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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv2-cord-ner
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. -->
# layoutlmv2-cord-ner
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0952
- Precision: 0.9639
- Recall: 0.9741
- F1: 0.9690
- Accuracy: 0.9911
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 113 | 0.5962 | 0.8714 | 0.8973 | 0.8842 | 0.9405 |
| No log | 2.0 | 226 | 0.4064 | 0.8713 | 0.9098 | 0.8901 | 0.9511 |
| No log | 3.0 | 339 | 0.2687 | 0.9314 | 0.9386 | 0.9350 | 0.9754 |
| No log | 4.0 | 452 | 0.2007 | 0.9355 | 0.9472 | 0.9413 | 0.9792 |
| 0.4677 | 5.0 | 565 | 0.1625 | 0.9497 | 0.9597 | 0.9547 | 0.9834 |
| 0.4677 | 6.0 | 678 | 0.1326 | 0.9526 | 0.9645 | 0.9585 | 0.9868 |
| 0.4677 | 7.0 | 791 | 0.1212 | 0.9508 | 0.9645 | 0.9576 | 0.9851 |
| 0.4677 | 8.0 | 904 | 0.1019 | 0.9675 | 0.9712 | 0.9693 | 0.9911 |
| 0.1131 | 9.0 | 1017 | 0.1029 | 0.9545 | 0.9664 | 0.9604 | 0.9881 |
| 0.1131 | 10.0 | 1130 | 0.0952 | 0.9639 | 0.9741 | 0.9690 | 0.9911 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
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