| ---
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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_7
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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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|
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| # albert-base-v2_fold_7
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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: 0.1324
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| - Accuracy: 0.9609
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| - F1: 0.9569
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| - Precision: 0.9629
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| - Recall: 0.9509
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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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| | 0.1092 | 1.0 | 15481 | 0.1292 | 0.9513 | 0.9459 | 0.9573 | 0.9348 |
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| | 0.0959 | 2.0 | 30962 | 0.1110 | 0.9592 | 0.9553 | 0.9555 | 0.9552 |
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| | 0.0669 | 3.0 | 46443 | 0.1324 | 0.9609 | 0.9569 | 0.9629 | 0.9509 |
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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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| |
