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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_paragraphs_ml512-v5
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+ results: []
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+ ---
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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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+
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+ # lilt-xlm-roberta-base-finetuned-DocLayNet-base_paragraphs_ml512-v5
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+
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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: 0.4104
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+ - Precision: 0.8634
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+ - Recall: 0.8634
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+ - F1: 0.8634
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+ - Accuracy: 0.8634
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-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: 1
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.05 | 100 | 0.9875 | 0.6585 | 0.6585 | 0.6585 | 0.6585 |
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+ | No log | 0.11 | 200 | 0.7886 | 0.7551 | 0.7551 | 0.7551 | 0.7551 |
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+ | No log | 0.16 | 300 | 0.5894 | 0.8248 | 0.8248 | 0.8248 | 0.8248 |
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+ | No log | 0.21 | 400 | 0.4794 | 0.8396 | 0.8396 | 0.8396 | 0.8396 |
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+ | 0.7446 | 0.27 | 500 | 0.3993 | 0.8703 | 0.8703 | 0.8703 | 0.8703 |
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+ | 0.7446 | 0.32 | 600 | 0.3631 | 0.8857 | 0.8857 | 0.8857 | 0.8857 |
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+ | 0.7446 | 0.37 | 700 | 0.4096 | 0.8630 | 0.8630 | 0.8630 | 0.8630 |
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+ | 0.7446 | 0.43 | 800 | 0.4492 | 0.8528 | 0.8528 | 0.8528 | 0.8528 |
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+ | 0.7446 | 0.48 | 900 | 0.3839 | 0.8834 | 0.8834 | 0.8834 | 0.8834 |
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+ | 0.4464 | 0.53 | 1000 | 0.4365 | 0.8498 | 0.8498 | 0.8498 | 0.8498 |
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+ | 0.4464 | 0.59 | 1100 | 0.3616 | 0.8812 | 0.8812 | 0.8812 | 0.8812 |
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+ | 0.4464 | 0.64 | 1200 | 0.3949 | 0.8796 | 0.8796 | 0.8796 | 0.8796 |
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+ | 0.4464 | 0.69 | 1300 | 0.4184 | 0.8613 | 0.8613 | 0.8613 | 0.8613 |
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+ | 0.4464 | 0.75 | 1400 | 0.4130 | 0.8743 | 0.8743 | 0.8743 | 0.8743 |
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+ | 0.3672 | 0.8 | 1500 | 0.4535 | 0.8289 | 0.8289 | 0.8289 | 0.8289 |
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+ | 0.3672 | 0.85 | 1600 | 0.3681 | 0.8713 | 0.8713 | 0.8713 | 0.8713 |
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+ | 0.3672 | 0.91 | 1700 | 0.3446 | 0.8857 | 0.8857 | 0.8857 | 0.8857 |
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+ | 0.3672 | 0.96 | 1800 | 0.4104 | 0.8634 | 0.8634 | 0.8634 | 0.8634 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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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