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--- |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: vinai/phobert-base |
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metrics: |
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- accuracy |
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model-index: |
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- name: training_sentiment_analysis |
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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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# training_sentiment_analysis |
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This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5287 |
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- Accuracy: 0.7977 |
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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: 0.0003 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.98) 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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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.9299 | 0.21 | 200 | 0.8274 | 0.6387 | |
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| 0.7793 | 0.43 | 400 | 0.6643 | 0.7188 | |
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| 0.6574 | 0.64 | 600 | 0.5868 | 0.7659 | |
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| 0.6132 | 0.86 | 800 | 0.5582 | 0.7723 | |
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| 0.5791 | 1.07 | 1000 | 0.5516 | 0.7831 | |
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| 0.554 | 1.28 | 1200 | 0.5187 | 0.7964 | |
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| 0.5258 | 1.5 | 1400 | 0.5126 | 0.8034 | |
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| 0.5373 | 1.71 | 1600 | 0.5168 | 0.8003 | |
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| 0.5266 | 1.93 | 1800 | 0.5284 | 0.8028 | |
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| 0.5076 | 2.14 | 2000 | 0.5178 | 0.7977 | |
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| 0.5094 | 2.36 | 2200 | 0.5135 | 0.8028 | |
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| 0.5032 | 2.57 | 2400 | 0.5023 | 0.8104 | |
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| 0.5034 | 2.78 | 2600 | 0.5088 | 0.8047 | |
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| 0.4923 | 3.0 | 2800 | 0.5219 | 0.7996 | |
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| 0.4934 | 3.21 | 3000 | 0.4905 | 0.8130 | |
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| 0.4798 | 3.43 | 3200 | 0.4908 | 0.8098 | |
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| 0.4831 | 3.64 | 3400 | 0.4875 | 0.8073 | |
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| 0.4707 | 3.85 | 3600 | 0.4986 | 0.8073 | |
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| 0.4674 | 4.07 | 3800 | 0.5196 | 0.8104 | |
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| 0.4535 | 4.28 | 4000 | 0.4896 | 0.8098 | |
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| 0.464 | 4.5 | 4200 | 0.5175 | 0.8079 | |
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| 0.4715 | 4.71 | 4400 | 0.5002 | 0.8028 | |
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| 0.468 | 4.93 | 4600 | 0.4883 | 0.8111 | |
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| 0.4645 | 5.14 | 4800 | 0.5187 | 0.8041 | |
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| 0.445 | 5.35 | 5000 | 0.4928 | 0.8066 | |
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| 0.4558 | 5.57 | 5200 | 0.4870 | 0.8079 | |
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| 0.4405 | 5.78 | 5400 | 0.4985 | 0.8104 | |
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| 0.4648 | 6.0 | 5600 | 0.4842 | 0.8060 | |
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| 0.435 | 6.21 | 5800 | 0.4911 | 0.8117 | |
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| 0.437 | 6.42 | 6000 | 0.4854 | 0.8085 | |
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| 0.4588 | 6.64 | 6200 | 0.4879 | 0.8085 | |
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| 0.4342 | 6.85 | 6400 | 0.4922 | 0.8104 | |
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| 0.4347 | 7.07 | 6600 | 0.4911 | 0.8142 | |
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| 0.4326 | 7.28 | 6800 | 0.4914 | 0.8079 | |
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| 0.4267 | 7.49 | 7000 | 0.4917 | 0.8104 | |
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| 0.4241 | 7.71 | 7200 | 0.4887 | 0.8136 | |
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| 0.4376 | 7.92 | 7400 | 0.5122 | 0.8079 | |
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| 0.4323 | 8.14 | 7600 | 0.4909 | 0.8098 | |
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| 0.4264 | 8.35 | 7800 | 0.4882 | 0.8142 | |
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| 0.4175 | 8.57 | 8000 | 0.5091 | 0.8053 | |
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| 0.4228 | 8.78 | 8200 | 0.5060 | 0.8098 | |
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| 0.4189 | 8.99 | 8400 | 0.4941 | 0.8092 | |
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| 0.4161 | 9.21 | 8600 | 0.5010 | 0.8174 | |
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| 0.4078 | 9.42 | 8800 | 0.4949 | 0.8079 | |
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| 0.4201 | 9.64 | 9000 | 0.5017 | 0.8073 | |
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| 0.4141 | 9.85 | 9200 | 0.4985 | 0.8092 | |
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| 0.4132 | 10.06 | 9400 | 0.5032 | 0.8053 | |
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| 0.4043 | 10.28 | 9600 | 0.5038 | 0.8130 | |
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| 0.4187 | 10.49 | 9800 | 0.4981 | 0.8104 | |
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| 0.3827 | 10.71 | 10000 | 0.5126 | 0.8073 | |
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| 0.4074 | 10.92 | 10200 | 0.5088 | 0.8073 | |
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| 0.4013 | 11.13 | 10400 | 0.5061 | 0.8073 | |
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| 0.3888 | 11.35 | 10600 | 0.5013 | 0.8085 | |
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| 0.3855 | 11.56 | 10800 | 0.4993 | 0.8060 | |
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| 0.3924 | 11.78 | 11000 | 0.5075 | 0.8085 | |
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| 0.4046 | 11.99 | 11200 | 0.4999 | 0.8028 | |
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| 0.3957 | 12.21 | 11400 | 0.5089 | 0.8034 | |
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| 0.381 | 12.42 | 11600 | 0.5208 | 0.8073 | |
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| 0.3906 | 12.63 | 11800 | 0.5137 | 0.8066 | |
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| 0.3734 | 12.85 | 12000 | 0.5183 | 0.8041 | |
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| 0.3928 | 13.06 | 12200 | 0.5069 | 0.8066 | |
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| 0.3774 | 13.28 | 12400 | 0.5086 | 0.8009 | |
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| 0.3892 | 13.49 | 12600 | 0.4967 | 0.8060 | |
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| 0.372 | 13.7 | 12800 | 0.5043 | 0.8041 | |
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| 0.388 | 13.92 | 13000 | 0.5095 | 0.8073 | |
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| 0.3754 | 14.13 | 13200 | 0.5104 | 0.8022 | |
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| 0.3639 | 14.35 | 13400 | 0.5263 | 0.7983 | |
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| 0.3795 | 14.56 | 13600 | 0.5146 | 0.8015 | |
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| 0.3792 | 14.78 | 13800 | 0.5066 | 0.8041 | |
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| 0.3589 | 14.99 | 14000 | 0.5136 | 0.8079 | |
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| 0.3624 | 15.2 | 14200 | 0.5237 | 0.8022 | |
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| 0.3659 | 15.42 | 14400 | 0.5166 | 0.8060 | |
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| 0.3657 | 15.63 | 14600 | 0.5178 | 0.8003 | |
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| 0.359 | 15.85 | 14800 | 0.5152 | 0.7983 | |
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| 0.3677 | 16.06 | 15000 | 0.5212 | 0.8034 | |
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| 0.3521 | 16.27 | 15200 | 0.5324 | 0.8003 | |
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| 0.3589 | 16.49 | 15400 | 0.5238 | 0.8041 | |
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| 0.3695 | 16.7 | 15600 | 0.5113 | 0.7977 | |
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| 0.3606 | 16.92 | 15800 | 0.5137 | 0.7983 | |
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| 0.3581 | 17.13 | 16000 | 0.5131 | 0.7996 | |
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| 0.3488 | 17.34 | 16200 | 0.5270 | 0.7990 | |
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| 0.3499 | 17.56 | 16400 | 0.5236 | 0.7964 | |
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| 0.3603 | 17.77 | 16600 | 0.5187 | 0.8003 | |
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| 0.3578 | 17.99 | 16800 | 0.5224 | 0.8022 | |
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| 0.3449 | 18.2 | 17000 | 0.5228 | 0.7990 | |
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| 0.3418 | 18.42 | 17200 | 0.5287 | 0.8009 | |
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| 0.3334 | 18.63 | 17400 | 0.5322 | 0.7996 | |
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| 0.3567 | 18.84 | 17600 | 0.5294 | 0.7983 | |
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| 0.3541 | 19.06 | 17800 | 0.5250 | 0.8003 | |
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| 0.365 | 19.27 | 18000 | 0.5246 | 0.7983 | |
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| 0.337 | 19.49 | 18200 | 0.5278 | 0.7977 | |
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| 0.3301 | 19.7 | 18400 | 0.5283 | 0.7990 | |
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| 0.3421 | 19.91 | 18600 | 0.5287 | 0.7977 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
