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---
license: mit
base_model: tangminhanh/cate-ts
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: subcate-ts
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. -->
# subcate-ts
This model is a fine-tuned version of [tangminhanh/cate-ts](https://huggingface.co/tangminhanh/cate-ts) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0130
- Accuracy: 0.3377
- F1: 0.4963
- Precision: 0.9460
- Recall: 0.3364
## 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: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8242 | 0.9987 | 188 | 0.2821 | 0.0 | 0.0081 | 0.0046 | 0.0321 |
| 0.1451 | 1.9973 | 376 | 0.0313 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0298 | 2.9960 | 564 | 0.0231 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.024 | 4.0 | 753 | 0.0206 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0212 | 4.9987 | 941 | 0.0181 | 0.1261 | 0.2233 | 0.9922 | 0.1258 |
| 0.0187 | 5.9973 | 1129 | 0.0162 | 0.2292 | 0.3696 | 0.9664 | 0.2285 |
| 0.0169 | 6.9960 | 1317 | 0.0147 | 0.3081 | 0.4657 | 0.9646 | 0.3070 |
| 0.0157 | 8.0 | 1506 | 0.0137 | 0.3184 | 0.4763 | 0.9551 | 0.3172 |
| 0.0151 | 8.9987 | 1694 | 0.0132 | 0.3310 | 0.4894 | 0.9486 | 0.3298 |
| 0.0145 | 9.9867 | 1880 | 0.0130 | 0.3377 | 0.4963 | 0.9460 | 0.3364 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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