Edit model card

LiLt-funsd-en

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7735
  • Answer: {'precision': 0.8693115519253208, 'recall': 0.9118727050183598, 'f1': 0.8900836320191159, 'number': 817}
  • Header: {'precision': 0.6630434782608695, 'recall': 0.5126050420168067, 'f1': 0.5781990521327014, 'number': 119}
  • Question: {'precision': 0.8992673992673993, 'recall': 0.9117920148560817, 'f1': 0.905486399262333, 'number': 1077}
  • Overall Precision: 0.8760
  • Overall Recall: 0.8882
  • Overall F1: 0.8821
  • Overall Accuracy: 0.8027

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
0.4108 10.53 200 0.9495 {'precision': 0.7949260042283298, 'recall': 0.9204406364749081, 'f1': 0.8530913216108904, 'number': 817} {'precision': 0.5338345864661654, 'recall': 0.5966386554621849, 'f1': 0.5634920634920635, 'number': 119} {'precision': 0.8950914340712224, 'recall': 0.8635097493036211, 'f1': 0.8790170132325141, 'number': 1077} 0.8277 0.8708 0.8487 0.7845
0.0402 21.05 400 1.3124 {'precision': 0.8167597765363128, 'recall': 0.8947368421052632, 'f1': 0.8539719626168224, 'number': 817} {'precision': 0.5071428571428571, 'recall': 0.5966386554621849, 'f1': 0.5482625482625483, 'number': 119} {'precision': 0.9133398247322297, 'recall': 0.8709377901578459, 'f1': 0.8916349809885932, 'number': 1077} 0.8438 0.8644 0.8540 0.7901
0.0141 31.58 600 1.4747 {'precision': 0.8429378531073446, 'recall': 0.9130966952264382, 'f1': 0.8766157461809635, 'number': 817} {'precision': 0.6493506493506493, 'recall': 0.42016806722689076, 'f1': 0.5102040816326531, 'number': 119} {'precision': 0.8861788617886179, 'recall': 0.9108635097493036, 'f1': 0.8983516483516484, 'number': 1077} 0.8589 0.8828 0.8707 0.7950
0.0079 42.11 800 1.6605 {'precision': 0.8132464712269273, 'recall': 0.9167686658506732, 'f1': 0.8619102416570771, 'number': 817} {'precision': 0.5407407407407407, 'recall': 0.6134453781512605, 'f1': 0.5748031496062992, 'number': 119} {'precision': 0.8919925512104283, 'recall': 0.8895078922934077, 'f1': 0.8907484890748489, 'number': 1077} 0.8357 0.8843 0.8593 0.7910
0.0064 52.63 1000 1.6328 {'precision': 0.8551564310544612, 'recall': 0.9033047735618115, 'f1': 0.8785714285714284, 'number': 817} {'precision': 0.7466666666666667, 'recall': 0.47058823529411764, 'f1': 0.5773195876288659, 'number': 119} {'precision': 0.9029918404351768, 'recall': 0.924791086350975, 'f1': 0.9137614678899083, 'number': 1077} 0.8770 0.8892 0.8831 0.7950
0.0091 63.16 1200 1.6620 {'precision': 0.8280044101433297, 'recall': 0.9192166462668299, 'f1': 0.87122969837587, 'number': 817} {'precision': 0.6875, 'recall': 0.46218487394957986, 'f1': 0.5527638190954773, 'number': 119} {'precision': 0.9016697588126159, 'recall': 0.9025069637883009, 'f1': 0.9020881670533641, 'number': 1077} 0.8610 0.8833 0.8720 0.7979
0.0031 73.68 1400 1.7310 {'precision': 0.855039637599094, 'recall': 0.9241126070991432, 'f1': 0.8882352941176471, 'number': 817} {'precision': 0.7023809523809523, 'recall': 0.4957983193277311, 'f1': 0.58128078817734, 'number': 119} {'precision': 0.8969917958067457, 'recall': 0.9136490250696379, 'f1': 0.9052437902483901, 'number': 1077} 0.8711 0.8932 0.8820 0.7938
0.002 84.21 1600 1.6978 {'precision': 0.870023419203747, 'recall': 0.9094247246022031, 'f1': 0.8892878515858766, 'number': 817} {'precision': 0.6987951807228916, 'recall': 0.48739495798319327, 'f1': 0.5742574257425742, 'number': 119} {'precision': 0.9021237303785781, 'recall': 0.9071494893221913, 'f1': 0.9046296296296297, 'number': 1077} 0.8802 0.8833 0.8817 0.8048
0.0023 94.74 1800 1.5996 {'precision': 0.8639534883720931, 'recall': 0.9094247246022031, 'f1': 0.8861061419200955, 'number': 817} {'precision': 0.6629213483146067, 'recall': 0.4957983193277311, 'f1': 0.5673076923076922, 'number': 119} {'precision': 0.8969641214351426, 'recall': 0.9052924791086351, 'f1': 0.9011090573012939, 'number': 1077} 0.8728 0.8828 0.8777 0.8026
0.0009 105.26 2000 1.7239 {'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817} {'precision': 0.5726495726495726, 'recall': 0.5630252100840336, 'f1': 0.5677966101694915, 'number': 119} {'precision': 0.889487870619946, 'recall': 0.9192200557103064, 'f1': 0.9041095890410958, 'number': 1077} 0.8698 0.8927 0.8811 0.8008
0.0009 115.79 2200 1.6091 {'precision': 0.8576349024110218, 'recall': 0.9143206854345165, 'f1': 0.8850710900473934, 'number': 817} {'precision': 0.6213592233009708, 'recall': 0.5378151260504201, 'f1': 0.5765765765765765, 'number': 119} {'precision': 0.8901098901098901, 'recall': 0.9025069637883009, 'f1': 0.896265560165975, 'number': 1077} 0.8630 0.8857 0.8742 0.8093
0.0003 126.32 2400 1.7210 {'precision': 0.8627450980392157, 'recall': 0.9155446756425949, 'f1': 0.8883610451306414, 'number': 817} {'precision': 0.6555555555555556, 'recall': 0.4957983193277311, 'f1': 0.5645933014354068, 'number': 119} {'precision': 0.8873994638069705, 'recall': 0.9220055710306406, 'f1': 0.9043715846994536, 'number': 1077} 0.8671 0.8942 0.8804 0.8072
0.0006 136.84 2600 1.8215 {'precision': 0.8293478260869566, 'recall': 0.9339045287637698, 'f1': 0.8785261945883708, 'number': 817} {'precision': 0.5887850467289719, 'recall': 0.5294117647058824, 'f1': 0.5575221238938053, 'number': 119} {'precision': 0.9232227488151659, 'recall': 0.904363974001857, 'f1': 0.9136960600375235, 'number': 1077} 0.8646 0.8942 0.8791 0.7946
0.0002 147.37 2800 1.8084 {'precision': 0.875886524822695, 'recall': 0.9069767441860465, 'f1': 0.8911605532170775, 'number': 817} {'precision': 0.5752212389380531, 'recall': 0.5462184873949579, 'f1': 0.5603448275862069, 'number': 119} {'precision': 0.8896860986547085, 'recall': 0.9210770659238626, 'f1': 0.9051094890510949, 'number': 1077} 0.8669 0.8932 0.8799 0.8000
0.0002 157.89 3000 1.8224 {'precision': 0.9067164179104478, 'recall': 0.8922888616891065, 'f1': 0.8994447871684145, 'number': 817} {'precision': 0.6222222222222222, 'recall': 0.47058823529411764, 'f1': 0.5358851674641149, 'number': 119} {'precision': 0.8937112488928255, 'recall': 0.9368616527390901, 'f1': 0.9147778785131461, 'number': 1077} 0.8868 0.8912 0.8890 0.8082
0.0005 168.42 3200 1.7383 {'precision': 0.8754406580493537, 'recall': 0.9118727050183598, 'f1': 0.8932853717026379, 'number': 817} {'precision': 0.6451612903225806, 'recall': 0.5042016806722689, 'f1': 0.5660377358490566, 'number': 119} {'precision': 0.8995475113122172, 'recall': 0.9229340761374187, 'f1': 0.9110907424381303, 'number': 1077} 0.8780 0.8937 0.8858 0.8117
0.0002 178.95 3400 1.7757 {'precision': 0.885954381752701, 'recall': 0.9033047735618115, 'f1': 0.8945454545454545, 'number': 817} {'precision': 0.6896551724137931, 'recall': 0.5042016806722689, 'f1': 0.5825242718446602, 'number': 119} {'precision': 0.9043321299638989, 'recall': 0.9303621169916435, 'f1': 0.917162471395881, 'number': 1077} 0.8876 0.8942 0.8909 0.8104
0.0001 189.47 3600 1.7467 {'precision': 0.8645833333333334, 'recall': 0.9143206854345165, 'f1': 0.8887566924449734, 'number': 817} {'precision': 0.6354166666666666, 'recall': 0.5126050420168067, 'f1': 0.5674418604651162, 'number': 119} {'precision': 0.9027522935779817, 'recall': 0.9136490250696379, 'f1': 0.9081679741578219, 'number': 1077} 0.8741 0.8902 0.8821 0.8054
0.0002 200.0 3800 1.7730 {'precision': 0.8631090487238979, 'recall': 0.9106487148102815, 'f1': 0.8862418106015486, 'number': 817} {'precision': 0.6354166666666666, 'recall': 0.5126050420168067, 'f1': 0.5674418604651162, 'number': 119} {'precision': 0.8952205882352942, 'recall': 0.904363974001857, 'f1': 0.899769053117783, 'number': 1077} 0.8695 0.8838 0.8766 0.8024
0.0001 210.53 4000 1.7735 {'precision': 0.8693115519253208, 'recall': 0.9118727050183598, 'f1': 0.8900836320191159, 'number': 817} {'precision': 0.6630434782608695, 'recall': 0.5126050420168067, 'f1': 0.5781990521327014, 'number': 119} {'precision': 0.8992673992673993, 'recall': 0.9117920148560817, 'f1': 0.905486399262333, 'number': 1077} 0.8760 0.8882 0.8821 0.8027

Framework versions

  • Transformers 4.24.0
  • Pytorch 1.12.1
  • Datasets 2.7.1
  • Tokenizers 0.13.2
Downloads last month
14

Space using jinhybr/LiLt-funsd-en 1