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--- |
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license: cc-by-4.0 |
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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: nb-bert-base-user-needs |
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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-user-needs |
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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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- Loss: 1.0600 |
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- Accuracy: 0.8479 |
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- F1: 0.8319 |
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- Precision: 0.8315 |
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- Recall: 0.8479 |
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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: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 64 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 25 |
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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 | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 98 | 1.1222 | 0.6263 | 0.5185 | 0.5076 | 0.6263 | |
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| No log | 2.0 | 196 | 1.0066 | 0.7216 | 0.6436 | 0.5899 | 0.7216 | |
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| No log | 3.0 | 294 | 0.8540 | 0.7577 | 0.7037 | 0.6760 | 0.7577 | |
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| No log | 4.0 | 392 | 0.8621 | 0.7603 | 0.6998 | 0.6568 | 0.7603 | |
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| No log | 5.0 | 490 | 0.8062 | 0.7887 | 0.7500 | 0.7449 | 0.7887 | |
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| 0.91 | 6.0 | 588 | 0.7465 | 0.8041 | 0.7660 | 0.7636 | 0.8041 | |
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| 0.91 | 7.0 | 686 | 0.6324 | 0.8247 | 0.8163 | 0.8187 | 0.8247 | |
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| 0.91 | 8.0 | 784 | 0.7333 | 0.7964 | 0.7703 | 0.7740 | 0.7964 | |
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| 0.91 | 9.0 | 882 | 0.6590 | 0.8325 | 0.8208 | 0.8106 | 0.8325 | |
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| 0.91 | 10.0 | 980 | 0.9854 | 0.8196 | 0.7890 | 0.7920 | 0.8196 | |
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| 0.4246 | 11.0 | 1078 | 0.7023 | 0.8247 | 0.8054 | 0.8138 | 0.8247 | |
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| 0.4246 | 12.0 | 1176 | 0.8995 | 0.8325 | 0.8120 | 0.8068 | 0.8325 | |
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| 0.4246 | 13.0 | 1274 | 0.8589 | 0.8299 | 0.8145 | 0.8058 | 0.8299 | |
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| 0.4246 | 14.0 | 1372 | 0.9859 | 0.8376 | 0.8151 | 0.8123 | 0.8376 | |
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| 0.4246 | 15.0 | 1470 | 0.8452 | 0.8402 | 0.8318 | 0.8341 | 0.8402 | |
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| 0.1637 | 16.0 | 1568 | 1.1156 | 0.8351 | 0.8157 | 0.8196 | 0.8351 | |
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| 0.1637 | 17.0 | 1666 | 1.1514 | 0.8325 | 0.8122 | 0.8218 | 0.8325 | |
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| 0.1637 | 18.0 | 1764 | 1.0092 | 0.8428 | 0.8266 | 0.8320 | 0.8428 | |
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| 0.1637 | 19.0 | 1862 | 1.0368 | 0.8351 | 0.8229 | 0.8287 | 0.8351 | |
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| 0.1637 | 20.0 | 1960 | 1.0600 | 0.8479 | 0.8319 | 0.8315 | 0.8479 | |
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| 0.0391 | 21.0 | 2058 | 1.1046 | 0.8428 | 0.8293 | 0.8269 | 0.8428 | |
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| 0.0391 | 22.0 | 2156 | 1.1178 | 0.8454 | 0.8262 | 0.8280 | 0.8454 | |
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| 0.0391 | 23.0 | 2254 | 1.1103 | 0.8428 | 0.8268 | 0.8295 | 0.8428 | |
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| 0.0391 | 24.0 | 2352 | 1.1179 | 0.8428 | 0.8274 | 0.8313 | 0.8428 | |
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| 0.0391 | 25.0 | 2450 | 1.1134 | 0.8402 | 0.8233 | 0.8254 | 0.8402 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.2+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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