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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- cne-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cne_nvidia_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cne-layoutlmv3
type: cne-layoutlmv3
config: cne-dataset
split: test
args: cne-dataset
metrics:
- name: Precision
type: precision
value: 0.9950738916256158
- name: Recall
type: recall
value: 0.9950738916256158
- name: F1
type: f1
value: 0.9950738916256159
- name: Accuracy
type: accuracy
value: 0.9992716678805535
---
<!-- 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. -->
# layoutlmv3-finetuned-cne_nvidia_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cne-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0064
- Precision: 0.9951
- Recall: 0.9951
- F1: 0.9951
- Accuracy: 0.9993
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 7.81 | 250 | 0.0143 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.1596 | 15.62 | 500 | 0.0085 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.1596 | 23.44 | 750 | 0.0074 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0195 | 31.25 | 1000 | 0.0068 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0195 | 39.06 | 1250 | 0.0067 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.008 | 46.88 | 1500 | 0.0067 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.008 | 54.69 | 1750 | 0.0064 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0034 | 62.5 | 2000 | 0.0063 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0034 | 70.31 | 2250 | 0.0063 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
| 0.0023 | 78.12 | 2500 | 0.0064 | 0.9951 | 0.9951 | 0.9951 | 0.9993 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.3
- Tokenizers 0.13.3
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