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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: BERT_Text_classification_noisy |
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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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# BERT_Text_classification_noisy |
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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: 0.4823 |
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- Accuracy: 0.8880 |
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- F1: 0.8776 |
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- Precision: 0.8835 |
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- Recall: 0.8779 |
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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: 16 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 1.7338 | 0.24 | 50 | 1.4138 | 0.7076 | 0.6300 | 0.5892 | 0.6828 | |
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| 0.9336 | 0.48 | 100 | 0.5276 | 0.8361 | 0.8305 | 0.8407 | 0.8303 | |
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| 0.5885 | 0.71 | 150 | 0.4815 | 0.8603 | 0.8541 | 0.8583 | 0.8525 | |
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| 0.6172 | 0.95 | 200 | 0.5176 | 0.8777 | 0.8648 | 0.8712 | 0.8664 | |
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| 0.5229 | 1.19 | 250 | 0.4818 | 0.8809 | 0.8709 | 0.8769 | 0.8707 | |
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| 0.4757 | 1.43 | 300 | 0.4845 | 0.8827 | 0.8720 | 0.8786 | 0.8722 | |
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| 0.4286 | 1.67 | 350 | 0.4231 | 0.8854 | 0.8759 | 0.8776 | 0.8758 | |
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| 0.4837 | 1.9 | 400 | 0.4763 | 0.8907 | 0.8794 | 0.8864 | 0.8799 | |
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| 0.4031 | 2.14 | 450 | 0.4539 | 0.8880 | 0.8766 | 0.8833 | 0.8773 | |
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| 0.4305 | 2.38 | 500 | 0.4775 | 0.8858 | 0.8752 | 0.8806 | 0.8755 | |
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| 0.3538 | 2.62 | 550 | 0.4863 | 0.8880 | 0.8771 | 0.8853 | 0.8776 | |
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| 0.325 | 2.86 | 600 | 0.4823 | 0.8880 | 0.8776 | 0.8835 | 0.8779 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.2 |
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