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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-DocLayNet-base_ml384-v2 |
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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-DocLayNet-base_ml384-v2 |
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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: 1.0003 |
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- Precision: 0.8584 |
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- Recall: 0.8584 |
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- F1: 0.8584 |
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- Accuracy: 0.8584 |
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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: 16 |
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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: 5 |
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- mixed_precision_training: Native AMP |
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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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| 0.7223 | 0.21 | 500 | 0.7765 | 0.7741 | 0.7741 | 0.7741 | 0.7741 | |
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| 0.4469 | 0.42 | 1000 | 0.5914 | 0.8312 | 0.8312 | 0.8312 | 0.8312 | |
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| 0.3819 | 0.62 | 1500 | 0.8745 | 0.8102 | 0.8102 | 0.8102 | 0.8102 | |
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| 0.3361 | 0.83 | 2000 | 0.6991 | 0.8337 | 0.8337 | 0.8337 | 0.8337 | |
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| 0.2784 | 1.04 | 2500 | 0.7513 | 0.8119 | 0.8119 | 0.8119 | 0.8119 | |
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| 0.2377 | 1.25 | 3000 | 0.9048 | 0.8166 | 0.8166 | 0.8166 | 0.8166 | |
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| 0.2401 | 1.45 | 3500 | 1.2411 | 0.7939 | 0.7939 | 0.7939 | 0.7939 | |
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| 0.2054 | 1.66 | 4000 | 1.1594 | 0.8080 | 0.8080 | 0.8080 | 0.8080 | |
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| 0.1909 | 1.87 | 4500 | 0.7545 | 0.8425 | 0.8425 | 0.8425 | 0.8425 | |
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| 0.1704 | 2.08 | 5000 | 0.8567 | 0.8318 | 0.8318 | 0.8318 | 0.8318 | |
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| 0.1294 | 2.29 | 5500 | 0.8486 | 0.8489 | 0.8489 | 0.8489 | 0.8489 | |
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| 0.134 | 2.49 | 6000 | 0.7682 | 0.8573 | 0.8573 | 0.8573 | 0.8573 | |
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| 0.1354 | 2.7 | 6500 | 0.9871 | 0.8256 | 0.8256 | 0.8256 | 0.8256 | |
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| 0.1239 | 2.91 | 7000 | 1.1430 | 0.8189 | 0.8189 | 0.8189 | 0.8189 | |
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| 0.1012 | 3.12 | 7500 | 0.8272 | 0.8386 | 0.8386 | 0.8386 | 0.8386 | |
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| 0.0788 | 3.32 | 8000 | 1.0288 | 0.8365 | 0.8365 | 0.8365 | 0.8365 | |
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| 0.0802 | 3.53 | 8500 | 0.7197 | 0.8849 | 0.8849 | 0.8849 | 0.8849 | |
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| 0.0861 | 3.74 | 9000 | 1.1420 | 0.8320 | 0.8320 | 0.8320 | 0.8320 | |
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| 0.0639 | 3.95 | 9500 | 0.9563 | 0.8585 | 0.8585 | 0.8585 | 0.8585 | |
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| 0.0464 | 4.15 | 10000 | 1.0768 | 0.8511 | 0.8511 | 0.8511 | 0.8511 | |
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| 0.0412 | 4.36 | 10500 | 1.1184 | 0.8439 | 0.8439 | 0.8439 | 0.8439 | |
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| 0.039 | 4.57 | 11000 | 0.9634 | 0.8636 | 0.8636 | 0.8636 | 0.8636 | |
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| 0.0469 | 4.78 | 11500 | 0.9585 | 0.8634 | 0.8634 | 0.8634 | 0.8634 | |
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| 0.0395 | 4.99 | 12000 | 1.0003 | 0.8584 | 0.8584 | 0.8584 | 0.8584 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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