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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: MRR-NER-08-09-Layoutlmv3
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. -->
# MRR-NER-08-09-Layoutlmv3
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0175
- Precision: 0.8367
- Recall: 0.9111
- F1: 0.8723
- Accuracy: 0.9960
## 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 | 8.33 | 100 | 0.2585 | 0.1667 | 0.0222 | 0.0392 | 0.9607 |
| No log | 16.67 | 200 | 0.1281 | 0.4783 | 0.2444 | 0.3235 | 0.9727 |
| No log | 25.0 | 300 | 0.0821 | 0.3696 | 0.3778 | 0.3736 | 0.9767 |
| No log | 33.33 | 400 | 0.0493 | 0.5111 | 0.5111 | 0.5111 | 0.9813 |
| 0.2244 | 41.67 | 500 | 0.0330 | 0.625 | 0.7778 | 0.6931 | 0.9913 |
| 0.2244 | 50.0 | 600 | 0.0272 | 0.6909 | 0.8444 | 0.7600 | 0.9927 |
| 0.2244 | 58.33 | 700 | 0.0218 | 0.7843 | 0.8889 | 0.8333 | 0.9953 |
| 0.2244 | 66.67 | 800 | 0.0190 | 0.7547 | 0.8889 | 0.8163 | 0.9947 |
| 0.2244 | 75.0 | 900 | 0.0158 | 0.8936 | 0.9333 | 0.9130 | 0.9973 |
| 0.038 | 83.33 | 1000 | 0.0175 | 0.8367 | 0.9111 | 0.8723 | 0.9960 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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