---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_model
  results: []
datasets:
- pythainlp/thainer-corpus-v2
language:
- th
---

<!-- 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. -->

# ner_model

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1247
- Precision: 0.8073
- Recall: 0.8695
- F1: 0.8372
- Accuracy: 0.9655

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.4   | 100  | 0.5360          | 0.4604    | 0.4644 | 0.4624 | 0.8846   |
| No log        | 0.81  | 200  | 0.2882          | 0.6137    | 0.6619 | 0.6369 | 0.9307   |
| No log        | 1.21  | 300  | 0.2128          | 0.7236    | 0.7649 | 0.7437 | 0.9442   |
| No log        | 1.62  | 400  | 0.1811          | 0.7146    | 0.7925 | 0.7515 | 0.9494   |
| 0.4608        | 2.02  | 500  | 0.1594          | 0.7369    | 0.8021 | 0.7681 | 0.9542   |
| 0.4608        | 2.43  | 600  | 0.1532          | 0.7494    | 0.8331 | 0.7890 | 0.9572   |
| 0.4608        | 2.83  | 700  | 0.1403          | 0.7660    | 0.8417 | 0.8021 | 0.9594   |
| 0.4608        | 3.24  | 800  | 0.1342          | 0.7909    | 0.8428 | 0.8160 | 0.9625   |
| 0.4608        | 3.64  | 900  | 0.1325          | 0.7867    | 0.8572 | 0.8204 | 0.9626   |
| 0.1256        | 4.05  | 1000 | 0.1275          | 0.8056    | 0.8632 | 0.8334 | 0.9648   |
| 0.1256        | 4.45  | 1100 | 0.1229          | 0.8131    | 0.8643 | 0.8379 | 0.9657   |
| 0.1256        | 4.86  | 1200 | 0.1247          | 0.8073    | 0.8695 | 0.8372 | 0.9655   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0