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README.md
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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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- accuracy
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- f1
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model-index:
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- name: bert-mrpc-analysis
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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-mrpc-analysis
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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: 0.6197
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- Accuracy: 0.8554
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- F1: 0.8985
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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: 16
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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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 |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
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| 0.6445 | 0.2174 | 50 | 0.5860 | 0.7010 | 0.8201 |
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| 0.5425 | 0.4348 | 100 | 0.5083 | 0.7647 | 0.8509 |
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| 0.4903 | 0.6522 | 150 | 0.4756 | 0.7868 | 0.8621 |
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| 0.459 | 0.8696 | 200 | 0.4043 | 0.8309 | 0.8832 |
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| 0.3848 | 1.0870 | 250 | 0.3972 | 0.8505 | 0.8968 |
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| 0.2851 | 1.3043 | 300 | 0.4763 | 0.8235 | 0.8808 |
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| 0.2758 | 1.5217 | 350 | 0.3576 | 0.8701 | 0.9065 |
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| 0.2241 | 1.7391 | 400 | 0.4367 | 0.8456 | 0.8919 |
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| 0.2699 | 1.9565 | 450 | 0.3583 | 0.8554 | 0.8948 |
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| 0.1345 | 2.1739 | 500 | 0.4947 | 0.8578 | 0.9 |
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| 0.092 | 2.3913 | 550 | 0.5921 | 0.8505 | 0.8968 |
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| 0.0825 | 2.6087 | 600 | 0.6000 | 0.8554 | 0.8985 |
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| 0.1194 | 2.8261 | 650 | 0.6197 | 0.8554 | 0.8985 |
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### Framework versions
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- Transformers 4.57.1
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- Pytorch 2.8.0+cu126
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- Datasets 4.0.0
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- Tokenizers 0.22.1
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