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Pavarissy
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wangchanberta-ud-thai-pud-upos

Token Classification
Transformers
PyTorch
Thai
camembert
Generated from Trainer
Eval Results
Inference Endpoints
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Pavarissy/wangchanberta-ud-thai-pud-upos

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+ ---
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+ base_model: airesearch/wangchanberta-base-att-spm-uncased
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - universal_dependencies
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: wangchanberta-ud-thai-pud-upos
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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: universal_dependencies
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+ type: universal_dependencies
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+ config: th_pud
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+ split: test
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+ args: th_pud
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9883334914161055
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+ ---
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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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+
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+ # wangchanberta-ud-thai-pud-upos
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+
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+ This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the universal_dependencies dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0442
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+ - Macro avg precision: 0.9221
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+ - Macro avg recall: 0.9178
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+ - Macro avg f1: 0.9199
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+ - Weighted avg precision: 0.9883
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+ - Weighted avg recall: 0.9883
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+ - Weighted avg f1: 0.9883
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+ - Accuracy: 0.9883
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 8
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+ - eval_batch_size: 8
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Macro avg precision | Macro avg recall | Macro avg f1 | Weighted avg precision | Weighted avg recall | Weighted avg f1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------------:|:-------------------:|:---------------:|:--------:|
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+ | No log | 1.0 | 125 | 0.5563 | 0.8103 | 0.7235 | 0.7552 | 0.8574 | 0.8522 | 0.8495 | 0.8522 |
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+ | No log | 2.0 | 250 | 0.2316 | 0.8701 | 0.8460 | 0.8564 | 0.9320 | 0.9315 | 0.9310 | 0.9315 |
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+ | No log | 3.0 | 375 | 0.1635 | 0.8903 | 0.8729 | 0.8809 | 0.9511 | 0.9511 | 0.9508 | 0.9511 |
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+ | 0.5782 | 4.0 | 500 | 0.1112 | 0.9037 | 0.8964 | 0.8998 | 0.9687 | 0.9685 | 0.9685 | 0.9685 |
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+ | 0.5782 | 5.0 | 625 | 0.0860 | 0.9110 | 0.9050 | 0.9079 | 0.9752 | 0.9752 | 0.9751 | 0.9752 |
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+ | 0.5782 | 6.0 | 750 | 0.0675 | 0.9160 | 0.9103 | 0.9131 | 0.9815 | 0.9814 | 0.9814 | 0.9814 |
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+ | 0.5782 | 7.0 | 875 | 0.0588 | 0.9189 | 0.9138 | 0.9163 | 0.9839 | 0.9839 | 0.9839 | 0.9839 |
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+ | 0.1073 | 8.0 | 1000 | 0.0514 | 0.9214 | 0.9155 | 0.9184 | 0.9858 | 0.9858 | 0.9858 | 0.9858 |
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+ | 0.1073 | 9.0 | 1125 | 0.0463 | 0.9225 | 0.9171 | 0.9197 | 0.9877 | 0.9876 | 0.9876 | 0.9876 |
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+ | 0.1073 | 10.0 | 1250 | 0.0442 | 0.9221 | 0.9178 | 0.9199 | 0.9883 | 0.9883 | 0.9883 | 0.9883 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.34.1
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.14.6
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+ - Tokenizers 0.14.1