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--- |
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library_name: transformers |
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license: cc-by-nc-sa-4.0 |
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base_model: ufal/robeczech-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- generator |
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metrics: |
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- accuracy |
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model-index: |
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- name: Robeczech-CERED2 |
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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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# Robeczech-CERED2 |
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This model is a fine-tuned version of [ufal/robeczech-base](https://huggingface.co/ufal/robeczech-base) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1300 |
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- Accuracy: 0.8985 |
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- Micro Precision: 0.8985 |
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- Micro Recall: 0.8985 |
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- Micro F1: 0.8985 |
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- Macro Precision: 0.8711 |
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- Macro Recall: 0.8608 |
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- Macro F1: 0.8632 |
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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: 1e-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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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Use 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: cosine |
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- lr_scheduler_warmup_steps: 1500 |
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- num_epochs: 10 |
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- label_smoothing_factor: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 | |
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|:-------------:|:------:|:------:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:| |
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| 1.1585 | 1.0000 | 11305 | 1.1208 | 0.8608 | 0.8608 | 0.8608 | 0.8608 | 0.8155 | 0.7878 | 0.7914 | |
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| 1.0617 | 2.0 | 22611 | 1.0567 | 0.8873 | 0.8873 | 0.8873 | 0.8873 | 0.8547 | 0.8428 | 0.8430 | |
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| 0.9804 | 3.0000 | 33916 | 1.0558 | 0.8900 | 0.8900 | 0.8900 | 0.8900 | 0.8546 | 0.8414 | 0.8438 | |
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| 0.9327 | 4.0 | 45222 | 1.0585 | 0.8920 | 0.8920 | 0.8920 | 0.8920 | 0.8557 | 0.8475 | 0.8483 | |
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| 0.8927 | 5.0000 | 56527 | 1.0820 | 0.8917 | 0.8917 | 0.8917 | 0.8917 | 0.8484 | 0.8499 | 0.8455 | |
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| 0.861 | 6.0 | 67833 | 1.0774 | 0.8982 | 0.8982 | 0.8982 | 0.8982 | 0.8596 | 0.8567 | 0.8545 | |
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| 0.8344 | 7.0000 | 79138 | 1.0987 | 0.8979 | 0.8979 | 0.8979 | 0.8979 | 0.8641 | 0.8558 | 0.8567 | |
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| 0.8222 | 8.0 | 90444 | 1.1113 | 0.8991 | 0.8991 | 0.8991 | 0.8991 | 0.8639 | 0.8544 | 0.8558 | |
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| 0.8096 | 9.0000 | 101749 | 1.1159 | 0.9001 | 0.9001 | 0.9001 | 0.9001 | 0.8584 | 0.8589 | 0.8552 | |
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| 0.8071 | 9.9996 | 113050 | 1.1176 | 0.8994 | 0.8994 | 0.8994 | 0.8994 | 0.8561 | 0.8577 | 0.8539 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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