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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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- Loss: 1.6214 |
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- Answer: {'precision': 0.8792899408284024, 'recall': 0.9094247246022031, 'f1': 0.8941034897713599, 'number': 817} |
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- Header: {'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119} |
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- Question: {'precision': 0.8960573476702509, 'recall': 0.9285051067780873, 'f1': 0.9119927040583674, 'number': 1077} |
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- Overall Precision: 0.8749 |
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- Overall Recall: 0.8967 |
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- Overall F1: 0.8857 |
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- Overall Accuracy: 0.8129 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:--------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.4022 | 10.5263 | 200 | 1.0717 | {'precision': 0.8156424581005587, 'recall': 0.8935128518971848, 'f1': 0.852803738317757, 'number': 817} | {'precision': 0.452991452991453, 'recall': 0.44537815126050423, 'f1': 0.4491525423728814, 'number': 119} | {'precision': 0.8516579406631762, 'recall': 0.9062209842154132, 'f1': 0.8780926675663517, 'number': 1077} | 0.8151 | 0.8738 | 0.8434 | 0.7966 | |
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| 0.0484 | 21.0526 | 400 | 1.2460 | {'precision': 0.8537794299876085, 'recall': 0.8433292533659731, 'f1': 0.8485221674876847, 'number': 817} | {'precision': 0.5454545454545454, 'recall': 0.5546218487394958, 'f1': 0.5499999999999999, 'number': 119} | {'precision': 0.8713398402839396, 'recall': 0.9117920148560817, 'f1': 0.8911070780399274, 'number': 1077} | 0.8453 | 0.8629 | 0.8540 | 0.8008 | |
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| 0.0143 | 31.5789 | 600 | 1.5585 | {'precision': 0.8566433566433567, 'recall': 0.8996328029375765, 'f1': 0.8776119402985075, 'number': 817} | {'precision': 0.5294117647058824, 'recall': 0.5294117647058824, 'f1': 0.5294117647058824, 'number': 119} | {'precision': 0.8879310344827587, 'recall': 0.8607242339832869, 'f1': 0.8741159830268741, 'number': 1077} | 0.8535 | 0.8569 | 0.8552 | 0.7935 | |
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| 0.0079 | 42.1053 | 800 | 1.5146 | {'precision': 0.8556338028169014, 'recall': 0.8922888616891065, 'f1': 0.8735769922109047, 'number': 817} | {'precision': 0.47761194029850745, 'recall': 0.5378151260504201, 'f1': 0.5059288537549407, 'number': 119} | {'precision': 0.8851540616246498, 'recall': 0.8802228412256268, 'f1': 0.88268156424581, 'number': 1077} | 0.8464 | 0.8649 | 0.8555 | 0.7989 | |
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| 0.0041 | 52.6316 | 1000 | 1.5279 | {'precision': 0.8536299765807962, 'recall': 0.8922888616891065, 'f1': 0.8725314183123878, 'number': 817} | {'precision': 0.6, 'recall': 0.5042016806722689, 'f1': 0.547945205479452, 'number': 119} | {'precision': 0.8764342453662842, 'recall': 0.9220055710306406, 'f1': 0.8986425339366516, 'number': 1077} | 0.8539 | 0.8852 | 0.8693 | 0.8063 | |
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| 0.0031 | 63.1579 | 1200 | 1.5225 | {'precision': 0.8413793103448276, 'recall': 0.8959608323133414, 'f1': 0.8678126852400712, 'number': 817} | {'precision': 0.5794392523364486, 'recall': 0.5210084033613446, 'f1': 0.5486725663716815, 'number': 119} | {'precision': 0.8873499538319483, 'recall': 0.8922934076137419, 'f1': 0.8898148148148148, 'number': 1077} | 0.8519 | 0.8718 | 0.8618 | 0.8014 | |
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| 0.0016 | 73.6842 | 1400 | 1.6214 | {'precision': 0.8792899408284024, 'recall': 0.9094247246022031, 'f1': 0.8941034897713599, 'number': 817} | {'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119} | {'precision': 0.8960573476702509, 'recall': 0.9285051067780873, 'f1': 0.9119927040583674, 'number': 1077} | 0.8749 | 0.8967 | 0.8857 | 0.8129 | |
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| 0.0011 | 84.2105 | 1600 | 1.7158 | {'precision': 0.8770186335403727, 'recall': 0.8641370869033048, 'f1': 0.8705302096177557, 'number': 817} | {'precision': 0.5419847328244275, 'recall': 0.5966386554621849, 'f1': 0.568, 'number': 119} | {'precision': 0.9052044609665427, 'recall': 0.904363974001857, 'f1': 0.9047840222944729, 'number': 1077} | 0.8703 | 0.8698 | 0.8701 | 0.8070 | |
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| 0.001 | 94.7368 | 1800 | 1.6261 | {'precision': 0.8481735159817352, 'recall': 0.9094247246022031, 'f1': 0.8777318369757826, 'number': 817} | {'precision': 0.6017699115044248, 'recall': 0.5714285714285714, 'f1': 0.5862068965517241, 'number': 119} | {'precision': 0.9050751879699248, 'recall': 0.8941504178272981, 'f1': 0.8995796356842597, 'number': 1077} | 0.8641 | 0.8813 | 0.8726 | 0.8128 | |
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| 0.0004 | 105.2632 | 2000 | 1.6253 | {'precision': 0.8611435239206534, 'recall': 0.9033047735618115, 'f1': 0.8817204301075269, 'number': 817} | {'precision': 0.625, 'recall': 0.5462184873949579, 'f1': 0.5829596412556054, 'number': 119} | {'precision': 0.894404332129964, 'recall': 0.9201485608170845, 'f1': 0.9070938215102976, 'number': 1077} | 0.8671 | 0.8912 | 0.8790 | 0.8194 | |
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| 0.0001 | 115.7895 | 2200 | 1.6711 | {'precision': 0.8823529411764706, 'recall': 0.8812729498164015, 'f1': 0.8818126148193509, 'number': 817} | {'precision': 0.6283185840707964, 'recall': 0.5966386554621849, 'f1': 0.6120689655172413, 'number': 119} | {'precision': 0.8967391304347826, 'recall': 0.9192200557103064, 'f1': 0.9078404401650619, 'number': 1077} | 0.8760 | 0.8847 | 0.8804 | 0.8190 | |
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| 0.0002 | 126.3158 | 2400 | 1.6682 | {'precision': 0.8756038647342995, 'recall': 0.8873929008567931, 'f1': 0.8814589665653496, 'number': 817} | {'precision': 0.6330275229357798, 'recall': 0.5798319327731093, 'f1': 0.6052631578947367, 'number': 119} | {'precision': 0.8997289972899729, 'recall': 0.924791086350975, 'f1': 0.9120879120879122, 'number': 1077} | 0.8757 | 0.8892 | 0.8824 | 0.8179 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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