---
library_name: transformers
license: mit
base_model: chandar-lab/NeoBERT
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: NeoBERT_druglib_regression_6ep_5e-06lr
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# NeoBERT_druglib_regression_6ep_5e-06lr

This model is a fine-tuned version of [chandar-lab/NeoBERT](https://huggingface.co/chandar-lab/NeoBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9323
- Accuracy: 0.6284
- Macro Precision: 0.6396
- Macro Recall: 0.5598
- Macro F1: 0.5573

## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro Precision | Macro Recall | Macro F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|
| 1.2053        | 1.0   | 311  | 1.0258          | 0.5900   | 0.5546          | 0.4409       | 0.4394   |
| 0.743         | 2.0   | 622  | 0.8838          | 0.6527   | 0.6404          | 0.5392       | 0.5352   |
| 0.5941        | 3.0   | 933  | 0.9091          | 0.6624   | 0.6102          | 0.5566       | 0.5674   |
| 0.4384        | 4.0   | 1244 | 1.0823          | 0.6592   | 0.6108          | 0.5464       | 0.5493   |
| 0.138         | 5.0   | 1555 | 1.3475          | 0.6543   | 0.5926          | 0.5503       | 0.5600   |
| 0.0683        | 6.0   | 1866 | 1.4926          | 0.6495   | 0.5865          | 0.5571       | 0.5668   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0