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--- |
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license: cc-by-4.0 |
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base_model: NbAiLab/nb-bert-base |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: nb-bert-base_FGN |
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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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# nb-bert-base_FGN |
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This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0904 |
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- F1-score: 0.8074 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1-score | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 120 | 0.6228 | 0.7307 | |
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| No log | 2.0 | 240 | 0.7442 | 0.7474 | |
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| No log | 3.0 | 360 | 0.7118 | 0.7785 | |
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| No log | 4.0 | 480 | 1.2081 | 0.7137 | |
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| 0.5388 | 5.0 | 600 | 1.1968 | 0.7628 | |
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| 0.5388 | 6.0 | 720 | 1.0904 | 0.8074 | |
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| 0.5388 | 7.0 | 840 | 1.2685 | 0.8007 | |
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| 0.5388 | 8.0 | 960 | 1.4070 | 0.7783 | |
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| 0.123 | 9.0 | 1080 | 1.6120 | 0.7608 | |
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| 0.123 | 10.0 | 1200 | 1.5899 | 0.7695 | |
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| 0.123 | 11.0 | 1320 | 1.4975 | 0.7705 | |
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| 0.123 | 12.0 | 1440 | 1.4624 | 0.7983 | |
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| 0.0475 | 13.0 | 1560 | 1.5148 | 0.7711 | |
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| 0.0475 | 14.0 | 1680 | 1.4680 | 0.7926 | |
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| 0.0475 | 15.0 | 1800 | 1.4216 | 0.8006 | |
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| 0.0475 | 16.0 | 1920 | 1.4962 | 0.8006 | |
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| 0.0201 | 17.0 | 2040 | 1.4150 | 0.7883 | |
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| 0.0201 | 18.0 | 2160 | 1.4259 | 0.7755 | |
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| 0.0201 | 19.0 | 2280 | 1.5040 | 0.7799 | |
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| 0.0201 | 20.0 | 2400 | 1.5045 | 0.7808 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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