File size: 3,280 Bytes
409f1c8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LayoutLMv3_5_entities_4
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_4
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: 0.2514
- Precision: 0.8762
- Recall: 0.8519
- F1: 0.8638
- Accuracy: 0.9739
## 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: 3e-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 | 0.2241 | 0.8571 | 0.8333 | 0.8451 | 0.9691 |
| No log | 5.13 | 200 | 0.2210 | 0.8952 | 0.8704 | 0.8826 | 0.9758 |
| No log | 7.69 | 300 | 0.2300 | 0.9029 | 0.8611 | 0.8815 | 0.9758 |
| No log | 10.26 | 400 | 0.2630 | 0.8922 | 0.8426 | 0.8667 | 0.9720 |
| 0.0021 | 12.82 | 500 | 0.2692 | 0.8980 | 0.8148 | 0.8544 | 0.9710 |
| 0.0021 | 15.38 | 600 | 0.2414 | 0.9 | 0.8333 | 0.8654 | 0.9729 |
| 0.0021 | 17.95 | 700 | 0.2617 | 0.875 | 0.8426 | 0.8585 | 0.9729 |
| 0.0021 | 20.51 | 800 | 0.2558 | 0.8713 | 0.8148 | 0.8421 | 0.9720 |
| 0.0021 | 23.08 | 900 | 0.2581 | 0.8725 | 0.8241 | 0.8476 | 0.9729 |
| 0.0006 | 25.64 | 1000 | 0.2574 | 0.8679 | 0.8519 | 0.8598 | 0.9739 |
| 0.0006 | 28.21 | 1100 | 0.2806 | 0.88 | 0.8148 | 0.8462 | 0.9710 |
| 0.0006 | 30.77 | 1200 | 0.3032 | 0.8958 | 0.7963 | 0.8431 | 0.9691 |
| 0.0006 | 33.33 | 1300 | 0.2627 | 0.8889 | 0.8148 | 0.8502 | 0.9729 |
| 0.0006 | 35.9 | 1400 | 0.2661 | 0.8980 | 0.8148 | 0.8544 | 0.9720 |
| 0.0006 | 38.46 | 1500 | 0.2650 | 0.9 | 0.8333 | 0.8654 | 0.9739 |
| 0.0006 | 41.03 | 1600 | 0.2543 | 0.8835 | 0.8426 | 0.8626 | 0.9729 |
| 0.0006 | 43.59 | 1700 | 0.2593 | 0.8911 | 0.8333 | 0.8612 | 0.9739 |
| 0.0006 | 46.15 | 1800 | 0.2494 | 0.8857 | 0.8611 | 0.8732 | 0.9749 |
| 0.0006 | 48.72 | 1900 | 0.2494 | 0.8857 | 0.8611 | 0.8732 | 0.9749 |
| 0.0002 | 51.28 | 2000 | 0.2514 | 0.8762 | 0.8519 | 0.8638 | 0.9739 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|