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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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- recall |
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- precision |
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- f1 |
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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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widget: |
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- text: นักวิจัยกล่าวว่าการวิเคราะห์ดีเอ็นเอของเนื้องอกอาจช่วยอธิบายถึงสาเหตุที่แท้จริงของมะเร็งชนิดอื่นๆ ได้ |
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example_title: test1 |
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- text: >- |
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คือผมไม่ได้ชอบกดดันพวกคุณหรอกนะ แต่ชะตากรรมของสาธารณรัฐอยู่ในกำมือคุณ |
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example_title: test2 |
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language: |
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- th |
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library_name: transformers |
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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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# wangchanberta-ud-thai-pud-upos |
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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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## Model description |
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This model is train on thai UD Thai PUD corpus with `Universal Part-of-speech (UPOS)` tag to help with pos tagging in Thai language. |
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## Example |
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```python |
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from transformers import AutoModelForTokenClassification, AutoTokenizer, TokenClassificationPipeline |
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model = AutoModelForTokenClassification.from_pretrained("Pavarissy/wangchanberta-ud-thai-pud-upos") |
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tokenizer = AutoTokenizer.from_pretrained("Pavarissy/wangchanberta-ud-thai-pud-upos") |
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pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer, grouped_entities=True) |
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outputs = pipeline("ประเทศไทย อยู่ใน ทวีป เอเชีย") |
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print(outputs) |
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# [{'entity_group': 'NOUN', 'score': 0.419697, 'word': '', 'start': 0, 'end': 1}, {'entity_group': 'PROPN', 'score': 0.8809489, 'word': 'ประเทศไทย', 'start': 0, 'end': 9}, {'entity_group': 'VERB', 'score': 0.7754166, 'word': 'อยู่ใน', 'start': 9, 'end': 16}, {'entity_group': 'NOUN', 'score': 0.9976932, 'word': 'ทวีป', 'start': 16, 'end': 21}, {'entity_group': 'PROPN', 'score': 0.97770107, 'word': 'เอเชีย', 'start': 21, 'end': 28}] |
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``` |
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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: 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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### Training results |
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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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### Framework versions |
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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 |
