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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: output2
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. -->
# output2
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0553
- Accuracy: 0.7264
- F1: 0.7307
## Model description
```
from transformers import pipeline
model = pipeline(model="Pannathad/xlm-roberta-base-th-product-review-sentiment-analysis")
result = model(["มีการกันกระแทกอย่างดี", "มีการห่อบับเบิ้ลอย่างหนา","มาส่งไว","แต่ราคาแพงมาก"])
# result
[
{'label': 'Quality', 'score': 0.8555123209953308},
{'label': 'Packaging', 'score': 0.9143754243850708},
{'label': 'DeliveryTime', 'score': 0.9672013521194458},
{'label': 'NEG-Price', 'score': 0.6597796082496643}
]
```
## 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: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.5671 | 1.0 | 26 | 2.3849 | 0.2712 | 0.1157 |
| 2.4121 | 2.0 | 52 | 2.2398 | 0.3438 | 0.2029 |
| 2.2116 | 3.0 | 78 | 1.8608 | 0.4649 | 0.3680 |
| 1.8806 | 4.0 | 104 | 1.5004 | 0.5763 | 0.5189 |
| 1.5356 | 5.0 | 130 | 1.2657 | 0.6077 | 0.5605 |
| 1.2656 | 6.0 | 156 | 1.0881 | 0.6852 | 0.6578 |
| 1.0485 | 7.0 | 182 | 1.1436 | 0.6707 | 0.6556 |
| 0.9568 | 8.0 | 208 | 1.0253 | 0.7143 | 0.6974 |
| 0.813 | 9.0 | 234 | 0.9546 | 0.7070 | 0.6900 |
| 0.7071 | 10.0 | 260 | 0.9333 | 0.7458 | 0.7287 |
| 0.613 | 11.0 | 286 | 1.0258 | 0.7167 | 0.7038 |
| 0.5596 | 12.0 | 312 | 0.9554 | 0.7119 | 0.6996 |
| 0.5081 | 13.0 | 338 | 1.0385 | 0.7215 | 0.7147 |
| 0.4615 | 14.0 | 364 | 0.9769 | 0.7264 | 0.7165 |
| 0.4102 | 15.0 | 390 | 0.9845 | 0.7215 | 0.7213 |
| 0.3453 | 16.0 | 416 | 0.9315 | 0.7361 | 0.7343 |
| 0.3521 | 17.0 | 442 | 0.9916 | 0.7409 | 0.7439 |
| 0.2984 | 18.0 | 468 | 1.0486 | 0.7264 | 0.7261 |
| 0.2737 | 19.0 | 494 | 1.0325 | 0.7215 | 0.7239 |
| 0.2611 | 20.0 | 520 | 1.0210 | 0.7337 | 0.7371 |
| 0.2436 | 21.0 | 546 | 1.0508 | 0.7264 | 0.7283 |
| 0.2451 | 22.0 | 572 | 1.0487 | 0.7312 | 0.7344 |
| 0.2285 | 23.0 | 598 | 1.0434 | 0.7337 | 0.7366 |
| 0.2072 | 24.0 | 624 | 1.0530 | 0.7288 | 0.7326 |
| 0.2078 | 25.0 | 650 | 1.0553 | 0.7264 | 0.7307 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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