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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: 0.6468 |
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- Accuracy: 0.8582 |
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- F1: 0.8388 |
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- Precision: 0.8295 |
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- Recall: 0.8582 |
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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: 10 |
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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 | 1.2122 | 0.6005 | 0.4506 | 0.3606 | 0.6005 | |
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| No log | 2.0 | 196 | 0.9735 | 0.7113 | 0.6231 | 0.5549 | 0.7113 | |
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| No log | 3.0 | 294 | 0.7894 | 0.7655 | 0.6996 | 0.7399 | 0.7655 | |
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| No log | 4.0 | 392 | 0.9499 | 0.6933 | 0.6584 | 0.6617 | 0.6933 | |
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| No log | 5.0 | 490 | 0.7529 | 0.7784 | 0.7217 | 0.7107 | 0.7784 | |
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| 0.9006 | 6.0 | 588 | 0.7510 | 0.7964 | 0.7491 | 0.7370 | 0.7964 | |
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| 0.9006 | 7.0 | 686 | 0.5963 | 0.8273 | 0.8044 | 0.7960 | 0.8273 | |
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| 0.9006 | 8.0 | 784 | 0.6918 | 0.8351 | 0.8071 | 0.8096 | 0.8351 | |
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| 0.9006 | 9.0 | 882 | 0.7391 | 0.8273 | 0.8017 | 0.8042 | 0.8273 | |
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| 0.9006 | 10.0 | 980 | 0.6468 | 0.8582 | 0.8388 | 0.8295 | 0.8582 | |
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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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