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---
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library_name: transformers
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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: newly_fine_tuned_bert_v2
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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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# newly_fine_tuned_bert_v2
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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: 0.0273
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- F1: 0.5517
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- Roc Auc: 0.6994
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- Accuracy: 0.4
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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: 1e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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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: 300
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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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| 0.0339 | 11.3636 | 500 | 0.0355 | 0.0 | 0.5 | 0.0 |
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| 0.028 | 22.7273 | 1000 | 0.0327 | 0.0 | 0.5 | 0.0 |
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| 0.0255 | 34.0909 | 1500 | 0.0327 | 0.0 | 0.5 | 0.0 |
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| 0.0234 | 45.4545 | 2000 | 0.0316 | 0.0 | 0.5 | 0.0 |
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| 0.0202 | 56.8182 | 2500 | 0.0309 | 0.0 | 0.5 | 0.0 |
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| 0.0174 | 68.1818 | 3000 | 0.0291 | 0.0 | 0.5 | 0.0 |
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| 0.0151 | 79.5455 | 3500 | 0.0281 | 0.0 | 0.5 | 0.0 |
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| 0.013 | 90.9091 | 4000 | 0.0274 | 0.0 | 0.5 | 0.0 |
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| 0.0109 | 102.2727 | 4500 | 0.0271 | 0.0 | 0.5 | 0.0 |
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| 0.0095 | 113.6364 | 5000 | 0.0267 | 0.0 | 0.5 | 0.0 |
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| 0.0081 | 125.0 | 5500 | 0.0262 | 0.0 | 0.5 | 0.0 |
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| 0.007 | 136.3636 | 6000 | 0.0262 | 0.0952 | 0.525 | 0.05 |
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| 0.0062 | 147.7273 | 6500 | 0.0267 | 0.4 | 0.625 | 0.25 |
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| 0.0053 | 159.0909 | 7000 | 0.0262 | 0.4 | 0.625 | 0.25 |
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| 0.0048 | 170.4545 | 7500 | 0.0266 | 0.4615 | 0.65 | 0.3 |
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| 0.0043 | 181.8182 | 8000 | 0.0259 | 0.5 | 0.6744 | 0.35 |
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| 0.0039 | 193.1818 | 8500 | 0.0264 | 0.5714 | 0.7 | 0.4 |
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| 0.0036 | 204.5455 | 9000 | 0.0268 | 0.5517 | 0.6994 | 0.4 |
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| 0.0032 | 215.9091 | 9500 | 0.0270 | 0.5517 | 0.6994 | 0.4 |
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| 0.003 | 227.2727 | 10000 | 0.0272 | 0.5517 | 0.6994 | 0.4 |
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| 0.0028 | 238.6364 | 10500 | 0.0269 | 0.5517 | 0.6994 | 0.4 |
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| 0.0027 | 250.0 | 11000 | 0.0267 | 0.5333 | 0.6988 | 0.4 |
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| 0.0026 | 261.3636 | 11500 | 0.0271 | 0.5333 | 0.6988 | 0.4 |
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| 0.0025 | 272.7273 | 12000 | 0.0272 | 0.5333 | 0.6988 | 0.4 |
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| 0.0025 | 284.0909 | 12500 | 0.0272 | 0.5517 | 0.6994 | 0.4 |
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| 0.0024 | 295.4545 | 13000 | 0.0273 | 0.5517 | 0.6994 | 0.4 |
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### Framework versions
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- Transformers 4.45.2
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- Pytorch 2.4.0+cu124
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- Datasets 3.0.1
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- Tokenizers 0.20.1
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