| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: FacebookAI/xlm-roberta-base |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: test_linsearch_only_abstract |
| | 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. --> |
| |
|
| | # test_linsearch_only_abstract |
| | |
| | This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.2716 |
| | - Accuracy: 0.6508 |
| | - F1 Macro: 0.5942 |
| | - Precision Macro: 0.6170 |
| | - Recall Macro: 0.5858 |
| | |
| | ## 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: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - optimizer: Use OptimizerNames.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_steps: 500 |
| | - num_epochs: 10 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | |
| | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:| |
| | | 1.2232 | 1.0 | 2466 | 1.1574 | 0.6405 | 0.5484 | 0.5538 | 0.5608 | |
| | | 1.0386 | 2.0 | 4932 | 1.0934 | 0.6497 | 0.5631 | 0.5712 | 0.5642 | |
| | | 0.9215 | 3.0 | 7398 | 1.0725 | 0.6634 | 0.5933 | 0.5950 | 0.5970 | |
| | | 0.8026 | 4.0 | 9864 | 1.0994 | 0.6532 | 0.5817 | 0.5905 | 0.5796 | |
| | | 0.6754 | 5.0 | 12330 | 1.1462 | 0.6558 | 0.5838 | 0.5934 | 0.5806 | |
| | | 0.5958 | 6.0 | 14796 | 1.2077 | 0.6537 | 0.5857 | 0.5963 | 0.5813 | |
| | | 0.4924 | 7.0 | 17262 | 1.2716 | 0.6508 | 0.5942 | 0.6170 | 0.5858 | |
| | | 0.4165 | 8.0 | 19728 | 1.3450 | 0.6450 | 0.5938 | 0.6037 | 0.5923 | |
| | | 0.3599 | 9.0 | 22194 | 1.4048 | 0.6412 | 0.5906 | 0.6077 | 0.5812 | |
| | | 0.3262 | 10.0 | 24660 | 1.4422 | 0.6389 | 0.5941 | 0.6032 | 0.5894 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.50.1 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 3.4.1 |
| | - Tokenizers 0.21.1 |
| |
|
