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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Output_LayoutLMv3_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. -->
# Output_LayoutLMv3_1
This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2963
- Precision: 0.8017
- Recall: 0.8407
- F1: 0.8207
- Accuracy: 0.9724
## 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: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.27 | 100 | 0.1310 | 0.7254 | 0.7832 | 0.7532 | 0.9619 |
| No log | 4.55 | 200 | 0.1070 | 0.8333 | 0.8407 | 0.8370 | 0.9733 |
| No log | 6.82 | 300 | 0.1617 | 0.8421 | 0.8496 | 0.8458 | 0.9752 |
| No log | 9.09 | 400 | 0.1895 | 0.8145 | 0.7965 | 0.8054 | 0.9705 |
| 0.074 | 11.36 | 500 | 0.1689 | 0.8018 | 0.7876 | 0.7946 | 0.9686 |
| 0.074 | 13.64 | 600 | 0.1659 | 0.8291 | 0.8584 | 0.8435 | 0.9771 |
| 0.074 | 15.91 | 700 | 0.2043 | 0.8077 | 0.8363 | 0.8217 | 0.9705 |
| 0.074 | 18.18 | 800 | 0.2111 | 0.8151 | 0.8584 | 0.8362 | 0.9743 |
| 0.074 | 20.45 | 900 | 0.1938 | 0.8390 | 0.8761 | 0.8571 | 0.9762 |
| 0.0072 | 22.73 | 1000 | 0.1802 | 0.8319 | 0.8540 | 0.8428 | 0.98 |
| 0.0072 | 25.0 | 1100 | 0.2604 | 0.7940 | 0.8186 | 0.8061 | 0.9695 |
| 0.0072 | 27.27 | 1200 | 0.2550 | 0.8025 | 0.8451 | 0.8233 | 0.9733 |
| 0.0072 | 29.55 | 1300 | 0.2632 | 0.8077 | 0.8363 | 0.8217 | 0.9705 |
| 0.0072 | 31.82 | 1400 | 0.2865 | 0.8155 | 0.8407 | 0.8279 | 0.9714 |
| 0.0018 | 34.09 | 1500 | 0.2554 | 0.8253 | 0.8363 | 0.8308 | 0.9743 |
| 0.0018 | 36.36 | 1600 | 0.2763 | 0.8101 | 0.8496 | 0.8294 | 0.9743 |
| 0.0018 | 38.64 | 1700 | 0.2541 | 0.8197 | 0.8451 | 0.8322 | 0.9743 |
| 0.0018 | 40.91 | 1800 | 0.2785 | 0.8025 | 0.8628 | 0.8316 | 0.9724 |
| 0.0018 | 43.18 | 1900 | 0.2760 | 0.8059 | 0.8451 | 0.8251 | 0.9733 |
| 0.001 | 45.45 | 2000 | 0.2956 | 0.8155 | 0.8407 | 0.8279 | 0.9733 |
| 0.001 | 47.73 | 2100 | 0.2998 | 0.8017 | 0.8407 | 0.8207 | 0.9724 |
| 0.001 | 50.0 | 2200 | 0.3007 | 0.8017 | 0.8407 | 0.8207 | 0.9724 |
| 0.001 | 52.27 | 2300 | 0.3000 | 0.8017 | 0.8407 | 0.8207 | 0.9724 |
| 0.001 | 54.55 | 2400 | 0.3031 | 0.8017 | 0.8407 | 0.8207 | 0.9724 |
| 0.0002 | 56.82 | 2500 | 0.3174 | 0.7950 | 0.8407 | 0.8172 | 0.9714 |
| 0.0002 | 59.09 | 2600 | 0.3061 | 0.7950 | 0.8407 | 0.8172 | 0.9714 |
| 0.0002 | 61.36 | 2700 | 0.3059 | 0.7908 | 0.8363 | 0.8129 | 0.9705 |
| 0.0002 | 63.64 | 2800 | 0.2988 | 0.8017 | 0.8407 | 0.8207 | 0.9724 |
| 0.0002 | 65.91 | 2900 | 0.2972 | 0.8017 | 0.8407 | 0.8207 | 0.9724 |
| 0.0001 | 68.18 | 3000 | 0.2963 | 0.8017 | 0.8407 | 0.8207 | 0.9724 |
### Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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