Downloads · 30 days
5
1% of all-time downloads
doc-ai-information-extraction
sxandie/doc-ai-information-extraction
doc-ai-information-extraction is a token classification model from sxandie. 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
616
Public
Repo size
1 GB
Likes
1
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 2, 2023
- Updated
- Jan 24, 2023
- SHA
- c40364ba
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. -->
sougemi_model
This model is a fine-tuned version of microsoft/layoutlmv3-base on the dataset created. It achieves the following results on the evaluation set:
- Loss: 0.1812
- Precision: 0.8454
- Recall: 0.8913
- F1: 0.8677
- Accuracy: 0.9534
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: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 33.33 | 100 | 0.7803 | 0.8966 | 0.8478 | 0.8715 | 0.9663 |
| No log | 66.67 | 200 | 0.3016 | 0.8696 | 0.8696 | 0.8696 | 0.9767 |
| No log | 100.0 | 300 | 0.1623 | 0.9130 | 0.9130 | 0.9130 | 0.9819 |
| No log | 133.33 | 400 | 0.1680 | 0.8454 | 0.8913 | 0.8677 | 0.9637 |
| 0.5801 | 166.67 | 500 | 0.1812 | 0.8454 | 0.8913 | 0.8677 | 0.9534 |
| 0.5801 | 200.0 | 600 | 0.1231 | 0.8947 | 0.9239 | 0.9091 | 0.9715 |
| 0.5801 | 233.33 | 700 | 0.1363 | 0.8617 | 0.8804 | 0.8710 | 0.9663 |
| 0.5801 | 266.67 | 800 | 0.1949 | 0.8333 | 0.8696 | 0.8511 | 0.9508 |
| 0.5801 | 300.0 | 900 | 0.1749 | 0.8163 | 0.8696 | 0.8421 | 0.9534 |
| 0.0607 | 333.33 | 1000 | 0.1817 | 0.8163 | 0.8696 | 0.8421 | 0.9534 |
Framework versions
- Transformers 4.26.0.dev0
- 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 2, 2023
- Updated
- Jan 24, 2023