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---
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
model-index:
- name: lilt-en-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lilt-en-funsd
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.
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
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