---
library_name: transformers
license: apache-2.0
base_model: Geotrend/bert-base-th-cased
tags:
- generated_from_trainer
datasets:
- lst20
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lst20
      type: lst20
      config: lst20
      split: validation
      args: lst20
    metrics:
    - name: Precision
      type: precision
      value: 0.8146895294348094
    - name: Recall
      type: recall
      value: 0.8409048492954679
    - name: F1
      type: f1
      value: 0.8275896376229288
    - name: Accuracy
      type: accuracy
      value: 0.9378905377283859
---

<!-- 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. -->

# test-ner

This model is a fine-tuned version of [Geotrend/bert-base-th-cased](https://huggingface.co/Geotrend/bert-base-th-cased) on the lst20 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1910
- Precision: 0.8147
- Recall: 0.8409
- F1: 0.8276
- Accuracy: 0.9379

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1623        | 1.0   | 3957 | 0.1910          | 0.8147    | 0.8409 | 0.8276 | 0.9379   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1