| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - f1 |
| | base_model: xlm-roberta-base |
| | model-index: |
| | - name: DIPROMATS_subtask_1 |
| | 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. --> |
| |
|
| | # DIPROMATS_subtask_1 |
| |
|
| | This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0338 |
| | - F1: 0.9893 |
| |
|
| | ## 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: 64 |
| | - eval_batch_size: 64 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | | 0.2333 | 1.0 | 227 | 0.3143 | 0.8275 | |
| | | 0.2264 | 2.0 | 454 | 0.2628 | 0.8729 | |
| | | 0.2179 | 3.0 | 681 | 0.1320 | 0.9398 | |
| | | 0.1609 | 4.0 | 908 | 0.1025 | 0.9508 | |
| | | 0.1894 | 5.0 | 1135 | 0.0947 | 0.9640 | |
| | | 0.0291 | 6.0 | 1362 | 0.0581 | 0.9793 | |
| | | 0.0075 | 7.0 | 1589 | 0.0633 | 0.9785 | |
| | | 0.1243 | 8.0 | 1816 | 0.0372 | 0.9874 | |
| | | 0.0925 | 9.0 | 2043 | 0.0483 | 0.9851 | |
| | | 0.1582 | 10.0 | 2270 | 0.0338 | 0.9893 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.28.1 |
| | - Pytorch 1.13.1 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.13.3 |
| |
|
