| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: xlm-roberta-base |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - super_glue |
| | metrics: |
| | - accuracy |
| | - f1 |
| | model-index: |
| | - name: xlmroberta-multirc |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: super_glue |
| | type: super_glue |
| | config: multirc |
| | split: validation |
| | args: multirc |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.5719884488448845 |
| | - name: F1 |
| | type: f1 |
| | value: 0.4162508774824471 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # xlmroberta-multirc |
| |
|
| | This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the super_glue dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.6823 |
| | - Accuracy: 0.5720 |
| | - F1: 0.4163 |
| | |
| | ## 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: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 1 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
| | | 0.6873 | 1.0 | 1703 | 0.6823 | 0.5720 | 0.4163 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.46.2 |
| | - Pytorch 2.5.1+cu121 |
| | - Datasets 3.1.0 |
| | - Tokenizers 0.20.3 |
| |
|
