---
library_name: transformers
base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
- generated_from_trainer
datasets:
- orchid_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_test_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: orchid_corpus
      type: orchid_corpus
      config: thai_orchid_dataset
      split: test
      args: thai_orchid_dataset
    metrics:
    - name: Precision
      type: precision
      value: 0.6866152910160211
    - name: Recall
      type: recall
      value: 0.6612566160817172
    - name: F1
      type: f1
      value: 0.673697406254042
    - name: Accuracy
      type: accuracy
      value: 0.8615229701891011
---

<!-- 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. -->

# my_test_model

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 orchid_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4402
- Precision: 0.6866
- Recall: 0.6613
- F1: 0.6737
- Accuracy: 0.8615

## 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: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6071        | 1.0   | 1157 | 0.4812          | 0.6686    | 0.6451 | 0.6566 | 0.8524   |
| 0.4831        | 2.0   | 2314 | 0.4402          | 0.6866    | 0.6613 | 0.6737 | 0.8615   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0