---
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