Downloads · 30 days
6
13% of all-time downloads
layoutlmv3-finetuned-cord_100
AzadDjan/layoutlmv3-finetuned-cord_100
layoutlmv3-finetuned-cord_100 is a token classification model from AzadDjan. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as cc-by-nc-sa-4.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
48
Public
Repo size
1.5 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin504 MB · 99%
At a glance
- Task
- Token Classification
- Library
- transformers
- License
- cc-by-nc-sa-4.0
- Model type
- layoutlmv3
- Access
- Public
- Created
- Jan 8, 2023
- Updated
- Jan 8, 2023
- SHA
- e8e3c965
Try a prompt
Datasets
Model card
From the Hugging Face model README
<!-- 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_100
This model is a fine-tuned version of microsoft/layoutlmv3-base on the cord-layoutlmv3 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2246
- Precision: 0.9415
- Recall: 0.9521
- F1: 0.9468
- Accuracy: 0.9576
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: 5
- eval_batch_size: 5
- 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 | 1.56 | 250 | 1.0265 | 0.7630 | 0.8099 | 0.7858 | 0.8086 |
| 1.4021 | 3.12 | 500 | 0.5804 | 0.8290 | 0.8638 | 0.8460 | 0.8718 |
| 1.4021 | 4.69 | 750 | 0.3937 | 0.8882 | 0.9034 | 0.8957 | 0.9126 |
| 0.4062 | 6.25 | 1000 | 0.3171 | 0.9137 | 0.9274 | 0.9205 | 0.9351 |
| 0.4062 | 7.81 | 1250 | 0.2798 | 0.9332 | 0.9409 | 0.9370 | 0.9444 |
| 0.2212 | 9.38 | 1500 | 0.2558 | 0.9277 | 0.9416 | 0.9346 | 0.9461 |
| 0.2212 | 10.94 | 1750 | 0.2479 | 0.9335 | 0.9454 | 0.9394 | 0.9516 |
| 0.1525 | 12.5 | 2000 | 0.2356 | 0.9444 | 0.9536 | 0.9490 | 0.9588 |
| 0.1525 | 14.06 | 2250 | 0.2286 | 0.9365 | 0.9491 | 0.9428 | 0.9563 |
| 0.1134 | 15.62 | 2500 | 0.2246 | 0.9415 | 0.9521 | 0.9468 | 0.9576 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- layoutlmv3
- License
- cc-by-nc-sa-4.0
- Created
- Jan 8, 2023
- Updated
- Jan 8, 2023