---
library_name: transformers
license: mit
base_model: Mardiyyah/cellate2.0-tapt_base-LR_5e-05
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: no_vague_no_downsample
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# no_vague_no_downsample

This model is a fine-tuned version of [Mardiyyah/cellate2.0-tapt_base-LR_5e-05](https://huggingface.co/Mardiyyah/cellate2.0-tapt_base-LR_5e-05) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0743
- Precision: 0.7128
- Recall: 0.7825
- F1: 0.7460
- Accuracy: 0.9815

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 3407
- optimizer: Use OptimizerNames.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: 0.01
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7697        | 0.4950 | 100  | 0.1502          | 0.3079    | 0.2172 | 0.2547 | 0.9608   |
| 0.1727        | 0.9901 | 200  | 0.1198          | 0.4065    | 0.6694 | 0.5058 | 0.9620   |
| 0.1057        | 1.4851 | 300  | 0.0818          | 0.7075    | 0.6856 | 0.6964 | 0.9804   |
| 0.0753        | 1.9802 | 400  | 0.0765          | 0.7167    | 0.7244 | 0.7205 | 0.9807   |
| 0.0555        | 2.4752 | 500  | 0.1019          | 0.3659    | 0.8505 | 0.5117 | 0.9471   |
| 0.0511        | 2.9703 | 600  | 0.0741          | 0.7128    | 0.7825 | 0.7460 | 0.9815   |
| 0.0381        | 3.4653 | 700  | 0.0898          | 0.7111    | 0.7458 | 0.7280 | 0.9811   |
| 0.0369        | 3.9604 | 800  | 0.0846          | 0.7078    | 0.7804 | 0.7423 | 0.9818   |
| 0.0295        | 4.4554 | 900  | 0.0919          | 0.6923    | 0.7723 | 0.7301 | 0.9809   |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.21.0