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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: final-lr2e-5-bs16-fullprecision |
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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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# final-lr2e-5-bs16-fullprecision |
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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: 0.4633 |
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- F1 Macro: 0.8276 |
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- F1 Weighted: 0.8754 |
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- F1: 0.7348 |
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- Accuracy: 0.8775 |
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- Confusion Matrix: [[2831 199] |
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[ 291 679]] |
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- Confusion Matrix Norm: [[0.93432343 0.06567657] |
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[0.3 0.7 ]] |
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- Classification Report: precision recall f1-score support |
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0 0.906791 0.934323 0.920351 3030.0000 |
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1 0.773349 0.700000 0.734848 970.0000 |
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accuracy 0.877500 0.877500 0.877500 0.8775 |
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macro avg 0.840070 0.817162 0.827600 4000.0000 |
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weighted avg 0.874431 0.877500 0.875367 4000.0000 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 12345 |
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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: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 | Accuracy | Confusion Matrix | Confusion Matrix Norm | Classification Report | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------:|:--------:|:--------------------------:|:--------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 0.3362 | 1.0 | 1000 | 0.3034 | 0.8182 | 0.8693 | 0.7191 | 0.8722 | [[2835 195] |
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[ 316 654]] | [[0.93564356 0.06435644] |
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[0.3257732 0.6742268 ]] | precision recall f1-score support |
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0 0.899714 0.935644 0.917327 3030.00000 |
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1 0.770318 0.674227 0.719076 970.00000 |
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accuracy 0.872250 0.872250 0.872250 0.87225 |
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macro avg 0.835016 0.804935 0.818202 4000.00000 |
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weighted avg 0.868336 0.872250 0.869251 4000.00000 | |
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| 0.2352 | 2.0 | 2000 | 0.3730 | 0.8270 | 0.8730 | 0.7374 | 0.8732 | [[2781 249] |
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[ 258 712]] | [[0.91782178 0.08217822] |
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[0.26597938 0.73402062]] | precision recall f1-score support |
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0 0.915104 0.917822 0.916461 3030.00000 |
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1 0.740895 0.734021 0.737442 970.00000 |
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accuracy 0.873250 0.873250 0.873250 0.87325 |
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macro avg 0.827999 0.825921 0.826951 4000.00000 |
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weighted avg 0.872858 0.873250 0.873049 4000.00000 | |
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| 0.1566 | 3.0 | 3000 | 0.4633 | 0.8276 | 0.8754 | 0.7348 | 0.8775 | [[2831 199] |
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[ 291 679]] | [[0.93432343 0.06567657] |
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[0.3 0.7 ]] | precision recall f1-score support |
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0 0.906791 0.934323 0.920351 3030.0000 |
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1 0.773349 0.700000 0.734848 970.0000 |
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accuracy 0.877500 0.877500 0.877500 0.8775 |
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macro avg 0.840070 0.817162 0.827600 4000.0000 |
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weighted avg 0.874431 0.877500 0.875367 4000.0000 | |
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### Framework versions |
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- Transformers 4.27.0.dev0 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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