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