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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: PKOBP/polish-roberta-8k |
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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: mwik-classifier-ext |
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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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# mwik-classifier-ext |
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This model is a fine-tuned version of [PKOBP/polish-roberta-8k](https://huggingface.co/PKOBP/polish-roberta-8k) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1140 |
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- Accuracy: 0.7371 |
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- Precision: 0.7338 |
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- Recall: 0.7371 |
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- F1: 0.7276 |
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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: 0.0001 |
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- train_batch_size: 24 |
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- eval_batch_size: 48 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 96 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: polynomial |
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- lr_scheduler_warmup_ratio: 0.06 |
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- num_epochs: 7 |
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- mixed_precision_training: Native AMP |
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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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| 3.0764 | 1.0 | 64 | 1.6987 | 0.6210 | 0.5278 | 0.6210 | 0.5529 | |
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| 1.6258 | 2.0 | 128 | 1.3065 | 0.6845 | 0.6331 | 0.6845 | 0.6401 | |
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| 1.2496 | 3.0 | 192 | 1.1332 | 0.7113 | 0.6755 | 0.7113 | 0.6801 | |
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| 0.7597 | 4.0 | 256 | 1.0614 | 0.7435 | 0.7298 | 0.7435 | 0.7219 | |
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| 0.6033 | 5.0 | 320 | 1.0464 | 0.7565 | 0.7427 | 0.7565 | 0.7423 | |
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| 0.457 | 6.0 | 384 | 1.0559 | 0.7496 | 0.7399 | 0.7496 | 0.7383 | |
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| 0.3654 | 7.0 | 448 | 1.0642 | 0.7519 | 0.7393 | 0.7519 | 0.7395 | |
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### Framework versions |
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- Transformers 4.57.3 |
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- Pytorch 2.9.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.1 |
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