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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- seanghay/khPOS |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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widget: |
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- text: គាត់ផឹកទឹកនៅភ្នំពេញ |
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- text: តើលោកស្រីបានសាកសួរទៅគាត់ទេ? |
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- text: នេត្រា មិនដឹងសោះថាអ្នកជាមនុស្ស! |
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model-index: |
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- name: khmer-pos-roberta-10 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: kh_pos |
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type: kh_pos |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9511876225757245 |
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- name: Recall |
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type: recall |
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value: 0.9526407682234832 |
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- name: F1 |
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type: f1 |
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value: 0.9519136408243376 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9735370853522176 |
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language: |
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- km |
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library_name: transformers |
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pipeline_tag: token-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Khmer Part of Speech Tagging with XLM RoBERTa |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [khPOS](https://huggingface.co/seanghay/khPOS) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1063 |
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- Precision: 0.9512 |
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- Recall: 0.9526 |
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- F1: 0.9519 |
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- Accuracy: 0.9735 |
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## Model description |
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The [original paper](https://arxiv.org/pdf/2103.16801.pdf) achieved 98.15% accuracy while this model achieved only 97.35% which is close. However, this is a multilingual model so it has more tokens than the original paper. |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 24 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 450 | 0.1347 | 0.9314 | 0.9333 | 0.9324 | 0.9603 | |
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| 0.4834 | 2.0 | 900 | 0.1183 | 0.9407 | 0.9377 | 0.9392 | 0.9653 | |
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| 0.1323 | 3.0 | 1350 | 0.1026 | 0.9484 | 0.9482 | 0.9483 | 0.9699 | |
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| 0.095 | 4.0 | 1800 | 0.0986 | 0.9502 | 0.9490 | 0.9496 | 0.9712 | |
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| 0.0774 | 5.0 | 2250 | 0.0978 | 0.9494 | 0.9491 | 0.9493 | 0.9712 | |
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| 0.0616 | 6.0 | 2700 | 0.0991 | 0.9493 | 0.9507 | 0.9500 | 0.9715 | |
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| 0.0494 | 7.0 | 3150 | 0.0989 | 0.9529 | 0.9540 | 0.9534 | 0.9731 | |
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| 0.0414 | 8.0 | 3600 | 0.1037 | 0.9499 | 0.9501 | 0.9500 | 0.9722 | |
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| 0.0339 | 9.0 | 4050 | 0.1056 | 0.9516 | 0.9517 | 0.9516 | 0.9734 | |
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| 0.029 | 10.0 | 4500 | 0.1063 | 0.9512 | 0.9526 | 0.9519 | 0.9735 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
