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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-PT |
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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.pt |
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split: validation |
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args: xfun.pt |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6997755331088664 |
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- name: Recall |
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type: recall |
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value: 0.7550711474417197 |
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- name: F1 |
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type: f1 |
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value: 0.72637250618902 |
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- name: Accuracy |
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type: accuracy |
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value: 0.7709534665415047 |
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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-PT |
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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: 2.1403 |
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- Precision: 0.6998 |
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- Recall: 0.7551 |
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- F1: 0.7264 |
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- Accuracy: 0.7710 |
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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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| 0.0838 | 8.47 | 500 | 0.7697 | 0.6542 | 1.0006 | 0.6081 | 0.7078 | |
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| 0.0366 | 16.95 | 1000 | 0.7606 | 0.6795 | 1.4063 | 0.6533 | 0.7078 | |
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| 0.0173 | 25.42 | 1500 | 0.7848 | 0.7047 | 1.4681 | 0.6752 | 0.7369 | |
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| 0.0036 | 33.9 | 2000 | 0.7706 | 0.7003 | 1.6267 | 0.6577 | 0.7487 | |
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| 0.0023 | 42.37 | 2500 | 1.6728 | 0.6839 | 0.7172 | 0.7002 | 0.7698 | |
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| 0.0001 | 50.85 | 3000 | 1.6210 | 0.6742 | 0.7493 | 0.7098 | 0.7941 | |
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| 0.0001 | 59.32 | 3500 | 1.6883 | 0.6962 | 0.7505 | 0.7223 | 0.7929 | |
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| 0.0007 | 67.8 | 4000 | 1.8709 | 0.6730 | 0.7590 | 0.7134 | 0.7811 | |
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| 0.0003 | 76.27 | 4500 | 1.9387 | 0.6884 | 0.7151 | 0.7015 | 0.7690 | |
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| 0.0034 | 84.75 | 5000 | 1.8042 | 0.6927 | 0.7554 | 0.7227 | 0.7787 | |
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| 0.0 | 93.22 | 5500 | 2.0395 | 0.6954 | 0.7596 | 0.7261 | 0.7527 | |
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| 0.0003 | 101.69 | 6000 | 1.9295 | 0.6861 | 0.7511 | 0.7172 | 0.7790 | |
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| 0.0001 | 110.17 | 6500 | 1.9690 | 0.6813 | 0.7611 | 0.7190 | 0.7694 | |
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| 0.0 | 118.64 | 7000 | 1.9217 | 0.6974 | 0.7520 | 0.7237 | 0.7754 | |
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| 0.0001 | 127.12 | 7500 | 2.0703 | 0.6885 | 0.7536 | 0.7196 | 0.7694 | |
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| 0.0002 | 135.59 | 8000 | 2.0438 | 0.6915 | 0.7635 | 0.7258 | 0.7770 | |
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| 0.0 | 144.07 | 8500 | 2.0429 | 0.6980 | 0.7599 | 0.7276 | 0.7782 | |
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| 0.0 | 152.54 | 9000 | 2.1403 | 0.6998 | 0.7551 | 0.7264 | 0.7710 | |
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| 0.0 | 161.02 | 9500 | 2.1786 | 0.6986 | 0.7578 | 0.7270 | 0.7726 | |
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| 0.0 | 169.49 | 10000 | 2.1782 | 0.6965 | 0.7560 | 0.7250 | 0.7721 | |
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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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