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---
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 |