---
license: cc-by-4.0
base_model: pythainlp/thainer-corpus-v2-base-model
tags:
- generated_from_trainer
datasets:
- lst20
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: toneza
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lst20
      type: lst20
      config: default
      split: validation
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.768370802562324
    - name: Recall
      type: recall
      value: 0.8120041393583994
    - name: F1
      type: f1
      value: 0.7895851240015932
    - name: Accuracy
      type: accuracy
      value: 0.956478116244312
---

<!-- 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. -->

# toneza

This model is a fine-tuned version of [pythainlp/thainer-corpus-v2-base-model](https://huggingface.co/pythainlp/thainer-corpus-v2-base-model) on the lst20 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1293
- Precision: 0.7684
- Recall: 0.8120
- F1: 0.7896
- Accuracy: 0.9565

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1226        | 1.0   | 1978 | 0.1416          | 0.7414    | 0.7802 | 0.7603 | 0.9518   |
| 0.098         | 2.0   | 3956 | 0.1324          | 0.7602    | 0.7966 | 0.7780 | 0.9545   |
| 0.0895        | 3.0   | 5934 | 0.1293          | 0.7684    | 0.8120 | 0.7896 | 0.9565   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0