--- license: cc-by-4.0 base_model: NbAiLab/nb-bert-large tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: nb-bert-large-user-needs-v2 results: [] --- # nb-bert-large-user-needs-v2 This model is a fine-tuned version of [NbAiLab/nb-bert-large](https://huggingface.co/NbAiLab/nb-bert-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0173 - Accuracy: 0.8 - F1: 0.7945 - Precision: 0.7947 - Recall: 0.8 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 188 | 0.7673 | 0.696 | 0.6619 | 0.6566 | 0.696 | | No log | 2.0 | 376 | 0.5713 | 0.7707 | 0.7423 | 0.7163 | 0.7707 | | 0.6847 | 3.0 | 564 | 0.5849 | 0.7653 | 0.7547 | 0.7654 | 0.7653 | | 0.6847 | 4.0 | 752 | 0.7731 | 0.7467 | 0.7254 | 0.7474 | 0.7467 | | 0.6847 | 5.0 | 940 | 0.6056 | 0.7733 | 0.7740 | 0.7756 | 0.7733 | | 0.4443 | 6.0 | 1128 | 0.7752 | 0.792 | 0.7877 | 0.7901 | 0.792 | | 0.4443 | 7.0 | 1316 | 1.0173 | 0.8 | 0.7945 | 0.7947 | 0.8 | | 0.2807 | 8.0 | 1504 | 1.1683 | 0.7813 | 0.7789 | 0.7783 | 0.7813 | | 0.2807 | 9.0 | 1692 | 1.1886 | 0.7893 | 0.7825 | 0.7841 | 0.7893 | | 0.2807 | 10.0 | 1880 | 1.3052 | 0.776 | 0.7695 | 0.7729 | 0.776 | | 0.1282 | 11.0 | 2068 | 1.4641 | 0.784 | 0.7769 | 0.7804 | 0.784 | | 0.1282 | 12.0 | 2256 | 1.5614 | 0.7813 | 0.7716 | 0.7871 | 0.7813 | | 0.1282 | 13.0 | 2444 | 1.6424 | 0.784 | 0.7774 | 0.7804 | 0.784 | | 0.0529 | 14.0 | 2632 | 1.7250 | 0.7813 | 0.7767 | 0.7770 | 0.7813 | | 0.0529 | 15.0 | 2820 | 1.6061 | 0.8 | 0.7934 | 0.8058 | 0.8 | | 0.0182 | 16.0 | 3008 | 1.7678 | 0.792 | 0.7854 | 0.7908 | 0.792 | | 0.0182 | 17.0 | 3196 | 1.8226 | 0.7893 | 0.7834 | 0.7849 | 0.7893 | | 0.0182 | 18.0 | 3384 | 1.8330 | 0.7973 | 0.7906 | 0.7936 | 0.7973 | | 0.0061 | 19.0 | 3572 | 1.8423 | 0.7947 | 0.7879 | 0.7909 | 0.7947 | | 0.0061 | 20.0 | 3760 | 1.8536 | 0.7973 | 0.7906 | 0.7936 | 0.7973 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.0 - Datasets 2.18.0 - Tokenizers 0.15.2