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update model card README.md
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README.md
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---
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license: mit
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tags:
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- generated_from_trainer
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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-xlm-roberta-base-finetuned-funsd-iob-original
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results: []
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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-xlm-roberta-base-finetuned-funsd-iob-original
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.1573
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- Precision: 0.7252
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- Recall: 0.7718
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- F1: 0.7478
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- Accuracy: 0.7676
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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: 8
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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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- num_epochs: 30
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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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| No log | 1.33 | 100 | 0.8309 | 0.5157 | 0.6673 | 0.5818 | 0.6594 |
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| No log | 2.67 | 200 | 1.0045 | 0.6080 | 0.6699 | 0.6374 | 0.7387 |
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| No log | 4.0 | 300 | 0.9127 | 0.6177 | 0.7310 | 0.6696 | 0.7427 |
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| No log | 5.33 | 400 | 0.9808 | 0.6478 | 0.7300 | 0.6865 | 0.7521 |
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| 0.6318 | 6.67 | 500 | 1.2169 | 0.6863 | 0.7376 | 0.7110 | 0.7547 |
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| 0.6318 | 8.0 | 600 | 1.1830 | 0.6918 | 0.7580 | 0.7234 | 0.7326 |
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| 0.6318 | 9.33 | 700 | 1.3537 | 0.6955 | 0.7504 | 0.7219 | 0.7426 |
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| 0.6318 | 10.67 | 800 | 1.3888 | 0.6994 | 0.7611 | 0.7290 | 0.7507 |
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| 0.6318 | 12.0 | 900 | 1.5929 | 0.7204 | 0.7560 | 0.7378 | 0.7553 |
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| 0.1082 | 13.33 | 1000 | 1.7679 | 0.6891 | 0.7397 | 0.7135 | 0.7452 |
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| 0.1082 | 14.67 | 1100 | 1.7197 | 0.7003 | 0.7570 | 0.7275 | 0.7530 |
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| 0.1082 | 16.0 | 1200 | 1.8053 | 0.7188 | 0.7448 | 0.7315 | 0.7616 |
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| 0.1082 | 17.33 | 1300 | 1.9315 | 0.7109 | 0.7728 | 0.7405 | 0.7643 |
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| 0.1082 | 18.67 | 1400 | 2.0142 | 0.7240 | 0.7789 | 0.7504 | 0.7676 |
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| 0.0312 | 20.0 | 1500 | 2.0475 | 0.7264 | 0.7478 | 0.7369 | 0.7654 |
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| 0.0312 | 21.33 | 1600 | 2.0463 | 0.7251 | 0.7539 | 0.7393 | 0.7599 |
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| 0.0312 | 22.67 | 1700 | 2.0648 | 0.7289 | 0.7753 | 0.7514 | 0.7623 |
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| 0.0312 | 24.0 | 1800 | 2.1301 | 0.7272 | 0.7606 | 0.7435 | 0.7667 |
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| 0.0312 | 25.33 | 1900 | 2.1319 | 0.7274 | 0.7585 | 0.7426 | 0.7694 |
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| 0.0064 | 26.67 | 2000 | 2.1499 | 0.7247 | 0.7723 | 0.7477 | 0.7673 |
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| 0.0064 | 28.0 | 2100 | 2.1627 | 0.7235 | 0.7733 | 0.7476 | 0.7670 |
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| 0.0064 | 29.33 | 2200 | 2.1573 | 0.7252 | 0.7718 | 0.7478 | 0.7676 |
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.13.0+cu116
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- Datasets 2.8.0
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- Tokenizers 0.13.2
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