---
library_name: transformers
license: mit
base_model: Mardiyyah/cellate1.0-tapt_freeze_llrd_ww_mask-LR_2e-05
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: TAPT_CeLLaTe_llrd_only
  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. -->

# TAPT_CeLLaTe_llrd_only

This model is a fine-tuned version of [Mardiyyah/cellate1.0-tapt_freeze_llrd_ww_mask-LR_2e-05](https://huggingface.co/Mardiyyah/cellate1.0-tapt_freeze_llrd_ww_mask-LR_2e-05) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1153
- Precision: 0.8168
- Recall: 0.8404
- F1: 0.8285
- Accuracy: 0.9743

## 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: 16
- 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: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.5314        | 1.0   | 55   | 0.6843          | 0.0       | 0.0    | 0.0    | 0.8947   |
| 0.4343        | 2.0   | 110  | 0.2517          | 0.3447    | 0.3526 | 0.3486 | 0.9195   |
| 0.2147        | 3.0   | 165  | 0.1484          | 0.6493    | 0.7204 | 0.6830 | 0.9563   |
| 0.1396        | 4.0   | 220  | 0.1172          | 0.7452    | 0.7599 | 0.7524 | 0.9681   |
| 0.1112        | 5.0   | 275  | 0.1102          | 0.7370    | 0.8176 | 0.7752 | 0.9660   |
| 0.0892        | 6.0   | 330  | 0.0984          | 0.7994    | 0.7994 | 0.7994 | 0.9713   |
| 0.0747        | 7.0   | 385  | 0.1059          | 0.8238    | 0.8100 | 0.8169 | 0.9735   |
| 0.0643        | 8.0   | 440  | 0.1112          | 0.7768    | 0.8252 | 0.8003 | 0.9703   |
| 0.0533        | 9.0   | 495  | 0.1079          | 0.8361    | 0.8298 | 0.8330 | 0.9748   |
| 0.0473        | 10.0  | 550  | 0.1082          | 0.8121    | 0.8343 | 0.8231 | 0.9736   |
| 0.0445        | 11.0  | 605  | 0.1094          | 0.8468    | 0.8146 | 0.8304 | 0.9750   |
| 0.0375        | 12.0  | 660  | 0.1047          | 0.8477    | 0.8374 | 0.8425 | 0.9762   |
| 0.0312        | 13.0  | 715  | 0.1052          | 0.8149    | 0.8298 | 0.8223 | 0.9741   |
| 0.0299        | 14.0  | 770  | 0.1095          | 0.8070    | 0.8389 | 0.8227 | 0.9727   |
| 0.0269        | 15.0  | 825  | 0.1195          | 0.7874    | 0.8389 | 0.8124 | 0.9718   |
| 0.0238        | 16.0  | 880  | 0.1096          | 0.8301    | 0.8389 | 0.8345 | 0.9749   |
| 0.0218        | 17.0  | 935  | 0.1134          | 0.8070    | 0.8450 | 0.8255 | 0.9741   |
| 0.022         | 18.0  | 990  | 0.1174          | 0.8038    | 0.8404 | 0.8217 | 0.9736   |
| 0.02          | 19.0  | 1045 | 0.1189          | 0.8151    | 0.8374 | 0.8261 | 0.9741   |
| 0.02          | 20.0  | 1100 | 0.1153          | 0.8168    | 0.8404 | 0.8285 | 0.9743   |


### Framework versions

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