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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
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