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--- |
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license: mit |
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base_model: nielsr/lilt-xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- xfun |
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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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model-index: |
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- name: LiLT-SER-ZH |
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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: xfun |
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type: xfun |
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config: xfun.zh |
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split: validation |
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args: xfun.zh |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8408488063660478 |
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- name: Recall |
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type: recall |
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value: 0.9347968545216252 |
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- name: F1 |
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type: f1 |
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value: 0.8853374709076804 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8116519985331867 |
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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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# LiLT-SER-ZH |
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This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on the xfun dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8792 |
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- Precision: 0.8408 |
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- Recall: 0.9348 |
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- F1: 0.8853 |
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- Accuracy: 0.8117 |
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## Model description |
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More information needed |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 2 |
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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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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall | |
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|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:| |
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| 0.2166 | 10.64 | 500 | 0.7724 | 0.8544 | 1.1239 | 0.7932 | 0.9260 | |
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| 0.0238 | 21.28 | 1000 | 0.8201 | 0.8624 | 1.0535 | 0.8572 | 0.8676 | |
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| 0.0034 | 31.91 | 1500 | 0.8057 | 0.8599 | 1.4675 | 0.8088 | 0.9178 | |
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| 0.0163 | 42.55 | 2000 | 0.8232 | 0.8729 | 1.2837 | 0.8572 | 0.8893 | |
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| 0.0037 | 53.19 | 2500 | 0.8114 | 0.8627 | 1.5315 | 0.8142 | 0.9174 | |
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| 0.0003 | 63.83 | 3000 | 0.8137 | 0.8652 | 1.4604 | 0.8471 | 0.8840 | |
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| 0.0005 | 74.47 | 3500 | 0.8115 | 0.8767 | 1.5980 | 0.8409 | 0.9158 | |
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| 0.0005 | 85.11 | 4000 | 0.8129 | 0.8634 | 1.5108 | 0.8261 | 0.9043 | |
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| 0.0004 | 95.74 | 4500 | 0.8161 | 0.8817 | 1.7719 | 0.8397 | 0.9282 | |
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| 0.0001 | 106.38 | 5000 | 0.8203 | 0.8813 | 1.4313 | 0.8600 | 0.9037 | |
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| 0.0001 | 117.02 | 5500 | 0.8181 | 0.8832 | 1.5232 | 0.8509 | 0.9181 | |
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| 0.0 | 127.66 | 6000 | 0.8069 | 0.8808 | 1.6845 | 0.8532 | 0.9102 | |
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| 0.0179 | 138.3 | 6500 | 0.8192 | 0.8793 | 1.6293 | 0.8398 | 0.9227 | |
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| 0.0 | 148.94 | 7000 | 0.8081 | 0.8815 | 1.8209 | 0.8381 | 0.9296 | |
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| 0.0 | 159.57 | 7500 | 1.8224 | 0.8443 | 0.9184 | 0.8798 | 0.8070 | |
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| 0.0 | 170.21 | 8000 | 1.7810 | 0.8450 | 0.9305 | 0.8857 | 0.8127 | |
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| 0.0 | 180.85 | 8500 | 1.7531 | 0.8454 | 0.9230 | 0.8825 | 0.8088 | |
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| 0.0 | 191.49 | 9000 | 1.8757 | 0.8394 | 0.9302 | 0.8825 | 0.8070 | |
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| 0.0 | 202.13 | 9500 | 1.8757 | 0.8417 | 0.9338 | 0.8854 | 0.8123 | |
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| 0.0 | 212.77 | 10000 | 1.8792 | 0.8408 | 0.9348 | 0.8853 | 0.8117 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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