| --- |
| license: mit |
| base_model: nielsr/lilt-xlm-roberta-base |
| tags: |
| - generated_from_trainer |
| datasets: |
| - xfun |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LiLT-SER-IT |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: xfun |
| type: xfun |
| config: xfun.it |
| split: validation |
| args: xfun.it |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.726186733731531 |
| - name: Recall |
| type: recall |
| value: 0.7927247769389156 |
| - name: F1 |
| type: f1 |
| value: 0.7579983593109106 |
| - name: Accuracy |
| type: accuracy |
| value: 0.768676917924818 |
| --- |
| |
| <!-- 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. --> |
|
|
| # LiLT-SER-IT |
|
|
| This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on the xfun dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.5355 |
| - Precision: 0.7262 |
| - Recall: 0.7927 |
| - F1: 0.7580 |
| - Accuracy: 0.7687 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - training_steps: 10000 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0696 | 7.46 | 500 | 1.0876 | 0.6322 | 0.6517 | 0.6418 | 0.7584 | |
| | 0.0576 | 14.93 | 1000 | 1.3989 | 0.6712 | 0.7601 | 0.7129 | 0.7601 | |
| | 0.0096 | 22.39 | 1500 | 1.8059 | 0.6774 | 0.7639 | 0.7181 | 0.7662 | |
| | 0.0092 | 29.85 | 2000 | 2.0416 | 0.7266 | 0.7334 | 0.7300 | 0.7652 | |
| | 0.0003 | 37.31 | 2500 | 2.0467 | 0.7166 | 0.7539 | 0.7348 | 0.7628 | |
| | 0.0013 | 44.78 | 3000 | 2.0159 | 0.7027 | 0.7821 | 0.7403 | 0.7638 | |
| | 0.0013 | 52.24 | 3500 | 2.2751 | 0.6961 | 0.7728 | 0.7325 | 0.7575 | |
| | 0.0002 | 59.7 | 4000 | 2.2084 | 0.7236 | 0.7563 | 0.7396 | 0.7723 | |
| | 0.0002 | 67.16 | 4500 | 2.1843 | 0.7048 | 0.7701 | 0.7360 | 0.7581 | |
| | 0.0001 | 74.63 | 5000 | 2.2483 | 0.7366 | 0.7745 | 0.7551 | 0.7770 | |
| | 0.0001 | 82.09 | 5500 | 2.2685 | 0.7171 | 0.7752 | 0.7451 | 0.7677 | |
| | 0.0005 | 89.55 | 6000 | 2.2877 | 0.7180 | 0.7821 | 0.7487 | 0.7692 | |
| | 0.0001 | 97.01 | 6500 | 2.2574 | 0.7308 | 0.7725 | 0.7511 | 0.7721 | |
| | 0.0 | 104.48 | 7000 | 2.4696 | 0.7255 | 0.7862 | 0.7546 | 0.7660 | |
| | 0.0 | 111.94 | 7500 | 2.3996 | 0.7140 | 0.7917 | 0.7509 | 0.7725 | |
| | 0.0 | 119.4 | 8000 | 2.4592 | 0.7261 | 0.7852 | 0.7545 | 0.7665 | |
| | 0.0 | 126.87 | 8500 | 2.4129 | 0.7336 | 0.7900 | 0.7607 | 0.7718 | |
| | 0.0 | 134.33 | 9000 | 2.5367 | 0.7316 | 0.7896 | 0.7595 | 0.7666 | |
| | 0.0 | 141.79 | 9500 | 2.5327 | 0.7278 | 0.7900 | 0.7576 | 0.7663 | |
| | 0.0 | 149.25 | 10000 | 2.5355 | 0.7262 | 0.7927 | 0.7580 | 0.7687 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.38.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.1 |
|
|
