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--- |
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license: mit |
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base_model: microsoft/mdeberta-v3-base |
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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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- precision |
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- recall |
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model-index: |
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- name: mdeberta-v3-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: 0.9934846474601972 |
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widget: |
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- text: นักวิจัยกล่าวว่าการวิเคราะห์ดีเอ็นเอของเนื้องอกอาจช่วยอธิบายถึงสาเหตุที่แท้จริงของมะเร็งชนิดอื่นๆ ได้ |
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example_title: test_example_1 |
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- text: >- |
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คือผมไม่ได้ชอบกดดันพวกคุณหรอกนะ แต่ชะตากรรมของสาธารณรัฐอยู่ในกำมือคุณ |
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example_title: test_example_2 |
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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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# mdeberta-v3-ud-thai-pud-upos |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the universal_dependencies dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0303 |
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- Macro avg precision: 0.9235 |
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- Macro avg recall: 0.9228 |
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- Macro avg f1: 0.9231 |
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- Weighted avg precision: 0.9935 |
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- Weighted avg recall: 0.9935 |
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- Weighted avg f1: 0.9935 |
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- Accuracy: 0.9935 |
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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/mdeberta-v3-ud-thai-pud-upos") |
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tokenizer = AutoTokenizer.from_pretrained("Pavarissy/mdeberta-v3-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': 'PROPN', 'score': 0.9946701, 'word': 'ประเทศไทย', 'start': 0, 'end': 9}, {'entity_group': 'VERB', 'score': 0.85809743, 'word': 'อยู่ใน', 'start': 9, 'end': 16}, {'entity_group': 'NOUN', 'score': 0.99632, 'word': 'ทวีป', 'start': 16, 'end': 21}, {'entity_group': 'PROPN', 'score': 0.9961184, '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 | 0.3898 | 0.8417 | 0.7849 | 0.8078 | 0.9119 | 0.9112 | 0.9101 | 0.9112 | |
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| No log | 2.0 | 250 | 0.1768 | 0.8765 | 0.8683 | 0.8720 | 0.9561 | 0.9560 | 0.9559 | 0.9560 | |
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| No log | 3.0 | 375 | 0.1217 | 0.8972 | 0.8892 | 0.8929 | 0.9701 | 0.9701 | 0.9699 | 0.9701 | |
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| 0.4709 | 4.0 | 500 | 0.0841 | 0.9057 | 0.9064 | 0.9059 | 0.9802 | 0.9800 | 0.9800 | 0.9800 | |
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| 0.4709 | 5.0 | 625 | 0.0649 | 0.9128 | 0.9133 | 0.9130 | 0.9854 | 0.9853 | 0.9853 | 0.9853 | |
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| 0.4709 | 6.0 | 750 | 0.0513 | 0.9147 | 0.9170 | 0.9158 | 0.9878 | 0.9877 | 0.9877 | 0.9877 | |
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| 0.4709 | 7.0 | 875 | 0.0423 | 0.9199 | 0.9180 | 0.9189 | 0.9900 | 0.9900 | 0.9900 | 0.9900 | |
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| 0.0857 | 8.0 | 1000 | 0.0350 | 0.9226 | 0.9207 | 0.9216 | 0.9921 | 0.9921 | 0.9921 | 0.9921 | |
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| 0.0857 | 9.0 | 1125 | 0.0318 | 0.9237 | 0.9219 | 0.9228 | 0.9932 | 0.9932 | 0.9932 | 0.9932 | |
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| 0.0857 | 10.0 | 1250 | 0.0303 | 0.9235 | 0.9228 | 0.9231 | 0.9935 | 0.9935 | 0.9935 | 0.9935 | |
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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 |
