| ---
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| library_name: transformers
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| license: apache-2.0
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| base_model: albert-base-v2
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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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| - precision
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| - recall
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| model-index:
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| - name: albert-base-v2_fold_6
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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
|
| should probably proofread and complete it, then remove this comment. -->
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|
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| # albert-base-v2_fold_6
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| This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset.
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| It achieves the following results on the evaluation set:
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| - Loss: 0.1259
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| - Accuracy: 0.9585
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| - F1: 0.9544
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| - Precision: 0.9603
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| - Recall: 0.9486
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|
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| ## Model description
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| More information needed
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|
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| ## Intended uses & limitations
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| More information needed
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|
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| ## Training and evaluation data
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| More information needed
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|
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| ## Training procedure
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|
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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: 40
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| - eval_batch_size: 40
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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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| - mixed_precision_training: Native AMP
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|
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| ### Training results
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| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| | 0.1506 | 1.0 | 15481 | 0.1341 | 0.9487 | 0.9437 | 0.9487 | 0.9387 |
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| | 0.1085 | 2.0 | 30962 | 0.1173 | 0.9550 | 0.9502 | 0.9635 | 0.9372 |
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| | 0.0526 | 3.0 | 46443 | 0.1259 | 0.9585 | 0.9544 | 0.9603 | 0.9486 |
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| ### Framework versions
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| - Transformers 5.3.0
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| - Pytorch 2.10.0+cu128
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| - Datasets 4.6.1
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| - Tokenizers 0.22.2
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| |
