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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_3
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_3
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: 0.2107
- Precision: 0.8835
- Recall: 0.8426
- F1: 0.8626
- Accuracy: 0.9729
## 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: 5e-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 | 0.1391 | 0.78 | 0.7222 | 0.7500 | 0.9575 |
| No log | 5.13 | 200 | 0.1103 | 0.8725 | 0.8241 | 0.8476 | 0.9720 |
| No log | 7.69 | 300 | 0.1415 | 0.8922 | 0.8426 | 0.8667 | 0.9739 |
| No log | 10.26 | 400 | 0.1649 | 0.8378 | 0.8611 | 0.8493 | 0.9710 |
| 0.0838 | 12.82 | 500 | 0.1545 | 0.8713 | 0.8148 | 0.8421 | 0.9729 |
| 0.0838 | 15.38 | 600 | 0.1396 | 0.8545 | 0.8704 | 0.8624 | 0.9749 |
| 0.0838 | 17.95 | 700 | 0.1523 | 0.8942 | 0.8611 | 0.8774 | 0.9768 |
| 0.0838 | 20.51 | 800 | 0.1718 | 0.8519 | 0.8519 | 0.8519 | 0.9710 |
| 0.0838 | 23.08 | 900 | 0.2242 | 0.87 | 0.8056 | 0.8365 | 0.9700 |
| 0.0044 | 25.64 | 1000 | 0.2165 | 0.88 | 0.8148 | 0.8462 | 0.9710 |
| 0.0044 | 28.21 | 1100 | 0.2235 | 0.8866 | 0.7963 | 0.8390 | 0.9681 |
| 0.0044 | 30.77 | 1200 | 0.2174 | 0.9 | 0.8333 | 0.8654 | 0.9739 |
| 0.0044 | 33.33 | 1300 | 0.1991 | 0.8692 | 0.8611 | 0.8651 | 0.9729 |
| 0.0044 | 35.9 | 1400 | 0.1986 | 0.8762 | 0.8519 | 0.8638 | 0.9739 |
| 0.0015 | 38.46 | 1500 | 0.2061 | 0.8713 | 0.8148 | 0.8421 | 0.9700 |
| 0.0015 | 41.03 | 1600 | 0.1970 | 0.8641 | 0.8241 | 0.8436 | 0.9710 |
| 0.0015 | 43.59 | 1700 | 0.2127 | 0.8614 | 0.8056 | 0.8325 | 0.9700 |
| 0.0015 | 46.15 | 1800 | 0.2070 | 0.875 | 0.8426 | 0.8585 | 0.9729 |
| 0.0015 | 48.72 | 1900 | 0.2097 | 0.8835 | 0.8426 | 0.8626 | 0.9729 |
| 0.0008 | 51.28 | 2000 | 0.2107 | 0.8835 | 0.8426 | 0.8626 | 0.9729 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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