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---
language:
- en
license: mit
base_model: microsoft/deberta-v3-base
tags:
- nycu-112-2-datamining-hw2
- generated_from_trainer
datasets:
- DandinPower/review_mergeallfeaturetotext
metrics:
- accuracy
model-index:
- name: deberta-v3-base-maftt
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: DandinPower/review_mergeallfeaturetotext
type: DandinPower/review_mergeallfeaturetotext
metrics:
- name: Accuracy
type: accuracy
value: 0.6288571428571429
---
<!-- 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. -->
# deberta-v3-base-maftt
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the DandinPower/review_mergeallfeaturetotext dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4616
- Accuracy: 0.6289
- Macro F1: 0.6302
## 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: 4.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 1.0302 | 0.14 | 500 | 1.0771 | 0.5511 | 0.5499 |
| 1.0412 | 0.29 | 1000 | 0.9406 | 0.5966 | 0.6030 |
| 0.9494 | 0.43 | 1500 | 0.9546 | 0.5949 | 0.5602 |
| 0.898 | 0.57 | 2000 | 1.0436 | 0.5957 | 0.5872 |
| 0.9171 | 0.71 | 2500 | 0.9004 | 0.622 | 0.6074 |
| 0.8856 | 0.86 | 3000 | 0.8741 | 0.6137 | 0.5990 |
| 0.9359 | 1.0 | 3500 | 0.8821 | 0.6267 | 0.6245 |
| 0.8626 | 1.14 | 4000 | 0.8859 | 0.6213 | 0.6200 |
| 0.7953 | 1.29 | 4500 | 0.8606 | 0.6337 | 0.6271 |
| 0.8206 | 1.43 | 5000 | 0.8543 | 0.6169 | 0.6202 |
| 0.8184 | 1.57 | 5500 | 0.9360 | 0.6266 | 0.6165 |
| 0.8044 | 1.71 | 6000 | 0.8606 | 0.6234 | 0.6227 |
| 0.7094 | 1.86 | 6500 | 0.8842 | 0.6434 | 0.6387 |
| 0.8264 | 2.0 | 7000 | 0.9063 | 0.612 | 0.6128 |
| 0.6951 | 2.14 | 7500 | 0.8782 | 0.6386 | 0.6415 |
| 0.704 | 2.29 | 8000 | 0.9510 | 0.6326 | 0.6308 |
| 0.6806 | 2.43 | 8500 | 0.8709 | 0.6413 | 0.6455 |
| 0.6983 | 2.57 | 9000 | 0.8977 | 0.6426 | 0.6436 |
| 0.6852 | 2.71 | 9500 | 0.9686 | 0.5984 | 0.6010 |
| 0.6761 | 2.86 | 10000 | 0.8961 | 0.6386 | 0.6406 |
| 0.6804 | 3.0 | 10500 | 0.9378 | 0.6307 | 0.6332 |
| 0.5329 | 3.14 | 11000 | 1.1209 | 0.6341 | 0.6382 |
| 0.5461 | 3.29 | 11500 | 1.0323 | 0.6393 | 0.6377 |
| 0.5725 | 3.43 | 12000 | 1.0678 | 0.6334 | 0.6366 |
| 0.5499 | 3.57 | 12500 | 1.0547 | 0.6374 | 0.6394 |
| 0.5218 | 3.71 | 13000 | 1.0524 | 0.6453 | 0.6460 |
| 0.5022 | 3.86 | 13500 | 1.1100 | 0.6363 | 0.6358 |
| 0.534 | 4.0 | 14000 | 1.0378 | 0.6357 | 0.6386 |
| 0.3823 | 4.14 | 14500 | 1.3985 | 0.6357 | 0.6357 |
| 0.4518 | 4.29 | 15000 | 1.3265 | 0.6314 | 0.6318 |
| 0.4147 | 4.43 | 15500 | 1.3946 | 0.631 | 0.6324 |
| 0.3936 | 4.57 | 16000 | 1.4649 | 0.6279 | 0.6308 |
| 0.4339 | 4.71 | 16500 | 1.5322 | 0.6286 | 0.6314 |
| 0.4448 | 4.86 | 17000 | 1.4890 | 0.629 | 0.6302 |
| 0.4006 | 5.0 | 17500 | 1.4616 | 0.6289 | 0.6302 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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