File size: 2,827 Bytes
6332857 5e50d7b 8ceaebc 6332857 8ceaebc 6332857 47c8538 8ceaebc 705218d 8ceaebc 705218d 8ceaebc 705218d 8ceaebc 705218d 6332857 47c8538 6332857 8ceaebc 6332857 705218d 6332857 d7c9320 15fb840 6332857 705218d 6332857 d7c9320 705218d 6332857 5e50d7b 254a705 6332857 |
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 86 87 88 89 90 91 92 93 94 95 96 97 |
---
library_name: transformers
base_model: layoutlmv3
tags:
- generated_from_trainer
datasets:
- mp-02/cord
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mp-02/cord
type: mp-02/cord
metrics:
- name: Precision
type: precision
value: 0.9672131147540983
- name: Recall
type: recall
value: 0.9776304888152444
- name: F1
type: f1
value: 0.9723939019365472
- name: Accuracy
type: accuracy
value: 0.9766697163769442
---
<!-- 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-finetuned-cord
This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/cord dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1292
- Precision: 0.9672
- Recall: 0.9776
- F1: 0.9724
- Accuracy: 0.9767
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 3.125 | 250 | 0.6018 | 0.8218 | 0.8633 | 0.8420 | 0.8577 |
| 1.0098 | 6.25 | 500 | 0.2695 | 0.9205 | 0.9495 | 0.9347 | 0.9451 |
| 1.0098 | 9.375 | 750 | 0.1813 | 0.9528 | 0.9693 | 0.9610 | 0.9639 |
| 0.1993 | 12.5 | 1000 | 0.1557 | 0.9616 | 0.9743 | 0.9679 | 0.9739 |
| 0.1993 | 15.625 | 1250 | 0.1749 | 0.9608 | 0.9743 | 0.9675 | 0.9703 |
| 0.0787 | 18.75 | 1500 | 0.1482 | 0.9616 | 0.9743 | 0.9679 | 0.9730 |
| 0.0787 | 21.875 | 1750 | 0.1288 | 0.9640 | 0.9751 | 0.9695 | 0.9762 |
| 0.0433 | 25.0 | 2000 | 0.1292 | 0.9672 | 0.9776 | 0.9724 | 0.9767 |
| 0.0433 | 28.125 | 2250 | 0.1372 | 0.9623 | 0.9735 | 0.9679 | 0.9735 |
| 0.031 | 31.25 | 2500 | 0.1408 | 0.9631 | 0.9743 | 0.9687 | 0.9730 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|