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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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- f1 |
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- accuracy |
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model-index: |
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- name: bert-finetuned-char-classification-e15 |
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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-finetuned-char-classification-e15 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1233 |
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- F1: 0.5572 |
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- Roc Auc: 0.7345 |
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- Accuracy: 0.3761 |
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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: 0.0002 |
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- train_batch_size: 512 |
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- eval_batch_size: 512 |
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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: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| 0.1881 | 1.2 | 500 | 0.1694 | 0.0104 | 0.5024 | 0.0007 | |
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| 0.1563 | 2.39 | 1000 | 0.1415 | 0.3037 | 0.5948 | 0.1221 | |
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| 0.1374 | 3.59 | 1500 | 0.1290 | 0.3997 | 0.6353 | 0.1958 | |
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| 0.127 | 4.78 | 2000 | 0.1231 | 0.4467 | 0.6579 | 0.2376 | |
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| 0.1194 | 5.98 | 2500 | 0.1188 | 0.4801 | 0.6748 | 0.2665 | |
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| 0.1114 | 7.18 | 3000 | 0.1183 | 0.5071 | 0.6944 | 0.3016 | |
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| 0.1048 | 8.37 | 3500 | 0.1177 | 0.5202 | 0.7032 | 0.3216 | |
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| 0.0983 | 9.57 | 4000 | 0.1172 | 0.5344 | 0.7123 | 0.3371 | |
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| 0.0917 | 10.77 | 4500 | 0.1164 | 0.5414 | 0.7175 | 0.3458 | |
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| 0.0852 | 11.96 | 5000 | 0.1175 | 0.5495 | 0.7240 | 0.3580 | |
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| 0.0787 | 13.16 | 5500 | 0.1226 | 0.5545 | 0.7328 | 0.3742 | |
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| 0.0738 | 14.35 | 6000 | 0.1233 | 0.5572 | 0.7345 | 0.3761 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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