--- library_name: transformers license: mit base_model: microsoft/mdeberta-v3-base tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: mdeberta-ner-ghtk-hirach_NER-first_1000_data-3090-15Nov results: [] --- # mdeberta-ner-ghtk-hirach_NER-first_1000_data-3090-15Nov This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0975 - Accuracy: 0.9820 - F1: 0.4359 - Precision: 0.4857 - Recall: 0.3953 ## 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: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 250 | 0.0903 | 0.9825 | 0.0 | 0.0 | 0.0 | | 0.1391 | 2.0 | 500 | 0.0941 | 0.9825 | 0.0 | 0.0 | 0.0 | | 0.1391 | 3.0 | 750 | 0.0933 | 0.9825 | 0.0 | 0.0 | 0.0 | | 0.075 | 4.0 | 1000 | 0.0924 | 0.9825 | 0.0 | 0.0 | 0.0 | | 0.075 | 5.0 | 1250 | 0.0894 | 0.9825 | 0.0 | 0.0 | 0.0 | | 0.0634 | 6.0 | 1500 | 0.0870 | 0.9825 | 0.0851 | 0.5 | 0.0465 | | 0.0634 | 7.0 | 1750 | 0.0846 | 0.9820 | 0.0833 | 0.4 | 0.0465 | | 0.0508 | 8.0 | 2000 | 0.0799 | 0.9825 | 0.1224 | 0.5 | 0.0698 | | 0.0508 | 9.0 | 2250 | 0.0794 | 0.9829 | 0.125 | 0.6 | 0.0698 | | 0.0394 | 10.0 | 2500 | 0.0793 | 0.9800 | 0.0755 | 0.2 | 0.0465 | | 0.0394 | 11.0 | 2750 | 0.0801 | 0.9808 | 0.2034 | 0.375 | 0.1395 | | 0.0302 | 12.0 | 3000 | 0.0825 | 0.9812 | 0.2069 | 0.4 | 0.1395 | | 0.0302 | 13.0 | 3250 | 0.0763 | 0.9829 | 0.2759 | 0.5333 | 0.1860 | | 0.0232 | 14.0 | 3500 | 0.0755 | 0.9833 | 0.3692 | 0.5455 | 0.2791 | | 0.0232 | 15.0 | 3750 | 0.0799 | 0.9829 | 0.3226 | 0.5263 | 0.2326 | | 0.0176 | 16.0 | 4000 | 0.0785 | 0.9833 | 0.3692 | 0.5455 | 0.2791 | | 0.0176 | 17.0 | 4250 | 0.0776 | 0.9825 | 0.3768 | 0.5 | 0.3023 | | 0.0132 | 18.0 | 4500 | 0.0803 | 0.9833 | 0.3881 | 0.5417 | 0.3023 | | 0.0132 | 19.0 | 4750 | 0.0826 | 0.9812 | 0.3611 | 0.4483 | 0.3023 | | 0.0106 | 20.0 | 5000 | 0.0787 | 0.9825 | 0.4110 | 0.5 | 0.3488 | | 0.0106 | 21.0 | 5250 | 0.0879 | 0.9816 | 0.3478 | 0.4615 | 0.2791 | | 0.0085 | 22.0 | 5500 | 0.0848 | 0.9816 | 0.4156 | 0.4706 | 0.3721 | | 0.0085 | 23.0 | 5750 | 0.0818 | 0.9825 | 0.4267 | 0.5 | 0.3721 | | 0.0068 | 24.0 | 6000 | 0.0816 | 0.9833 | 0.4533 | 0.5312 | 0.3953 | | 0.0068 | 25.0 | 6250 | 0.0819 | 0.9825 | 0.4267 | 0.5 | 0.3721 | | 0.0056 | 26.0 | 6500 | 0.0848 | 0.9833 | 0.4533 | 0.5312 | 0.3953 | | 0.0056 | 27.0 | 6750 | 0.0872 | 0.9833 | 0.4533 | 0.5312 | 0.3953 | | 0.0049 | 28.0 | 7000 | 0.0844 | 0.9837 | 0.4595 | 0.5484 | 0.3953 | | 0.0049 | 29.0 | 7250 | 0.0881 | 0.9820 | 0.4211 | 0.4848 | 0.3721 | | 0.0042 | 30.0 | 7500 | 0.0925 | 0.9820 | 0.45 | 0.4865 | 0.4186 | | 0.0042 | 31.0 | 7750 | 0.0924 | 0.9825 | 0.4267 | 0.5 | 0.3721 | | 0.0038 | 32.0 | 8000 | 0.0938 | 0.9833 | 0.4675 | 0.5294 | 0.4186 | | 0.0038 | 33.0 | 8250 | 0.0939 | 0.9825 | 0.4416 | 0.5 | 0.3953 | | 0.0032 | 34.0 | 8500 | 0.0941 | 0.9833 | 0.4384 | 0.5333 | 0.3721 | | 0.0032 | 35.0 | 8750 | 0.0942 | 0.9833 | 0.4675 | 0.5294 | 0.4186 | | 0.0029 | 36.0 | 9000 | 0.0949 | 0.9820 | 0.4359 | 0.4857 | 0.3953 | | 0.0029 | 37.0 | 9250 | 0.0961 | 0.9820 | 0.4359 | 0.4857 | 0.3953 | | 0.0027 | 38.0 | 9500 | 0.0980 | 0.9820 | 0.4359 | 0.4857 | 0.3953 | | 0.0027 | 39.0 | 9750 | 0.0972 | 0.9820 | 0.4359 | 0.4857 | 0.3953 | | 0.0026 | 40.0 | 10000 | 0.0975 | 0.9820 | 0.4359 | 0.4857 | 0.3953 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1