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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- funsd-layoutlmv3 |
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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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inference: false |
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base_model: nielsr/lilt-roberta-en-base |
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model-index: |
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- name: lilt-roberta-en-base-finetuned-funsd |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: funsd-layoutlmv3 |
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type: funsd-layoutlmv3 |
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config: funsd |
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split: train |
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args: funsd |
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metrics: |
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- type: precision |
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value: 0.8761670761670761 |
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name: Precision |
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- type: recall |
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value: 0.8857426726279185 |
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name: Recall |
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- type: f1 |
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value: 0.8809288537549407 |
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name: F1 |
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- type: accuracy |
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value: 0.8068465470105789 |
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name: Accuracy |
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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-roberta-en-base-finetuned-funsd |
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This model is a fine-tuned version of [nielsr/lilt-roberta-en-base](https://huggingface.co/nielsr/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6552 |
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- Precision: 0.8762 |
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- Recall: 0.8857 |
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- F1: 0.8809 |
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- Accuracy: 0.8068 |
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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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- lr_scheduler_warmup_steps: 0.1 |
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- training_steps: 2000 |
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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 | 5.26 | 100 | 1.1789 | 0.8506 | 0.8485 | 0.8495 | 0.7869 | |
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| No log | 10.53 | 200 | 1.2382 | 0.8360 | 0.8788 | 0.8569 | 0.7970 | |
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| No log | 15.79 | 300 | 1.3766 | 0.8557 | 0.8897 | 0.8724 | 0.7909 | |
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| No log | 21.05 | 400 | 1.5590 | 0.8368 | 0.8763 | 0.8561 | 0.7792 | |
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| 0.04 | 26.32 | 500 | 1.4379 | 0.8562 | 0.8813 | 0.8685 | 0.7992 | |
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| 0.04 | 31.58 | 600 | 1.5397 | 0.8593 | 0.8947 | 0.8766 | 0.8054 | |
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| 0.04 | 36.84 | 700 | 1.6132 | 0.8621 | 0.8723 | 0.8672 | 0.7933 | |
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| 0.04 | 42.11 | 800 | 1.6483 | 0.8566 | 0.8872 | 0.8716 | 0.7777 | |
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| 0.04 | 47.37 | 900 | 1.6593 | 0.8641 | 0.8813 | 0.8726 | 0.7895 | |
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| 0.0044 | 52.63 | 1000 | 1.6704 | 0.8595 | 0.8718 | 0.8656 | 0.7925 | |
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| 0.0044 | 57.89 | 1100 | 1.6795 | 0.8495 | 0.8803 | 0.8646 | 0.7748 | |
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| 0.0044 | 63.16 | 1200 | 1.5515 | 0.8604 | 0.8912 | 0.8755 | 0.7991 | |
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| 0.0044 | 68.42 | 1300 | 1.6665 | 0.8573 | 0.8867 | 0.8718 | 0.7821 | |
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| 0.0044 | 73.68 | 1400 | 1.5893 | 0.8604 | 0.8877 | 0.8738 | 0.7895 | |
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| 0.0008 | 78.95 | 1500 | 1.5613 | 0.8603 | 0.8872 | 0.8736 | 0.8123 | |
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| 0.0008 | 84.21 | 1600 | 1.5853 | 0.8521 | 0.8872 | 0.8693 | 0.8040 | |
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| 0.0008 | 89.47 | 1700 | 1.6539 | 0.8707 | 0.8833 | 0.8769 | 0.8077 | |
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| 0.0008 | 94.74 | 1800 | 1.6634 | 0.8787 | 0.8813 | 0.8800 | 0.8079 | |
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| 0.0008 | 100.0 | 1900 | 1.6534 | 0.8810 | 0.8862 | 0.8836 | 0.8073 | |
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| 0.0004 | 105.26 | 2000 | 1.6552 | 0.8762 | 0.8857 | 0.8809 | 0.8068 | |
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### Framework versions |
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- Transformers 4.23.0.dev0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.5.1 |
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- Tokenizers 0.13.0 |
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