| --- |
| license: mit |
| base_model: roberta-large |
| tags: |
| - generated_from_trainer |
| datasets: |
| - generator |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: model |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: generator |
| type: generator |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.5931758530183727 |
| - name: Recall |
| type: recall |
| value: 0.7371167645140247 |
| - name: F1 |
| type: f1 |
| value: 0.6573589296102385 |
| - name: Accuracy |
| type: accuracy |
| value: 0.896675559203776 |
| --- |
| |
| <!-- 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. --> |
|
|
| # model |
|
|
| This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the generator dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5350 |
| - Precision: 0.5932 |
| - Recall: 0.7371 |
| - F1: 0.6574 |
| - Accuracy: 0.8967 |
|
|
| ## 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: 1e-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 |
| - training_steps: 1000 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 0.47 | 466 | 0.5513 | 0.5389 | 0.7358 | 0.6222 | 0.8787 | |
| | 0.4041 | 1.47 | 932 | 0.5179 | 0.5398 | 0.7613 | 0.6317 | 0.8797 | |
| | 0.3968 | 2.07 | 1000 | 0.5350 | 0.5932 | 0.7371 | 0.6574 | 0.8967 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.33.2 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.5 |
| - Tokenizers 0.13.3 |
|
|
