---
base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: wcBERTaAttSpmm-ggTranslate-senticPolarEmotion-bully-f2
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/herobye13579/huggingface/runs/i28c752o)
# wcBERTaAttSpmm-ggTranslate-senticPolarEmotion-bully-f2

This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5206
- Accuracy: 0.7354
- Precision: 0.7320
- Recall: 0.7354
- F1 Score: 0.7335

## 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: 5e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 0.6386        | 1.0   | 120  | 0.6121          | 0.7008   | 0.4912    | 0.7008 | 0.5776   |
| 0.603         | 2.0   | 240  | 0.5663          | 0.7144   | 0.6835    | 0.7144 | 0.6762   |
| 0.5758        | 3.0   | 360  | 0.5828          | 0.7029   | 0.6989    | 0.7029 | 0.7007   |
| 0.5724        | 4.0   | 480  | 0.5518          | 0.7238   | 0.6978    | 0.7238 | 0.6894   |
| 0.5412        | 5.0   | 600  | 0.5336          | 0.7082   | 0.6926    | 0.7082 | 0.6975   |
| 0.5295        | 6.0   | 720  | 0.5222          | 0.7280   | 0.7111    | 0.7280 | 0.7145   |
| 0.4851        | 7.0   | 840  | 0.5090          | 0.7312   | 0.7136    | 0.7312 | 0.7162   |
| 0.4639        | 8.0   | 960  | 0.5153          | 0.7259   | 0.7164    | 0.7259 | 0.7200   |
| 0.4606        | 9.0   | 1080 | 0.5233          | 0.7029   | 0.7241    | 0.7029 | 0.7104   |
| 0.4412        | 10.0  | 1200 | 0.5501          | 0.6998   | 0.7496    | 0.6998 | 0.7116   |
| 0.4106        | 11.0  | 1320 | 0.5262          | 0.7155   | 0.7398    | 0.7155 | 0.7235   |
| 0.3976        | 12.0  | 1440 | 0.5148          | 0.7374   | 0.7300    | 0.7374 | 0.7329   |
| 0.3917        | 13.0  | 1560 | 0.5194          | 0.7364   | 0.7338    | 0.7364 | 0.7350   |
| 0.3858        | 14.0  | 1680 | 0.5225          | 0.7259   | 0.7293    | 0.7259 | 0.7275   |
| 0.3853        | 15.0  | 1800 | 0.5206          | 0.7354   | 0.7320    | 0.7354 | 0.7335   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1