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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-IT |
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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.it |
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split: validation |
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args: xfun.it |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.726186733731531 |
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- name: Recall |
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type: recall |
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value: 0.7927247769389156 |
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- name: F1 |
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type: f1 |
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value: 0.7579983593109106 |
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- name: Accuracy |
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type: accuracy |
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value: 0.768676917924818 |
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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-IT |
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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: 2.5355 |
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- Precision: 0.7262 |
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- Recall: 0.7927 |
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- F1: 0.7580 |
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- Accuracy: 0.7687 |
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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 | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0696 | 7.46 | 500 | 1.0876 | 0.6322 | 0.6517 | 0.6418 | 0.7584 | |
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| 0.0576 | 14.93 | 1000 | 1.3989 | 0.6712 | 0.7601 | 0.7129 | 0.7601 | |
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| 0.0096 | 22.39 | 1500 | 1.8059 | 0.6774 | 0.7639 | 0.7181 | 0.7662 | |
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| 0.0092 | 29.85 | 2000 | 2.0416 | 0.7266 | 0.7334 | 0.7300 | 0.7652 | |
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| 0.0003 | 37.31 | 2500 | 2.0467 | 0.7166 | 0.7539 | 0.7348 | 0.7628 | |
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| 0.0013 | 44.78 | 3000 | 2.0159 | 0.7027 | 0.7821 | 0.7403 | 0.7638 | |
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| 0.0013 | 52.24 | 3500 | 2.2751 | 0.6961 | 0.7728 | 0.7325 | 0.7575 | |
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| 0.0002 | 59.7 | 4000 | 2.2084 | 0.7236 | 0.7563 | 0.7396 | 0.7723 | |
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| 0.0002 | 67.16 | 4500 | 2.1843 | 0.7048 | 0.7701 | 0.7360 | 0.7581 | |
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| 0.0001 | 74.63 | 5000 | 2.2483 | 0.7366 | 0.7745 | 0.7551 | 0.7770 | |
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| 0.0001 | 82.09 | 5500 | 2.2685 | 0.7171 | 0.7752 | 0.7451 | 0.7677 | |
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| 0.0005 | 89.55 | 6000 | 2.2877 | 0.7180 | 0.7821 | 0.7487 | 0.7692 | |
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| 0.0001 | 97.01 | 6500 | 2.2574 | 0.7308 | 0.7725 | 0.7511 | 0.7721 | |
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| 0.0 | 104.48 | 7000 | 2.4696 | 0.7255 | 0.7862 | 0.7546 | 0.7660 | |
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| 0.0 | 111.94 | 7500 | 2.3996 | 0.7140 | 0.7917 | 0.7509 | 0.7725 | |
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| 0.0 | 119.4 | 8000 | 2.4592 | 0.7261 | 0.7852 | 0.7545 | 0.7665 | |
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| 0.0 | 126.87 | 8500 | 2.4129 | 0.7336 | 0.7900 | 0.7607 | 0.7718 | |
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| 0.0 | 134.33 | 9000 | 2.5367 | 0.7316 | 0.7896 | 0.7595 | 0.7666 | |
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| 0.0 | 141.79 | 9500 | 2.5327 | 0.7278 | 0.7900 | 0.7576 | 0.7663 | |
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| 0.0 | 149.25 | 10000 | 2.5355 | 0.7262 | 0.7927 | 0.7580 | 0.7687 | |
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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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