---
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7407
- Education: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
- Email: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Github: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0}
- Location: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
- Name: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Name : {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2}
- Phone Number: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
- Soft Skills: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0}
- Technical Skills: {'precision': 0.2, 'recall': 0.35714285714285715, 'f1': 0.25641025641025644, 'number': 14}
- Overall Precision: 0.1176
- Overall Recall: 0.2
- Overall F1: 0.1481
- Overall Accuracy: 0.1475

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Education                                                 | Email                                                     | Github                                                    | Linkedin                                                  | Location                                                  | Name                                                      | Name                                                                                      | Phone Number                                              | Soft Skills                                               | Technical Skills                                                                                           | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 2.9387        | 1.0   | 2    | 2.8701          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.14285714285714285, 'recall': 0.5, 'f1': 0.22222222222222224, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 14}                                                 | 0.0185            | 0.0333         | 0.0238     | 0.0328           |
| 2.6716        | 2.0   | 4    | 2.7798          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2}| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.10526315789473684, 'recall': 0.14285714285714285, 'f1': 0.12121212121212122, 'number': 14}| 0.0612                                                                                                     | 0.1               | 0.0759         | 0.1311     |
| 2.5524        | 3.0   | 6    | 2.7407          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.2, 'recall': 0.5, 'f1': 0.28571428571428575, 'number': 2}| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}                                 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 0} | {'precision': 0.2, 'recall': 0.35714285714285715, 'f1': 0.25641025641025644, 'number': 14}| 0.1176                                                                                                     | 0.2               | 0.1481         | 0.1475     |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1