| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: ro-sequence |
| 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. --> |
|
|
| # ro-sequence |
|
|
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 815.9874 |
| - Precision: 0.7802 |
| - Recall: 0.8225 |
| - F1: 0.8008 |
|
|
| ## 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: 4e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 64 |
| - seed: 352269 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 30 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| |
| | 798.342 | 1.0 | 125 | 619.5866 | 0.7472 | 0.7369 | 0.7420 | |
| | 377.8265 | 2.0 | 250 | 521.1552 | 0.7833 | 0.7998 | 0.7915 | |
| | 288.2568 | 3.0 | 375 | 559.4092 | 0.7469 | 0.8145 | 0.7792 | |
| | 192.2052 | 4.0 | 500 | 555.9223 | 0.8252 | 0.7889 | 0.8066 | |
| | 128.4364 | 5.0 | 625 | 719.3274 | 0.7848 | 0.8042 | 0.7944 | |
| | 86.742 | 6.0 | 750 | 797.8254 | 0.7391 | 0.8281 | 0.7811 | |
| | 62.8087 | 7.0 | 875 | 815.9874 | 0.7802 | 0.8225 | 0.8008 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.34.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.5 |
| - Tokenizers 0.14.1 |
| |
