | 240  | 0.3069          | 0.9160   |
| 0.3683        | 2.0   | 480  | 0.2135          | 0.9431   |
| 0.2633        | 3.0   | 720  | 0.1324          | 0.9577   |
| 0.1692        | 4.0   | 960  | 0.0647          | 0.9802   |
| 0.1109        | 5.0   | 1200 | 0.0463          | 0.9870   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.12.1
