| --- |
| license: apache-2.0 |
| base_model: bert-large-cased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: bert-large-cased_ner |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # bert-large-cased_ner |
| |
| This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6758 |
| - Precision: 0.8709 |
| - Recall: 0.8781 |
| - F1: 0.8737 |
| - Accuracy: 0.9135 |
| |
| ## 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: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 438 | 0.3035 | 0.8701 | 0.8816 | 0.8748 | 0.9096 | |
| | 0.4531 | 2.0 | 876 | 0.3008 | 0.8820 | 0.8839 | 0.8819 | 0.9197 | |
| | 0.2183 | 3.0 | 1314 | 0.4003 | 0.8706 | 0.8759 | 0.8715 | 0.9119 | |
| | 0.1254 | 4.0 | 1752 | 0.3581 | 0.8843 | 0.8912 | 0.8870 | 0.9219 | |
| | 0.0704 | 5.0 | 2190 | 0.4627 | 0.8668 | 0.8683 | 0.8669 | 0.9092 | |
| | 0.0408 | 6.0 | 2628 | 0.5183 | 0.8703 | 0.8783 | 0.8737 | 0.9144 | |
| | 0.0264 | 7.0 | 3066 | 0.6201 | 0.8705 | 0.8784 | 0.8738 | 0.9122 | |
| | 0.0092 | 8.0 | 3504 | 0.6004 | 0.8673 | 0.8766 | 0.8712 | 0.9113 | |
| | 0.0092 | 9.0 | 3942 | 0.6578 | 0.8716 | 0.8782 | 0.8744 | 0.9133 | |
| | 0.004 | 10.0 | 4380 | 0.6758 | 0.8709 | 0.8781 | 0.8737 | 0.9135 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.42.4 |
| - Pytorch 2.3.1+cu121 |
| - Datasets 2.20.0 |
| - Tokenizers 0.19.1 |
| |
