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--- |
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base_model: vinai/phobert-base-v2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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model-index: |
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- name: cls-comment-phobert-base-v2-v2.3 |
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results: [] |
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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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# cls-comment-phobert-base-v2-v2.3 |
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This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6228 |
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- Accuracy: 0.9272 |
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- F1 Score: 0.8944 |
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- Recall: 0.8843 |
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- Precision: 0.9063 |
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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: 1e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 4000 |
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- label_smoothing_factor: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:| |
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| 1.1884 | 1.04 | 100 | 1.0905 | 0.7277 | 0.4371 | 0.4229 | 0.5017 | |
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| 1.0502 | 2.08 | 200 | 0.9278 | 0.7956 | 0.5245 | 0.5274 | 0.5244 | |
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| 0.9031 | 3.12 | 300 | 0.7793 | 0.8446 | 0.5653 | 0.5897 | 0.7067 | |
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| 0.7772 | 4.17 | 400 | 0.7090 | 0.8697 | 0.6917 | 0.6804 | 0.7269 | |
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| 0.6741 | 5.21 | 500 | 0.6483 | 0.8959 | 0.8078 | 0.7818 | 0.8798 | |
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| 0.6156 | 6.25 | 600 | 0.6268 | 0.9079 | 0.8601 | 0.8345 | 0.8933 | |
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| 0.5745 | 7.29 | 700 | 0.6176 | 0.9161 | 0.8739 | 0.8610 | 0.8881 | |
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| 0.5466 | 8.33 | 800 | 0.6134 | 0.9181 | 0.8730 | 0.8555 | 0.8951 | |
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| 0.5254 | 9.38 | 900 | 0.6196 | 0.9184 | 0.8794 | 0.8634 | 0.8980 | |
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| 0.5059 | 10.42 | 1000 | 0.6175 | 0.9220 | 0.8799 | 0.8702 | 0.8925 | |
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| 0.4971 | 11.46 | 1100 | 0.6110 | 0.9226 | 0.8771 | 0.8639 | 0.8929 | |
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| 0.4872 | 12.5 | 1200 | 0.6191 | 0.9230 | 0.8834 | 0.8774 | 0.8914 | |
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| 0.4756 | 13.54 | 1300 | 0.6240 | 0.9243 | 0.8929 | 0.8931 | 0.8938 | |
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| 0.4737 | 14.58 | 1400 | 0.6246 | 0.9203 | 0.8794 | 0.8582 | 0.9038 | |
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| 0.4626 | 15.62 | 1500 | 0.6267 | 0.9249 | 0.8890 | 0.8842 | 0.8955 | |
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| 0.4641 | 16.67 | 1600 | 0.6228 | 0.9272 | 0.8944 | 0.8843 | 0.9063 | |
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| 0.4562 | 17.71 | 1700 | 0.6256 | 0.9278 | 0.8923 | 0.8800 | 0.9065 | |
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| 0.4522 | 18.75 | 1800 | 0.6203 | 0.9285 | 0.8913 | 0.8845 | 0.8993 | |
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| 0.4476 | 19.79 | 1900 | 0.6258 | 0.9262 | 0.8849 | 0.8692 | 0.9034 | |
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| 0.4474 | 20.83 | 2000 | 0.6333 | 0.9272 | 0.8932 | 0.8751 | 0.9134 | |
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| 0.4421 | 21.88 | 2100 | 0.6340 | 0.9292 | 0.8929 | 0.8896 | 0.8975 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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