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---
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license: mit
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base_model: SCUT-DLVCLab/lilt-roberta-en-base
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tags:
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- generated_from_trainer
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model-index:
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- name: lilt-en-funsd
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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-en-funsd
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This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 1.6706
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- eval_ANSWER: {'precision': 0.875, 'recall': 0.9082007343941249, 'f1': 0.8912912912912913, 'number': 817}
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- eval_HEADER: {'precision': 0.6666666666666666, 'recall': 0.5714285714285714, 'f1': 0.6153846153846153, 'number': 119}
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- eval_QUESTION: {'precision': 0.8880931065353626, 'recall': 0.9210770659238626, 'f1': 0.9042844120328168, 'number': 1077}
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- eval_overall_precision: 0.8718
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- eval_overall_recall: 0.8952
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- eval_overall_f1: 0.8833
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- eval_overall_accuracy: 0.8026
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- eval_runtime: 50.8863
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- eval_samples_per_second: 0.983
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- eval_steps_per_second: 0.138
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- epoch: 90.8421
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- step: 1726
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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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- training_steps: 2500
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 4.42.4
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- Pytorch 2.3.1+cpu
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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