lilt-en-funsd / README.md
someet's picture
End of training
ffefe89 verified
metadata
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
  - generated_from_trainer
model-index:
  - name: lilt-en-funsd
    results: []

lilt-en-funsd

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

  • Loss: 1.5254
  • Answer: {'precision': 0.8486238532110092, 'recall': 0.9057527539779682, 'f1': 0.8762581409117821, 'number': 817}
  • Header: {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119}
  • Question: {'precision': 0.9026629935720845, 'recall': 0.9127205199628597, 'f1': 0.9076638965835643, 'number': 1077}
  • Overall Precision: 0.8683
  • Overall Recall: 0.8872
  • Overall F1: 0.8776
  • Overall Accuracy: 0.8064

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: 2500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
0.4037 10.53 200 1.0901 {'precision': 0.8236658932714617, 'recall': 0.8690330477356181, 'f1': 0.8457415128052411, 'number': 817} {'precision': 0.42528735632183906, 'recall': 0.6218487394957983, 'f1': 0.5051194539249146, 'number': 119} {'precision': 0.871356783919598, 'recall': 0.8050139275766016, 'f1': 0.8368725868725869, 'number': 1077} 0.8129 0.8202 0.8165 0.7725
0.0456 21.05 400 1.4102 {'precision': 0.8165745856353591, 'recall': 0.9045287637698899, 'f1': 0.8583042973286875, 'number': 817} {'precision': 0.6071428571428571, 'recall': 0.42857142857142855, 'f1': 0.5024630541871921, 'number': 119} {'precision': 0.8835304822565969, 'recall': 0.9015784586815228, 'f1': 0.8924632352941178, 'number': 1077} 0.8434 0.8748 0.8588 0.7879
0.0146 31.58 600 1.5424 {'precision': 0.834056399132321, 'recall': 0.9412484700122399, 'f1': 0.8844163312248418, 'number': 817} {'precision': 0.5118110236220472, 'recall': 0.5462184873949579, 'f1': 0.5284552845528455, 'number': 119} {'precision': 0.9035004730368968, 'recall': 0.8867223769730733, 'f1': 0.895032802249297, 'number': 1077} 0.8495 0.8887 0.8687 0.7913
0.0074 42.11 800 1.4579 {'precision': 0.8571428571428571, 'recall': 0.8886168910648715, 'f1': 0.8725961538461537, 'number': 817} {'precision': 0.5798319327731093, 'recall': 0.5798319327731093, 'f1': 0.5798319327731093, 'number': 119} {'precision': 0.8590192644483362, 'recall': 0.9108635097493036, 'f1': 0.8841820639927895, 'number': 1077} 0.8425 0.8823 0.8619 0.8063
0.0043 52.63 1000 1.8745 {'precision': 0.8458100558659218, 'recall': 0.9265605875152999, 'f1': 0.8843457943925235, 'number': 817} {'precision': 0.5641025641025641, 'recall': 0.5546218487394958, 'f1': 0.559322033898305, 'number': 119} {'precision': 0.9229268292682927, 'recall': 0.8783658310120706, 'f1': 0.9000951474785919, 'number': 1077} 0.8684 0.8788 0.8736 0.7883
0.0035 63.16 1200 1.8084 {'precision': 0.8344086021505376, 'recall': 0.9498164014687882, 'f1': 0.8883800801373782, 'number': 817} {'precision': 0.580952380952381, 'recall': 0.5126050420168067, 'f1': 0.5446428571428571, 'number': 119} {'precision': 0.9076343072573044, 'recall': 0.8941504178272981, 'f1': 0.9008419083255378, 'number': 1077} 0.8588 0.8942 0.8761 0.7965
0.0022 73.68 1400 1.4973 {'precision': 0.8706586826347306, 'recall': 0.8898408812729498, 'f1': 0.8801452784503632, 'number': 817} {'precision': 0.6176470588235294, 'recall': 0.5294117647058824, 'f1': 0.5701357466063349, 'number': 119} {'precision': 0.8852313167259787, 'recall': 0.9238625812441968, 'f1': 0.9041344843253067, 'number': 1077} 0.8661 0.8867 0.8763 0.8137
0.0025 84.21 1600 1.5254 {'precision': 0.8486238532110092, 'recall': 0.9057527539779682, 'f1': 0.8762581409117821, 'number': 817} {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119} {'precision': 0.9026629935720845, 'recall': 0.9127205199628597, 'f1': 0.9076638965835643, 'number': 1077} 0.8683 0.8872 0.8776 0.8064
0.0006 94.74 1800 1.5072 {'precision': 0.8583042973286876, 'recall': 0.9045287637698899, 'f1': 0.8808104886769966, 'number': 817} {'precision': 0.64, 'recall': 0.5378151260504201, 'f1': 0.5844748858447488, 'number': 119} {'precision': 0.8841354723707665, 'recall': 0.9210770659238626, 'f1': 0.9022282855843565, 'number': 1077} 0.8617 0.8917 0.8765 0.8085
0.0004 105.26 2000 1.5540 {'precision': 0.847926267281106, 'recall': 0.9008567931456548, 'f1': 0.8735905044510385, 'number': 817} {'precision': 0.5959595959595959, 'recall': 0.4957983193277311, 'f1': 0.5412844036697246, 'number': 119} {'precision': 0.8814016172506739, 'recall': 0.9108635097493036, 'f1': 0.8958904109589041, 'number': 1077} 0.8538 0.8823 0.8678 0.8014
0.0002 115.79 2200 1.5880 {'precision': 0.8609501738122828, 'recall': 0.9094247246022031, 'f1': 0.8845238095238096, 'number': 817} {'precision': 0.5876288659793815, 'recall': 0.4789915966386555, 'f1': 0.5277777777777778, 'number': 119} {'precision': 0.8843416370106761, 'recall': 0.9229340761374187, 'f1': 0.9032258064516129, 'number': 1077} 0.8608 0.8912 0.8758 0.7986
0.0003 126.32 2400 1.5619 {'precision': 0.8586326767091541, 'recall': 0.9069767441860465, 'f1': 0.8821428571428572, 'number': 817} {'precision': 0.6021505376344086, 'recall': 0.47058823529411764, 'f1': 0.5283018867924528, 'number': 119} {'precision': 0.8775510204081632, 'recall': 0.9182915506035283, 'f1': 0.8974591651542649, 'number': 1077} 0.8574 0.8872 0.8721 0.8060

Framework versions

  • Transformers 4.38.1
  • Pytorch 2.2.1+cu118
  • Datasets 2.17.1
  • Tokenizers 0.15.2