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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_1
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_1
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: 0.2310
- Precision: 0.82
- Recall: 0.8119
- F1: 0.8159
- Accuracy: 0.9642
## 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-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 | 0.4044 | 0.5 | 0.0198 | 0.0381 | 0.8884 |
| No log | 5.13 | 200 | 0.2363 | 0.7571 | 0.5248 | 0.6199 | 0.9328 |
| No log | 7.69 | 300 | 0.1817 | 0.7083 | 0.6733 | 0.6904 | 0.9447 |
| No log | 10.26 | 400 | 0.1606 | 0.7551 | 0.7327 | 0.7437 | 0.9523 |
| 0.2439 | 12.82 | 500 | 0.1592 | 0.79 | 0.7822 | 0.7861 | 0.9577 |
| 0.2439 | 15.38 | 600 | 0.1676 | 0.8144 | 0.7822 | 0.7980 | 0.9621 |
| 0.2439 | 17.95 | 700 | 0.1912 | 0.7980 | 0.7822 | 0.7900 | 0.9588 |
| 0.2439 | 20.51 | 800 | 0.1860 | 0.8404 | 0.7822 | 0.8103 | 0.9642 |
| 0.2439 | 23.08 | 900 | 0.1990 | 0.7767 | 0.7921 | 0.7843 | 0.9567 |
| 0.0312 | 25.64 | 1000 | 0.2126 | 0.8081 | 0.7921 | 0.8000 | 0.9610 |
| 0.0312 | 28.21 | 1100 | 0.2105 | 0.8058 | 0.8218 | 0.8137 | 0.9621 |
| 0.0312 | 30.77 | 1200 | 0.2127 | 0.8119 | 0.8119 | 0.8119 | 0.9632 |
| 0.0312 | 33.33 | 1300 | 0.2308 | 0.81 | 0.8020 | 0.8060 | 0.9621 |
| 0.0312 | 35.9 | 1400 | 0.2211 | 0.82 | 0.8119 | 0.8159 | 0.9642 |
| 0.0126 | 38.46 | 1500 | 0.2244 | 0.82 | 0.8119 | 0.8159 | 0.9642 |
| 0.0126 | 41.03 | 1600 | 0.2241 | 0.82 | 0.8119 | 0.8159 | 0.9642 |
| 0.0126 | 43.59 | 1700 | 0.2332 | 0.82 | 0.8119 | 0.8159 | 0.9642 |
| 0.0126 | 46.15 | 1800 | 0.2345 | 0.82 | 0.8119 | 0.8159 | 0.9632 |
| 0.0126 | 48.72 | 1900 | 0.2318 | 0.82 | 0.8119 | 0.8159 | 0.9642 |
| 0.0069 | 51.28 | 2000 | 0.2310 | 0.82 | 0.8119 | 0.8159 | 0.9642 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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