| --- |
| license: cc-by-sa-4.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - fin |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: fin5 |
| 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: 0.9243027888446215 |
| - name: Recall |
| type: recall |
| value: 0.9243027888446215 |
| - name: F1 |
| type: f1 |
| value: 0.9243027888446215 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9908666100254885 |
| --- |
| |
| <!-- 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. --> |
|
|
| # fin5 |
|
|
| This model is a fine-tuned version of [nlpaueb/sec-bert-shape](https://huggingface.co/nlpaueb/sec-bert-shape) on the fin dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0752 |
| - Precision: 0.9243 |
| - Recall: 0.9243 |
| - F1: 0.9243 |
| - Accuracy: 0.9909 |
|
|
| ## 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 | 0.0825 | 0.8327 | 0.8924 | 0.8615 | 0.9811 | |
| | No log | 2.0 | 258 | 0.0633 | 0.8593 | 0.9243 | 0.8906 | 0.9866 | |
| | No log | 3.0 | 387 | 0.0586 | 0.9038 | 0.9363 | 0.9198 | 0.9894 | |
| | 0.0547 | 4.0 | 516 | 0.0607 | 0.9357 | 0.9283 | 0.932 | 0.9911 | |
| | 0.0547 | 5.0 | 645 | 0.0656 | 0.9216 | 0.9363 | 0.9289 | 0.9904 | |
| | 0.0547 | 6.0 | 774 | 0.0692 | 0.9249 | 0.9323 | 0.9286 | 0.9909 | |
| | 0.0547 | 7.0 | 903 | 0.0716 | 0.9246 | 0.9283 | 0.9264 | 0.9904 | |
| | 0.0019 | 8.0 | 1032 | 0.0742 | 0.9213 | 0.9323 | 0.9267 | 0.9909 | |
| | 0.0019 | 9.0 | 1161 | 0.0748 | 0.9246 | 0.9283 | 0.9264 | 0.9909 | |
| | 0.0019 | 10.0 | 1290 | 0.0752 | 0.9243 | 0.9243 | 0.9243 | 0.9909 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.25.1 |
| - Pytorch 1.13.0+cu116 |
| - Datasets 2.7.1 |
| - Tokenizers 0.13.2 |
|
|
