test / README.md
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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: funsd-layoutlmv3
type: funsd-layoutlmv3
config: funsd
split: test
args: funsd
metrics:
- name: Precision
type: precision
value: 0.8889970788704966
- name: Recall
type: recall
value: 0.907103825136612
- name: F1
type: f1
value: 0.8979591836734693
- name: Accuracy
type: accuracy
value: 0.8665161060263877
---
<!-- 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. -->
# test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5474
- Precision: 0.8890
- Recall: 0.9071
- F1: 0.8980
- Accuracy: 0.8665
## 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: 1e-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
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.33 | 100 | 0.5976 | 0.7412 | 0.8296 | 0.7829 | 0.8001 |
| No log | 2.67 | 200 | 0.5019 | 0.8259 | 0.8698 | 0.8473 | 0.8269 |
| No log | 4.0 | 300 | 0.4829 | 0.8701 | 0.8982 | 0.8839 | 0.8540 |
| No log | 5.33 | 400 | 0.4490 | 0.8829 | 0.9141 | 0.8982 | 0.8725 |
| 0.5303 | 6.67 | 500 | 0.5120 | 0.8721 | 0.9046 | 0.8881 | 0.8574 |
| 0.5303 | 8.0 | 600 | 0.5212 | 0.8802 | 0.9011 | 0.8905 | 0.8644 |
| 0.5303 | 9.33 | 700 | 0.5447 | 0.8918 | 0.9086 | 0.9001 | 0.8559 |
| 0.5303 | 10.67 | 800 | 0.5304 | 0.8875 | 0.9056 | 0.8965 | 0.8713 |
| 0.5303 | 12.0 | 900 | 0.5496 | 0.8878 | 0.9081 | 0.8978 | 0.8630 |
| 0.1291 | 13.33 | 1000 | 0.5474 | 0.8890 | 0.9071 | 0.8980 | 0.8665 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2