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--- |
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license: mit |
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base_model: nielsr/lilt-xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- xfun |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: LiLT-SER-ES |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: xfun |
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type: xfun |
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config: xfun.es |
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split: validation |
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args: xfun.es |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6718889883616831 |
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- name: Recall |
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type: recall |
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value: 0.6733961417676088 |
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- name: F1 |
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type: f1 |
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value: 0.6726417208155948 |
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- name: Accuracy |
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type: accuracy |
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value: 0.7462640815388152 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# LiLT-SER-ES |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5588 |
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- Precision: 0.6719 |
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- Recall: 0.6734 |
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- F1: 0.6726 |
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- Accuracy: 0.7463 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall | |
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|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:| |
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| 0.2279 | 8.2 | 500 | 0.6790 | 0.5205 | 1.2508 | 0.4589 | 0.6012 | |
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| 0.032 | 16.39 | 1000 | 0.6936 | 0.5885 | 1.9637 | 0.6321 | 0.5505 | |
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| 0.0073 | 24.59 | 1500 | 0.7351 | 0.6175 | 1.6711 | 0.5795 | 0.6608 | |
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| 0.0479 | 32.79 | 2000 | 0.7405 | 0.6422 | 1.8259 | 0.6265 | 0.6586 | |
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| 0.0666 | 40.98 | 2500 | 0.7424 | 0.6349 | 1.8343 | 0.5937 | 0.6824 | |
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| 0.0006 | 49.18 | 3000 | 0.7475 | 0.6536 | 2.0575 | 0.6512 | 0.6559 | |
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| 0.0084 | 57.38 | 3500 | 0.7138 | 0.6415 | 2.4488 | 0.6758 | 0.6106 | |
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| 0.0002 | 65.57 | 4000 | 0.7571 | 0.6468 | 1.9641 | 0.6406 | 0.6532 | |
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| 0.0005 | 73.77 | 4500 | 2.2976 | 0.6699 | 0.6429 | 0.6561 | 0.7413 | |
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| 0.0003 | 81.97 | 5000 | 2.1562 | 0.6287 | 0.6653 | 0.6465 | 0.7468 | |
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| 0.0007 | 90.16 | 5500 | 2.2806 | 0.6435 | 0.6689 | 0.6560 | 0.7435 | |
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| 0.0002 | 98.36 | 6000 | 2.0508 | 0.6294 | 0.6734 | 0.6506 | 0.7538 | |
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| 0.0 | 106.56 | 6500 | 2.2626 | 0.6602 | 0.6765 | 0.6683 | 0.7498 | |
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| 0.0 | 114.75 | 7000 | 2.3467 | 0.6687 | 0.6492 | 0.6588 | 0.7409 | |
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| 0.0 | 122.95 | 7500 | 2.4430 | 0.6773 | 0.6734 | 0.6754 | 0.7447 | |
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| 0.0 | 131.15 | 8000 | 2.3653 | 0.6643 | 0.6765 | 0.6704 | 0.7476 | |
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| 0.0 | 139.34 | 8500 | 2.2903 | 0.6567 | 0.6824 | 0.6693 | 0.7498 | |
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| 0.0 | 147.54 | 9000 | 2.4458 | 0.6536 | 0.6824 | 0.6677 | 0.7440 | |
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| 0.0 | 155.74 | 9500 | 2.5953 | 0.6703 | 0.6685 | 0.6694 | 0.7423 | |
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| 0.0 | 163.93 | 10000 | 2.5588 | 0.6719 | 0.6734 | 0.6726 | 0.7463 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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