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---
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: 1.0638
- Answer: {'precision': 0.24285714285714285, 'recall': 0.21013597033374537, 'f1': 0.22531477799867464, 'number': 809}
- Header: {'precision': 0.08695652173913043, 'recall': 0.01680672268907563, 'f1': 0.028169014084507043, 'number': 119}
- Question: {'precision': 0.5682362330407024, 'recall': 0.6685446009389672, 'f1': 0.6143226919758413, 'number': 1065}
- Overall Precision: 0.4474
- Overall Recall: 0.4436
- Overall F1: 0.4455
- Overall Accuracy: 0.6424
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.4448 | 1.0 | 75 | 1.0638 | {'precision': 0.24285714285714285, 'recall': 0.21013597033374537, 'f1': 0.22531477799867464, 'number': 809} | {'precision': 0.08695652173913043, 'recall': 0.01680672268907563, 'f1': 0.028169014084507043, 'number': 119} | {'precision': 0.5682362330407024, 'recall': 0.6685446009389672, 'f1': 0.6143226919758413, 'number': 1065} | 0.4474 | 0.4436 | 0.4455 | 0.6424 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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