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--- |
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base_model: NbAiLab/nb-bert-base |
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library_name: transformers |
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license: cc-by-4.0 |
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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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tags: |
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- generated_from_trainer |
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model-index: |
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- name: nbbert |
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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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# nbbert |
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This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.9305 |
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- Precision: 0.9342 |
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- Recall: 0.9305 |
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- F1: 0.9305 |
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- Loss: 0.4443 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Precision | Recall | F1 | Validation Loss | |
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|:-------------:|:-------:|:----:|:--------:|:---------:|:------:|:------:|:---------------:| |
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| No log | 0.9412 | 8 | 0.4361 | 0.6823 | 0.4361 | 0.3171 | 0.8924 | |
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| No log | 2.0 | 17 | 0.8851 | 0.8758 | 0.8851 | 0.8748 | 0.4652 | |
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| No log | 2.9412 | 25 | 0.8281 | 0.8333 | 0.8281 | 0.8204 | 0.5819 | |
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| No log | 4.0 | 34 | 0.8759 | 0.8922 | 0.8759 | 0.8749 | 0.4312 | |
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| No log | 4.9412 | 42 | 0.8550 | 0.8762 | 0.8550 | 0.8548 | 0.5312 | |
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| No log | 6.0 | 51 | 0.8944 | 0.8940 | 0.8944 | 0.8941 | 0.3318 | |
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| No log | 6.9412 | 59 | 0.9209 | 0.9255 | 0.9209 | 0.9210 | 0.3824 | |
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| No log | 8.0 | 68 | 0.9213 | 0.9282 | 0.9213 | 0.9219 | 0.4385 | |
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| No log | 8.9412 | 76 | 0.9205 | 0.9226 | 0.9205 | 0.9205 | 0.3830 | |
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| No log | 10.0 | 85 | 0.9249 | 0.9309 | 0.9249 | 0.9252 | 0.4137 | |
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| No log | 10.9412 | 93 | 0.9269 | 0.9310 | 0.9269 | 0.9270 | 0.4014 | |
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| No log | 12.0 | 102 | 0.9293 | 0.9321 | 0.9293 | 0.9293 | 0.3923 | |
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| No log | 12.9412 | 110 | 0.9277 | 0.9320 | 0.9277 | 0.9278 | 0.4565 | |
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| No log | 14.0 | 119 | 0.9305 | 0.9342 | 0.9305 | 0.9305 | 0.4166 | |
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| No log | 14.9412 | 127 | 0.9281 | 0.9325 | 0.9281 | 0.9282 | 0.4512 | |
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| No log | 16.0 | 136 | 0.9297 | 0.9336 | 0.9297 | 0.9298 | 0.4465 | |
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| No log | 16.9412 | 144 | 0.9273 | 0.9318 | 0.9273 | 0.9274 | 0.4624 | |
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| No log | 18.0 | 153 | 0.9277 | 0.9321 | 0.9277 | 0.9278 | 0.4593 | |
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| No log | 18.8235 | 160 | 0.9305 | 0.9342 | 0.9305 | 0.9305 | 0.4443 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.2 |
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- Tokenizers 0.19.1 |
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