| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - fin |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: fin6 |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: fin |
| type: fin |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.8237410071942446 |
| - name: Recall |
| type: recall |
| value: 0.9123505976095617 |
| - name: F1 |
| type: f1 |
| value: 0.8657844990548205 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9836742353161944 |
| --- |
| |
| <!-- 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. --> |
|
|
| # fin6 |
|
|
| This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the fin dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0732 |
| - Precision: 0.8237 |
| - Recall: 0.9124 |
| - F1: 0.8658 |
| - Accuracy: 0.9837 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 8 |
| - 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 | 129 | 0.0922 | 0.6559 | 0.8127 | 0.7260 | 0.9739 | |
| | No log | 2.0 | 258 | 0.0471 | 0.8889 | 0.9243 | 0.9062 | 0.9910 | |
| | No log | 3.0 | 387 | 0.0620 | 0.8419 | 0.9124 | 0.8757 | 0.9825 | |
| | 0.0622 | 4.0 | 516 | 0.0651 | 0.8156 | 0.9163 | 0.8630 | 0.9805 | |
| | 0.0622 | 5.0 | 645 | 0.0508 | 0.8614 | 0.9163 | 0.8880 | 0.9872 | |
| | 0.0622 | 6.0 | 774 | 0.0467 | 0.8988 | 0.9203 | 0.9094 | 0.9916 | |
| | 0.0622 | 7.0 | 903 | 0.0713 | 0.8099 | 0.9163 | 0.8598 | 0.9822 | |
| | 0.0052 | 8.0 | 1032 | 0.0767 | 0.8214 | 0.9163 | 0.8663 | 0.9824 | |
| | 0.0052 | 9.0 | 1161 | 0.0739 | 0.8179 | 0.9124 | 0.8625 | 0.9831 | |
| | 0.0052 | 10.0 | 1290 | 0.0732 | 0.8237 | 0.9124 | 0.8658 | 0.9837 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.25.1 |
| - Pytorch 1.13.0+cu116 |
| - Datasets 2.7.1 |
| - Tokenizers 0.13.2 |
|
|
