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--- |
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license: apache-2.0 |
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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: bert-base-uncased |
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metrics: |
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- accuracy |
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model-index: |
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- name: emotions_bert_qlora |
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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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# emotions_bert_qlora |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5184 |
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- F1 Micro: 0.6855 |
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- F1 Macro: 0.6029 |
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- Accuracy: 0.2129 |
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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: 0.0001 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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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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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:--------:| |
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| 0.8094 | 0.4082 | 20 | 0.7752 | 0.4570 | 0.1865 | 0.0990 | |
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| 0.7206 | 0.8163 | 40 | 0.6446 | 0.5938 | 0.3669 | 0.1230 | |
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| 0.6381 | 1.2245 | 60 | 0.6094 | 0.6163 | 0.4260 | 0.1159 | |
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| 0.5836 | 1.6327 | 80 | 0.5757 | 0.6345 | 0.4702 | 0.1553 | |
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| 0.5631 | 2.0408 | 100 | 0.5504 | 0.6674 | 0.5290 | 0.1922 | |
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| 0.5132 | 2.4490 | 120 | 0.5355 | 0.6686 | 0.5440 | 0.1922 | |
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| 0.504 | 2.8571 | 140 | 0.5260 | 0.6710 | 0.5843 | 0.1780 | |
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| 0.4853 | 3.2653 | 160 | 0.5209 | 0.6689 | 0.5854 | 0.1858 | |
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| 0.4494 | 3.6735 | 180 | 0.5165 | 0.6759 | 0.5942 | 0.1909 | |
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| 0.4663 | 4.0816 | 200 | 0.5146 | 0.6834 | 0.5900 | 0.2 | |
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| 0.4361 | 4.4898 | 220 | 0.5073 | 0.6876 | 0.6013 | 0.2214 | |
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| 0.4278 | 4.8980 | 240 | 0.5112 | 0.6830 | 0.6018 | 0.2045 | |
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| 0.4175 | 5.3061 | 260 | 0.5119 | 0.6775 | 0.5929 | 0.2013 | |
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| 0.4 | 5.7143 | 280 | 0.5118 | 0.6846 | 0.5961 | 0.2039 | |
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| 0.4088 | 6.1224 | 300 | 0.5100 | 0.6819 | 0.6015 | 0.2117 | |
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| 0.3811 | 6.5306 | 320 | 0.5211 | 0.6759 | 0.5952 | 0.1916 | |
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| 0.389 | 6.9388 | 340 | 0.5152 | 0.6770 | 0.6004 | 0.1974 | |
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| 0.3806 | 7.3469 | 360 | 0.5183 | 0.6853 | 0.6029 | 0.2123 | |
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| 0.3686 | 7.7551 | 380 | 0.5222 | 0.6739 | 0.5940 | 0.2019 | |
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| 0.3719 | 8.1633 | 400 | 0.5207 | 0.6777 | 0.5941 | 0.2091 | |
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| 0.3538 | 8.5714 | 420 | 0.5242 | 0.6765 | 0.5943 | 0.2006 | |
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| 0.3591 | 8.9796 | 440 | 0.5247 | 0.6784 | 0.5953 | 0.2091 | |
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| 0.3554 | 9.3878 | 460 | 0.5259 | 0.6815 | 0.5978 | 0.2142 | |
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| 0.346 | 9.7959 | 480 | 0.5256 | 0.6809 | 0.5976 | 0.2142 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
