File size: 7,081 Bytes
1e633e3 882df66 1e633e3 3467d6d 1e633e3 882df66 1e633e3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-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. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6712
- Answer: {'precision': 0.6719409282700421, 'recall': 0.7873918417799752, 'f1': 0.7250996015936254, 'number': 809}
- Header: {'precision': 0.3153153153153153, 'recall': 0.29411764705882354, 'f1': 0.30434782608695654, 'number': 119}
- Question: {'precision': 0.7069109075770191, 'recall': 0.7971830985915493, 'f1': 0.7493380406001765, 'number': 1065}
- Overall Precision: 0.6730
- Overall Recall: 0.7632
- Overall F1: 0.7153
- Overall Accuracy: 0.7909
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7528 | 1.0 | 10 | 1.5450 | {'precision': 0.04079497907949791, 'recall': 0.048207663782447466, 'f1': 0.04419263456090652, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1806060606060606, 'recall': 0.13990610328638498, 'f1': 0.15767195767195766, 'number': 1065} | 0.1056 | 0.0943 | 0.0996 | 0.3786 |
| 1.4294 | 2.0 | 20 | 1.2643 | {'precision': 0.20842824601366744, 'recall': 0.22620519159456118, 'f1': 0.2169531713100178, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4424778761061947, 'recall': 0.5164319248826291, 'f1': 0.4766031195840555, 'number': 1065} | 0.3456 | 0.3678 | 0.3563 | 0.5767 |
| 1.1277 | 3.0 | 30 | 0.9879 | {'precision': 0.4243845252051583, 'recall': 0.44746600741656367, 'f1': 0.4356197352587245, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5726141078838174, 'recall': 0.647887323943662, 'f1': 0.6079295154185022, 'number': 1065} | 0.5092 | 0.5278 | 0.5184 | 0.6932 |
| 0.8834 | 4.0 | 40 | 0.8188 | {'precision': 0.574052812858783, 'recall': 0.6180469715698393, 'f1': 0.5952380952380952, 'number': 809} | {'precision': 0.12, 'recall': 0.05042016806722689, 'f1': 0.07100591715976332, 'number': 119} | {'precision': 0.6459369817578773, 'recall': 0.7314553990610329, 'f1': 0.6860413914575078, 'number': 1065} | 0.6041 | 0.6448 | 0.6238 | 0.7497 |
| 0.7042 | 5.0 | 50 | 0.7333 | {'precision': 0.628385698808234, 'recall': 0.7169344870210136, 'f1': 0.6697459584295612, 'number': 809} | {'precision': 0.29411764705882354, 'recall': 0.16806722689075632, 'f1': 0.21390374331550802, 'number': 119} | {'precision': 0.6616242038216561, 'recall': 0.780281690140845, 'f1': 0.7160706591986213, 'number': 1065} | 0.6368 | 0.7180 | 0.6750 | 0.7748 |
| 0.6134 | 6.0 | 60 | 0.7075 | {'precision': 0.6507276507276507, 'recall': 0.7737948084054388, 'f1': 0.7069452286843592, 'number': 809} | {'precision': 0.2987012987012987, 'recall': 0.19327731092436976, 'f1': 0.23469387755102045, 'number': 119} | {'precision': 0.7140366172624237, 'recall': 0.7690140845070422, 'f1': 0.7405063291139241, 'number': 1065} | 0.6715 | 0.7366 | 0.7026 | 0.7789 |
| 0.5519 | 7.0 | 70 | 0.6817 | {'precision': 0.6593521421107628, 'recall': 0.7799752781211372, 'f1': 0.7146092865232163, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.24369747899159663, 'f1': 0.2815533980582524, 'number': 119} | {'precision': 0.7023608768971332, 'recall': 0.7821596244131456, 'f1': 0.7401155042203464, 'number': 1065} | 0.6695 | 0.7491 | 0.7071 | 0.7857 |
| 0.5105 | 8.0 | 80 | 0.6738 | {'precision': 0.6628630705394191, 'recall': 0.7898640296662547, 'f1': 0.7208121827411168, 'number': 809} | {'precision': 0.2912621359223301, 'recall': 0.25210084033613445, 'f1': 0.2702702702702703, 'number': 119} | {'precision': 0.709106239460371, 'recall': 0.7896713615023474, 'f1': 0.7472234562416704, 'number': 1065} | 0.6702 | 0.7577 | 0.7113 | 0.7899 |
| 0.4684 | 9.0 | 90 | 0.6721 | {'precision': 0.6656217345872518, 'recall': 0.7873918417799752, 'f1': 0.7214043035107587, 'number': 809} | {'precision': 0.3090909090909091, 'recall': 0.2857142857142857, 'f1': 0.296943231441048, 'number': 119} | {'precision': 0.703150912106136, 'recall': 0.7962441314553991, 'f1': 0.7468075737560547, 'number': 1065} | 0.6683 | 0.7622 | 0.7121 | 0.7906 |
| 0.4814 | 10.0 | 100 | 0.6712 | {'precision': 0.6719409282700421, 'recall': 0.7873918417799752, 'f1': 0.7250996015936254, 'number': 809} | {'precision': 0.3153153153153153, 'recall': 0.29411764705882354, 'f1': 0.30434782608695654, 'number': 119} | {'precision': 0.7069109075770191, 'recall': 0.7971830985915493, 'f1': 0.7493380406001765, 'number': 1065} | 0.6730 | 0.7632 | 0.7153 | 0.7909 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|