Downloads · 30 days
8
12% of all-time downloads
layoutlmv3-finetuned-cord_100
Chemsseddine/layoutlmv3-finetuned-cord_100
layoutlmv3-finetuned-cord_100 is a token classification model from Chemsseddine. 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
66
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
- Oct 31, 2022
- Updated
- Oct 31, 2022
- SHA
- 7d0fdb3d
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: 0.2213
- Precision: 0.9329
- Recall: 0.9469
- F1: 0.9398
- Accuracy: 0.9516
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 | 1.0664 | 0.6765 | 0.7530 | 0.7127 | 0.7818 |
| 1.4379 | 3.12 | 500 | 0.6115 | 0.8199 | 0.8518 | 0.8355 | 0.8646 |
| 1.4379 | 4.69 | 750 | 0.4192 | 0.8794 | 0.9004 | 0.8898 | 0.9028 |
| 0.4232 | 6.25 | 1000 | 0.3239 | 0.9180 | 0.9296 | 0.9238 | 0.9304 |
| 0.4232 | 7.81 | 1250 | 0.2840 | 0.9197 | 0.9341 | 0.9268 | 0.9389 |
| 0.2273 | 9.38 | 1500 | 0.2562 | 0.9217 | 0.9341 | 0.9279 | 0.9376 |
| 0.2273 | 10.94 | 1750 | 0.2574 | 0.9304 | 0.9401 | 0.9352 | 0.9410 |
| 0.157 | 12.5 | 2000 | 0.2327 | 0.9293 | 0.9439 | 0.9365 | 0.9482 |
| 0.157 | 14.06 | 2250 | 0.2217 | 0.9351 | 0.9491 | 0.9421 | 0.9520 |
| 0.1208 | 15.62 | 2500 | 0.2213 | 0.9329 | 0.9469 | 0.9398 | 0.9516 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
- Task
- Token Classification
- Library
- transformers
- Type
- layoutlmv3
- License
- cc-by-nc-sa-4.0
- Created
- Oct 31, 2022
- Updated
- Oct 31, 2022