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license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLMv3_5_entities_7
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. -->
# LayoutLMv3_5_entities_7
This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2592
- Precision: 0.8130
- Recall: 0.8850
- F1: 0.8475
- Accuracy: 0.9690
## 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: 6e-06
- 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: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.56 | 100 | 0.1332 | 0.7154 | 0.8230 | 0.7654 | 0.9566 |
| No log | 5.13 | 200 | 0.1432 | 0.7698 | 0.8584 | 0.8117 | 0.9646 |
| No log | 7.69 | 300 | 0.1612 | 0.7805 | 0.8496 | 0.8136 | 0.9619 |
| No log | 10.26 | 400 | 0.1885 | 0.8333 | 0.8407 | 0.8370 | 0.9655 |
| 0.0796 | 12.82 | 500 | 0.2244 | 0.7724 | 0.8407 | 0.8051 | 0.9611 |
| 0.0796 | 15.38 | 600 | 0.2407 | 0.8017 | 0.8584 | 0.8291 | 0.9655 |
| 0.0796 | 17.95 | 700 | 0.2231 | 0.8167 | 0.8673 | 0.8412 | 0.9699 |
| 0.0796 | 20.51 | 800 | 0.2435 | 0.7967 | 0.8673 | 0.8305 | 0.9655 |
| 0.0796 | 23.08 | 900 | 0.2429 | 0.8167 | 0.8673 | 0.8412 | 0.9690 |
| 0.0043 | 25.64 | 1000 | 0.2304 | 0.8684 | 0.8761 | 0.8722 | 0.9735 |
| 0.0043 | 28.21 | 1100 | 0.2704 | 0.7823 | 0.8584 | 0.8186 | 0.9655 |
| 0.0043 | 30.77 | 1200 | 0.2647 | 0.8033 | 0.8673 | 0.8340 | 0.9673 |
| 0.0043 | 33.33 | 1300 | 0.2509 | 0.8115 | 0.8761 | 0.8426 | 0.9681 |
| 0.0043 | 35.9 | 1400 | 0.2561 | 0.7967 | 0.8673 | 0.8305 | 0.9664 |
| 0.0014 | 38.46 | 1500 | 0.2774 | 0.7823 | 0.8584 | 0.8186 | 0.9664 |
| 0.0014 | 41.03 | 1600 | 0.2580 | 0.7951 | 0.8584 | 0.8255 | 0.9673 |
| 0.0014 | 43.59 | 1700 | 0.2688 | 0.7937 | 0.8850 | 0.8368 | 0.9673 |
| 0.0014 | 46.15 | 1800 | 0.2706 | 0.8 | 0.8850 | 0.8403 | 0.9681 |
| 0.0014 | 48.72 | 1900 | 0.2608 | 0.8130 | 0.8850 | 0.8475 | 0.9690 |
| 0.0008 | 51.28 | 2000 | 0.2592 | 0.8130 | 0.8850 | 0.8475 | 0.9690 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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