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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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- precision
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- recall
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- f1
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model-index:
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- name: bert-base-uncased-test
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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-base-uncased-test
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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: 3.3027
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- Accuracy: 0.725
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- Precision: 0.7153
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- Recall: 0.7372
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- F1: 0.7210
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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: 3e-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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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 8
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 0.0 | 1.0 | 30 | 3.2153 | 0.7333 | 0.7226 | 0.7478 | 0.7299 |
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| 0.0 | 2.0 | 60 | 3.2642 | 0.725 | 0.7153 | 0.7372 | 0.7210 |
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| 0.0 | 3.0 | 90 | 3.2930 | 0.725 | 0.7153 | 0.7372 | 0.7210 |
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| 0.0 | 4.0 | 120 | 3.3027 | 0.725 | 0.7153 | 0.7372 | 0.7210 |
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### Framework versions
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- Transformers 4.57.1
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- Pytorch 2.8.0+cu128
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- Datasets 4.2.0
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- Tokenizers 0.22.1
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